Road traffic accidents are associated with different factors, such as non-signalized road networks, inexperienced drivers, lack of communication skills, distraction, and visual or cognitive impairment of road users, which have led to this increase in traffic accidents. Understanding these factors in alignment with road user communication skills can provide solutions that can reduce traffic fatalities. Road users’ communication is predominantly formal in nature, but informal means of communication also exist but are not given proper attention during the driving training and licence testing processes. Therefore, this study investigated communication skills between drivers as a factor contributing to traffic accidents in South African cities. Consequently, 16 traffic scenarios of communication between drivers were simulated using PTV Vissim and Blender software, and a semistructured interview questionnaire was used to collect data from South African road users. The questionnaire was further used to evaluate the confidence level of road users concerning informal communication. The data were analysed via regression analysis to establish the relationship between traffic accidents and miscommunication between drivers. The information gathered and analysed from this study helps to understand the informal communication types between drivers and establish where communication loss exists, which ultimately leads to a traffic accident. The results also show that misinterpretation of communication signals can present the possibility of traffic safety risk.

The significance of Linked Open Data datasets for traffic information goes beyond just including open traffic data. It extends to incorporating links to other relevant thematic datasets available on the web. This enables federated queries across different data platforms from various countries and sectors, such as transport, geospatial, environmental, weather, and more. Businesses, researchers, national operators, administrators, and citizens at large can benefit from having dynamic traffic open data connected to heterogeneous datasets across different Member States. This paper focuses on the development of a semantic model that enhances the basic service of accessing open traffic data through a LOD-enhanced Traffic Information System in alignment with the ITS Directive (2010/40/EU). The objective is not limited to just viewing or downloading data but also to improve the extraction of meaningful information and enable other types of services that are only achievable through LOD. By structuring the information using the RDF format meant for machines and employing SPARQL for querying, LOD allows for comprehensive and unified access to all datasets. Considering that the European standard DATEX II is widely used in many priority areas and services mentioned in the ITS Directive, LOD DATEX II was developed as a complementary approach to DATEX II XML. This facilitates the accessibility and comprehensibility of European traffic data and services. As part of this development, an ontological model called dtx_srti, based on the DATEX II Ontology, was created to support these efforts.

Background: By 2025, road traffic injuries are projected to rank third in the global burden of disease, posing a significant challenge that affects health, social well-being, and economic aspects. According to data from the Romanian Police National Statistics Center, there has been an average of 342 traffic accidents per year involving pediatric patients over the past 10 years. Materials and Methods: A retrospective research study was conducted, encompassing 358 cases of road traffic accidents identified for the study. Data collection was conducted at "St. Mary’s" Emergency Hospital for Children in Iasi, Romania, from January 2015 to December 2022. To facilitate statistical analyses, participants were categorized into three specific age groups, correlating between the anatomical region affected and different types of accidents was examined, and the classification of injury types was determined based on the characteristics of the harm inflicted. Results: An oscillating trend is observed from 2015 to 2020, with its lowest value recorded in 2017 at around 6.8% and its peak in 2019 at 20.1%. Notably, post-pandemic (Covid-19), the cases underwent a substantial decline of approximately 50%. At least 78.7% of those who did not undergo orthopedic reduction required surgery, whereas among those who underwent orthopedic reduction, only 23.4% needed surgery. Regarding the frequency of complications 7,9% of the total cases experienced complications. Conclusion: The increased frequency of pediatric traffic accidents is attributed to the extensive services provided by "St. Mary’s" Emergency Hospital for Children, Iasi, to a significant number of patients from both rural and urban areas across the seven counties of the Moldova, Romania.

This paper deals with the possibility for construction of the database on friction coefficient on the actual roads from the viewpoint of traffic safety. A measurement algorithm is determined in order to construct road friction measuring device. Next, the tires used for measurement are selected and the characteristics are measured using bench tire characteristic tester, and the measuring device is designed and constructed based on the characteristics. Finally, using this device, the measurement results of the road friction characteristics for two types of road surfaces are shown.

Road collisions are among the world’s critical issues causing many casualties, deaths, and economic losses. To deal with this scenario, precise analysis is required. In this paper, we use Machine Learning (ML) techniques to assess and predict road traffic collisions. An ensemble machine learning model with two layers was developed to analyze simulation data from the driving simulator. The first (base) layer integrates supervised learning techniques namely, k- Nearest Neighbors (k-NN) AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method using logistic regression as a meta-classifier. The data for this study were imbalanced; therefore, we performed a SMOTE resampling strategy to handle the dataset imbalance. To simplify the model, the PSO algorithm was used to select the most important features in our dataset. The two-layer ensemble model proposed had the best outcomes with an accuracy of 88%, F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost.

This research article aims to examine the factors that influence behavioral intentions using the traffic accident claim system in Indonesia. This research uses the Theory of Planned Behavior, the Model of Technology Acceptance, and the Integrated Theory of Technology Acceptance and Use, as well as using the Structural Equation Modeling-Partial Least Squares. The questionnaire was distributed to 580 respondents from three institutions – police, hospitals, and state-owned insurance companies – using quota sampling techniques. The results showed that TPB, TAM, and UTAUT had a significant influence in encouraging behavioral intentions using the traffic accident claim system. Further, the study found that 21st-century digital skills have a moderate role in the impact of acceptance models, planned behavior, and technology use on behavioral intent. Although the study was conducted in Indonesia and is limited to three institutions, it provides strategic guidance for policymakers to improve the use of traffic accident claim systems and reduce Road Traffic Accidents. Further research is recommended to explore from different angles and focus on service providers/operators of traffic accident claim systems to refine the findings of this study. This study contributes to the literature by investigating the factors that influence behavioral intentions using traffic accident claim systems and has a high degree of accuracy of answers from the point of view of traffic safety officers.

The way Norway is spearheading electrification in the transport sector is of global interest, and details of its policies and emission impacts represent a reference. We used the NERVE model, a bottom-up high-resolution traffic emission model, to calculate all exhaust emissions in Norway (2009-2020). This allows us to evaluate the co-benefit of policies to target climate change mitigation and air quality. We have analyzed local municipal data with regards to traffic growth, road network influences, vehicle composition, emissions and energy consumption. Light vehicle CO2 emissions per kilometre have been reduced by 22% since 2009, mainly driven by an increaseing bio-fuel mixing and BEV share. BEVs are mostly located in and around the main cities, areas with young vehicle fleets, and strong local incentives. Beneficiaries of all BEVs incentives have been a subset of the population with strong economic indicators. The incentivized growth in the share of diesel-fuelled passenger vehicle has been turned, and together with Euro6 emission standards, light vehicle NOx emissions have been halved since peaking in 2014. BEV represent an investment in emission reductions in years to come and current sales set up for an accelerated decline in emissions despite growth in traffic.

In the article a statistical approach to the assessment of the emission rates discharged by the road traffic in a spatial context is proposed. It exploits an indicator, the Yearly Average Vehicle, representing the pollutant emission rate of the average vehicle belonging to a specific category, and considers the statistical variability of most of the involved traffic parameters: vehicle speed and mileage travelled in the considered time period. Finally, indicators, assessing both the most probable value among the possible emission rates and the extent of their variability range, are proposed. They may also be used to underpin decision making-processes, when the effects of different policies addressing air pollution issues, are to be evaluated. Therefore, they are suitable for the analysis supporting urban planning activities, with a view to addressing and mitigating the effects and the consequences of pollution due to the transportation sector of the urban context.

Road traffic transportation has flourished in the process of urbanization by its advantages, but it also produces harmful environmental vibrations. The vibration problem is especially significant for production workshops with precision instruments. A field measurement was conducted to analyze the source characteristics and propagation laws of traffic-induced vibrations. Off the northern side of an urban highway lies a microelectronics workshop. When a large truck passes by, both the vibration responses on the ground and in the workshop considerably increase. Greater vibration is produced in the vertical direction than in the X and Y axes. The predominant frequency band of the vibration response caused by road traffic vehicles is lower than 20 Hz. The vertical vibration responses of the first and second floors in the workshop greatly exceed the VC-C limit because of the high daily traffic flow. Attribute to the excitation of the operation of the manufacturing equipment on the second floor, the vertical vibration level of the second floor is essentially higher than that of the first floor. Therefore, it is impossible to disregard the micro-vibration in the workplace produced by the manufacturing machinery as well as the ex-citement of the road traffic. The measured data can be used to test potential numerical models for forecasting vibrations caused by traffic on roads as well as to guide vibration assessment work throughout the planning and design stages of roads.

Traffic accidents are among the most common causes of death worldwide. According to statistics from the World Health Organization (WHO), 50 million people are involved in traffic accidents every year. Canada, particularly Montreal, is not immune to this problem. Data from the Société de l’Assurance Automobile du Québec (SAAQ) shows that there were 392 deaths on Québec roads in 2022, 38 of them related to the city of Montreal. This value represents an increase of 29.3% for the city of Montreal compared to the average for the years 2017 to 2021. In this context, it is important to take concrete measures to improve traffic safety in the city of Montreal. In this article, we present a web-based solution based on machine learning that predicts the severity of traffic accidents in the city of Montreal. This solution uses dataset of traffic accidents that occurred in Montreal between 2012 and 2021. Classification algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Random Forest (RF) and Gradient Boosting (GB) were used to develop the prediction model. When evaluating the prediction model, performance metrics such as precision, recall, F1 score, and accuracy are taken into account. The performance analysis shows an excellent accuracy of 96% for the prediction model based on the XGBoost classifier. The other models (CatBoost, RF, GB) achieved 95%, 93% and 89% accuracy, respectively. The prediction model based on the XGBoost classifier was deployed using a client-server web application managed by Swagger-UI and the Flask Python framework.

Studying and understanding the behavior of people and vehicles on public roads can be of utmost importance for supporting the activities of many institutional stakeholders. It may allow automated supervision of the ongoing situation in a given place, with warnings or alarms raised in case of anomalies. It may be used to plan their interventions on road and town organization. It may provide them with advanced support to decision-making. The number of involved entities and places to manage makes it infeasible to manually handle all the traffic-related tasks. Moreover, the complexity of the tasks to be carried out requires the adoption of advanced approaches. Many AI solutions are nowadays mature to support these requirements. In some cases, the motivations and objectives of traffic management require the AI outcomes to be understandable, interpretable and explainable. In this paper, we propose TrAnSIT (TRaffic ANalysis Supervision and Interpretation Tool), an AI-based framework that combines several modules, each aimed at tackling a specific traffic-related task, so as to cover a wide landscape of traffic-related issues, from overall urban or suburban traffic management to surveying specific road segments that fall under the scope of one camera. Most of these modules are based on AI techniques that support a human-level understanding of the outcomes.

Despite various interventions in road safety work, fatal and severe road traffic accidents (RTAs) remain a significant challenge leading to human suffering and economic costs. Understanding the multicausal nature of RTAs, where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in RTAs, focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns (BPs), using the Austrian RTA database. The study proposes Fisher’s exact test to estimate the relationship between an accident-related variable and fatal and severe RTAs (severe casualties). In terms of pattern recognition, the study develops the maximum combination value (MCV) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX-method, created to accurately detect and analyse variable-specific BPs in RTA data. Findings indicate significant BPs contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX-method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity.

Communication pathways are an underestimated source of synthetic particles in the environment. This study investigated the impact of traffic volume on microplastics (MPs) and tire wear particles (TWPs) pollution in road snow. An examination was conducted in a medium-sized city situated in northeastern Poland, known for being one of the cleanest regions in the country. MPs and TWPs were found at all 53 sites, regardless of the intensity of traffic. The average concentration for all samples was 354.72 pcs/L. Statistically significant differences were found between the average values of particles concentration on low, medium, and heavy traffic roads amounted 62.32 pcs/L, 335.97 pcs/L and 792.76 pcs/L respectively. Within all three studied groups of roads, MPs and TWPs with the smallest size, ranging from 50 to 200 μm, were prevalent. In all studied groups of roads, 4 analyzed shapes of particles were found, with irregular fragments being the most abundant form (89.23%). The most frequently recorded color among the collected samples was black (99.85%), and the least frequently recorded was blue, constituting only 0.01%. This study suggests that snow cover on the roads may act like temporary storage of pollutants during winter in the temperate climate zone and after thaw can significantly increase the concentration of MPs and TWPs in surface waters. Possible measures to decrease the release of MPs and TWPs into the environment in the city may include reducing traffic volume and speed, implementing street sweeping, utilizing filtration chambers, and installing stormwater bioretention systems or settling ponds.

The purpose of the present study is to explore the linkage between Hazardous Road Locations-based crash counts and a variety of geometric characteristics, roadway characteristics, traffic flow characteristics and spatial features in the region of Sousse, Tunisia. For this purpose, collision data was collected from at 52 hazardous road sections including 1397 crash records for a 11-year monitoring period from January 1, 2004 to December 31, 2014 obtained from National Observatory for Information, Training, Documentation and Studies on Road Safety in Tunisia (NOITDRS). The matrix of Pearson correlation was used in order to avoid inclusion of both variables, which were highly correlated. Both the Random Effects Negative Binomial model and the Negative Binomial model were estimated. To evaluate the models, the Random Effect Negative Binomial model improves the goodness-of-fit compared to the Negative Binomial model. Average Daily Traffic volume, Curved alignment, Presence of public lighting, Visibility, Number of lane, Presence of vertical/horizontal sign, Presence of rural segment, Presence of drainage system, Roadway surface condition, Presence of paved shoulder and presence of major road were found as significant variables influencing accident occurrences. Overall, the current research contributes to the literature from empirical, modeling methodological standpoints since it was the first study conducted in Tunisia to use crash prediction models for hazardous road locations, and that portrays Tunisian reality. The research findings present advantageous insights on hazardous road locations in the region of Sousse, Tunisia and present useful planning tools for public authorities in Tunisia.

One of the foundations of Smart Cities is mobility. To manage the efficiency of urban mobility, continuous recording of traffic flows is essential. The road traffic parameters in the cross section of the road network can be registered with the use of various measurement technologies. This is indirectly due to the complexity of the phenomenon of vehicle traffic and the disturbances in physical quantities generated as a result of it. Traffic data carries: sound waves, vibrations, optical waves and other media. The characteristics of these physical media, properly recorded and mathematically transformed, may constitute a superposition of the position of individual vehicles moving in the traffic flow. Moreover, these waves interfere with each other depending on the observed parameters of the movement of individual vehicles at different places of the network. In this context, information obtained from various media is the overall view of traffic. This article focuses on acquiring infrared traffic data. The movement of each vehicle, regardless of its type, is inextricably linked to infrared radiation, the distribution changing as a function of the road network. Each vehicle is a source of heat and therefore also emits infrared radiation (it heats up gradually while driving, with small temperature fluctuations). Therefore, the temperature of a moving vehicle is generally different from the background (road network) temperature. This article presents a successful attempt to analyze the infrared radiation accompanying road traffic in a selected cross section of the road. In order to register infrared radiation in the traffic stream, a prototype of a measuring system consisting of a pyrometer commonly used in thermometers (cost about 10 € and less) was used. Measurements of infrared radiation of the vehicle stream obtained in this way were compared with the measurement data of handheld traffic recorders, the so-called mechanical vehicle counters. The comparison of the results from both recording methods allows for the formulation of further research hypotheses towards extended research in the field of infrared radiation of the traffic stream. In this regard, it is important to research with various disturbances in the measurements that come from other heat sources located in the road network and its immediate vicinity. This study is important from the point of view of smart cities and C-ITS systems (mainly V2I communication). The registration of data describing the traffic flow with the use of a single pyrometer, instead of an expensive IR camera, is a cheap alternative in modern city information networks. This allows you to create intelligent sensor networks using connectivity based on, e.g., mesh networks. Such activities are possible with costs for one measuring point reduced by one or two orders of magnitude compared to the costs incurred with the use of IR cameras ( € 1000, € 10,000 and more). In the current situation of the energy crisis, the search for innovative solutions with lower energy demand is very important in the Smart City policy.

This study investigates the application of Light Detection and Ranging (LiDAR) sensor technology for data collection at signalized intersections characterized by a high rate of traffic crashes. The research aims to provide valuable insights into the potential of LiDAR-based data analysis to enhance road safety and traffic management at signalized intersections. The research methodology involved the deployment of LiDAR sensors at Marlboro Pike & Brooks Dr. signalized intersection in Coral Hills, Maryland. Two LiDAR sensors installed in this intersection to collect high-resolution, three-dimensional data of the intersection area from June, 1^st to July, 7^th 2023. The data included information on vehicle trajectories, speeds, and behaviors, as well as pedestrian and cyclist movement patterns. Concurrently, historical traffic crashes recorded and traffic flow data were obtained for the same intersection.The analysis of LiDAR data involved several key aspects including LiDAR data allowed for a precise evaluation of traffic flow patterns, including congestion points, traffic volume fluctuations, and peak-hour behavior. This information provided insights into potential factors contributing to crashes. By analyzing LiDAR data, the study identified near-miss incidents, which are often precursors to actual crashes. This proactive approach could assist in identifying crash-prone areas within the intersection. The LiDAR data analysis also focused on pedestrian and cyclist movements, including jaywalking and bike lane usage. The aim was to identify areas where infrastructure improvements could enhance safety for vulnerable road users. LiDAR data was compared with historical crash data to identify specific locations within the intersection that exhibited a high frequency of crashes. This information can guide targeted safety interventions. Last but not least, the study explored opportunities to optimize traffic signal timings based on real-time traffic data from LiDAR. Adaptive signal control could help mitigate congestion and reduce the risk of crashes.The results of this study demonstrated the potential of LiDAR sensor technology in collecting detailed data for traffic analysis in signalized intersections. By combining LiDAR data with historical crash records and traffic flow data, traffic engineers and urban planners can develop evidence-based strategies to reduce the frequency and severity of crashes in high-risk areas. Ultimately, this research contributes to a comprehensive understanding of how LiDAR technology can be employed to enhance road safety and traffic management, providing valuable insights for traffic engineers, urban planners, and policymakers seeking to improve the safety and efficiency of signalized intersections with a history of high traffic crashes.

Aiming at the current optimization problem of electric vehicle charging path planning, a charging path optimization strategy for electric vehicles under the "Traffic-Price-Distribution" mode is proposed. This strategy builds an electric vehicle charging and navigation system based on road traffic network model, real-time electricity price model and distribution network model. Based on Dijkstra shortest path algorithm and Monte Carlo time-space prediction method, the goal is to minimize the charging cost of electric vehicles. Optimal charging path. The simulation results of MATLAB and MATPOWER show that the electric vehicle charging path optimization strategy can better solve the local traffic congestion problem and improve the safety and stability of the distribution network on the basic of fully considering the convenience of electric vehicle charging.

Single and multi-vehicle crashes are a significant issue that causes economic and social costs and has therefore gained attention. This study explored the factors associated with injury severity for both single- and multi-vehicle crashes using over 550,000 crash data points in Japan between 2019 and 2021. We identified the determinants of road infrastructure and traffic control while controlling for driver, vehicle, environmental, and accident characteristics by applying ordered logit and bias-reduced binomial regression models. Our findings are as follows. 1) Traffic control variables did not affect single-vehicle crashes. 2) Guardrails had a higher severity in both single-vehicle crashes and multi-vehicle crashes at intersections. 3) The impact of the centerline differed between intersections and non-intersections for multi-vehicle crashes.  These results of this study provide transportation agencies with important guidance as to the road infrastructure and transport control.

The objective of the study was to report the health-related quality of life (HRQOL) following road traffic accidents (RTAs) among children. A community-based survey using EuroQol five-dimension questionnaire (EQ-5D-5L) in Hindi was used to collected data from community. The survey included 2620 households from urban and rural areas of Ujjain, India. From these households 229 children aged 5–18 years with a history of RTA in the last 1 year were identified, with 27%, 63%, and 10% children reporting mild, moderate, and severe injury based on length of hospitalization. Motorcycles, bicycles, and pedestrians constituted most RTAs. Helmet use was low (12%). EQ-5D-5L revealed that the most severe and extreme problem was pain and discomfort, whereas the least severe problem was usual activity and self-care. The most common (65%) injuries were either abrasion or fracture and dislocation. EQ-5D-5L severity index was maximum (mean 72) for lower extremity injuries. The results of the present study highlight the ability and requirement for quality of life measurement using EQ-5D-5L among children post-RTA.

(1) Background: Pedestrian injuries (PIs) represent a significant proportion of road traffic injuries. We aimed at investigating the incidence and mortality of PIs in different age groups and Socio-demographic Index (SDI) categories between 1990 and 2017. (2) Method: Estimates of age-standardized incidence and mortality along with trends of PIs by SDI levels were obtained from the Global Burden of Disease from 1990 to 2017. We also calculated forecasts until 2040. (3) Results: Globally, PIs incidence increased by 3.31% (–9.94 to 16.56) in 2017 compared to 1990. Men have a higher incidence of PIs than women. Forecasted incidence was 132.02 (127.37 to 136.66) per 100,000 population in 2020, 101.52 (65.99 to 137.05) in 2030 and reduced further to 71.02 (10.62 to 152.65) by 2040. Globally across all SDI categories, there was a decreasing trend in mortality due to PIs with the global estimated percentage reduction of 37.12% (–45.19 to –29.04). (4) Conclusions: The results show that PIs are still a burden for all SDI categories despite some variation. Although incidence and mortality are expected to decrease globally, some SDI categories may require particular attention in addition to specific vulnerable age groups. Further studies addressing incidence and mortality patterns in vulnerable SDI categories are needed.

Road Safety Education (RSE) is widely known as a reliable determinant of future results for what concerns health and welfare, and as an undisputable factor which contributes to the social behavior of individuals and to their mid and long-term road safety outcomes. However, its development has been relatively scarce in most countries, fact which has contributed to letting matters as delicate as traffic crashes produced by road misbehaviors continue to be a prevalent problem, thus affecting the health of the community. Objective: The aim of this study was to describe the relationship between demographic and RSE-related variables and the self-reported road risky behavior of Spanish students. Methods: For this cross-sectional study, a representative sample of 4,062 (51.5 males and 48.5% females) participants attending primary (47.5%), secondary (40.7%) and high school (11.7%) was gathered through a national survey on RSE and road behaviors. Results: A set of significant associations between demographic factors, RSE variables and self-reported road behaviors was found. Furthermore, the SEM modelling allowed us to establish that age, observed misbehaviors, attitudes towards road safety and risk perception keep a direct link with the road risky behavior of children and young people. The knowledge of traffic rules was not a significant predictor of road behavior. Conclusions: The results of this study show that, together with demographic factors such as age, RSE-related variables have an effect on the road behavior of children and young people. They also suggest the need of strengthening actions to be implemented in road safety (Road Safety Education), into scholar and community levels.

This research seeks to provide local governments with a comprehensive alternative for the proper maintenance of roads, focusing on the surface and functional evaluation of pavements. It com-pares conventional methods of the International Roughness Index (IRI) and the Pavement Condi-tion Index (PCI) with novel methodologies that employ smart technologies. Road maintenance is essential for economic development, facilitating the circulation of services and resources. This re-search analyzes the efficiency of smart technologies in the maintenance of local roads in Arequipa, taking as a case study a 2 km section of the AR-780 highway in Polobaya. The International Roughness Indices (IRI) obtained through the Merlin Roughness Meter and the Roadroid applica-tion were compared, finding a minimum variation of 4.007% in the left lane and 8.5% in the right lane. Roadroid turned out to be 60 times faster than the conventional method, with a cost differ-ence of 220.11 soles. Both methods classified the road condition (PSI) as GOOD, validating the accuracy of Roadroid. In addition, the Pavement Condition Index (PCI) was evaluated with con-ventional methods and a DJI Mavic 2 Pro drone, finding a variation of 6.89%. The cost difference between the methodologies was 1,047.73 soles, and the use of the drone proved to be 10 times faster than visual inspection. Finally, it can be said that this study contributes to closing the knowledge gap regarding the use of smart technologies for better pavement management on local roads, so that the actors in charge of the roads can make decisions based on science and thus con-tribute to the well-being of the population.

The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation. A total of 171 asphalt concrete pavement sections in Taipei City with a total length of 803.49 km were analyzed and compared by IRI. The longitudinal profile of the detected road sections was measured using an inertial profiler. The statistical analysis showed that the IRI value prior to road leveling was mainly distributed between 5 and 8 m/km, while the IRI value after road leveling was mainly distributed between 3 and 4.5 m/km. This confirms that the implementation of the Road Smoothing Project has a significant effect on improving road smoothness. Moreover, based on the analysis results, it is recommended that the IRI threshold value for road rehabilitation in Taipei City be set at 4.50 m/km.

Existing studies often lack a systematic solution for Unmanned Aerial Vehicles (UAV) inspection system, which hinders their widespread application in crack detection. To enhance its practicality, this study proposes a formal and systematic framework for UAV inspection systems, specifically designed for automatic crack detection and pavement distress evaluation. The framework integrates UAV data acquisition, deep learning based crack identification, and road damage assessment in a comprehensive and orderly manner. Firstly, the flight control strategy is presented, and road crack data is collected using the DJI Mini 2 UAV imagery, establishing a high-quality UAV crack image datasets with ground truth information. Secondly, a validation and comparison study is conducted to enhance the automatic crack detection capability and provide an appropriate deployment scheme for UAV inspection systems. This study develops automatic crack detection models based on mainstream deep learning algorithms(namely, Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) in urban road scenarios. The results demonstrate that the Faster-RCNN algorithm achieves the highest accuracy and is suitable for online data collection of UAV and offline inspection at work stations. Meanwhile, the YOLO models, while slightly lower in accuracy, are the fastest algorithm and are suitable for lightweight deployment of UAV with online collection and real-time inspection. Quantitative measurement methods for road cracks are presented to assess road damage, which will enhance the application of UAV inspection systems and provide factual evidence for maintenance decisions made by road authorities.

Regarding number of vehicles, forest roads are characterized by low traffic intensity, but on the other hand great values of ground pressure between wheels of timber truck units and forest road surface occur, often with pressures values above 80 kN which additionally causes damage of the upper and lower forest road layer. There are currently several methods for assessing condition of a forest road surface which are mainly used for assessing state of public roads, but can be used in forestry as well. Assessing condition of forest road surface was done by measuring vibrations with a specially developed software for Android OS installed on a Huawei MediaPad 7 Lite. Software measured vibrations in all three axes, coordinates of device, speed of the vehicle and time. Aim of this research was to determine accuracy of collected data so that this method can be used for scientific and practical purposes. Research was carried out on the segment of a forest road during driving a vehicle equipped with a measuring device. Tests were performed in both driving direction of the forest road segment with different measuring frequencies, tyre inflation pressures and driving speeds. Values of vibrations were classified and translated on a map of forest road together with devices’ measured coordinates. Vibration values were compared with places of recorded forest road surface damages. Research results show no significant difference in vibration values between 1 Hz and 10 Hz of measurement frequencies. Based on the analysis of collected data and obtained results, it is clear that it is possible to assess the condition of a forest road surface by measuring vibrations. The greatest values of vibrations were recorded on the most damaged parts of the forest road. Vibrations do not depend on tyre inflation pressure, but ranges of vibrations are decreasing with decreasing driving speed. Accuracy of collected data depends on GPS signal quality, so it is recommended that each segment of forest road is recorded twice so that location of damages on forest road can be confirmed with certainty.

Road safety is a critical public health concern in Nepal, especially in urban areas where inadequate infrastructure exacerbates traffic incidents. This study reviews and synthesizes 21 peer-reviewed papers to investigate how road infrastructure contributes to traffic accidents. Key factors include poor road conditions, reckless driving, and inadequate traffic management. Findings indicate that road traffic accidents (RTAs) significantly increase morbidity, mortality, and economic burdens in Nepal. To address these issues, the study recommends infrastructure improvements, stricter enforcement of traffic laws, and enhanced public education on road safety.

Road congestion in Amman city increased yearly, due to the increase in private car ownership and traffic volumes. This study aims to (a) assess the social and economic impacts of the toll road in Amman, Jordan through a survey questionnaire using statistical software (SPSS), (b) identify the optimal price at selected road using micro-simulation (VISUM). Traffic, geometric and cost data of toll technique of two sections on the Airport Highway (From Ministry of Foreign Affairs to Madaba Interchange.; and From Madaba Interchange to Queen Alia International Airport (QAIA) interchange) were used for simulation purpose. The toll road (across 7 distinct scenarios at various price) were assessed for an optimal revenue. The survey questionnaire was made based on all scenarios including AM peak hour. The operation cost for toll road was determined based on greater Amman Municipality (GAM). The optimal scenario was measured based on the value ¬¬of revenue (JD), and type of payment. The results indicate that users found the most charging method to be based on traveled distance (54.02%) and the optimal value of the toll to be equal 0.25 JD (34.08%). In Addition, the total cost of Manual Toll Collection method (MTC) in 2025 is 126,935JD and the revenue is 1122.6 JD so this indicates to positive result. Eventually, the result of applying road pricing on the airport road in 2025 indicate to be effective and economically feasible only by using Manual Method.

Electric road systems (ERS) are a group of technologies that allow powering adequately-equipped road transport vehicles with electricity from the road infrastructure while in motorway traffic. They can be categorised in three technology groups: overhead catenary, ground conductive and ground inductive, depending on the used mode of power transfer. The supplied energy is used for propulsion and for charging the vehicle batteries to be used once the vehicle leaves the electrified road section. Also, another energy source, e.g. diesel, natural gas or hydrogen, can be used while away from ERS. This research investigates the potential impacts of implementing the different ERS technologies on the Rotterdam-Antwerp motorway corridor that links the two largest ports in Europe. The aim is to identify which of the routes between the ports is best suited for implementation of ERS, whether there are substantial differences in economic performance of the different ERS technologies, see what ERS vehicle traffic volumes are required and potentially available for successful implementation, what investment is required to build the system and whether the ERS operator can be profitable, and could transport operators operate their trucks on ERS profitability in this corridor setting. The research shows that that the route between Rotterdam and Antwerp that runs on motorway E19 is the best to be electrified from an economic standpoint. Our calculations show that the traffic on Rotterdam-Antwerp corridor is sufficient for economically justifying ERS infrastructure rollout and operation. For transport operators, that happen to have specific client base, e.g. where they usually serve clients from one of the ports along the electrified route, the construction of ERS on the route can prove to be very lucrative if they adopt the technology early.

This research investigates the lane width standards and guidelines implemented by various State Departments of Transportation (DOTs) to reduce vehicle speeds and enhance road safety. Lane width reduction is often perceived as a strategy to mitigate speed and improve safety. Still, its effectiveness and implications vary across different contexts, including regions, urban/rural settings, or other geometric design features. Drawing from interviews with five State DOTs and a review of their road design manuals, this study aims to identify suggested lane widths depending on the contexts, design exception process when narrowing or widening lane widths, and introduce representative before/after studies. The findings indicate that State DOTs tend to have lower recommended lane widths in urban areas than in rural areas. Moreover, lane width standards among these states vary due to several factors, including the geographical location of roadways (urban or rural areas), design or posted speeds, traffic volume, road classification, and geometric road design features. Design exceptions are required if the existing or proposed design element is incompatible with both AASHTO and department governing criteria. In conclusion, the findings will provide valuable insights and recommendations for policymakers, transportation planners, and road engineers to inform optimal lane width and decision-making processes.

Illegal removal of road barricades without the notice of road emergency officials and road users has resulted in fatalities, injuries, and property damages. It is only after an incident has occurred or someone noticed the removal and alerted the authorities for the barricade to be placed back at its intended location. There is a need for traditional barricades to be equipped with mechanisms that would alert officials on illegal road barricade removal and warn road users of a road/lane(s) closure to prevent fatalities, injuries, and property damages to the traveling public. This research has used the Global Positioning System (GPS) module to implement the detection of barricade displacement and an alerting system for emergency officials and road users. The barricade displacement was estimated from the haversine distance formula, corrected for errors, and interfaced with the displacement threshold value for the road users within a geofenced area to be alerted. The geofenced area radius was estimated to be 1.04 miles from the barricade location using AASHTO, NSC, and TransGuide ITS manuals. The non-parametric bootstrapping method estimated the GPS position error to 10.5 feet and corrected the measured distances. Data from a clear sunny day shows the best response to barricade movements compared to a cloudy day where movements can’t be explained easily. The proposed monitoring and warning systems would warn road users and alert emergency officials of the danger when the physical barricades have been removed illegally hence saving lives and reducing property damages.

Forest road pavement needs an evaluation methodology based on a comprehensive assessment of road conditions. This research was conducted to evaluate the performance of a method for rating the surface condition of forest roads and eventually to adapt the method to the situation prevailing in a forest road network. The rating method selected as the basis for this experiment was the pavement condition index (PCI) developed by the U.S. Army Corps of Engineers. A 53 km of forest roads were selected contained the most influential factors and conditions variability. Eventually, 201 road segments were delineated between 150-300 m in length. Within the given segments, sample plots were set 20 m in length consecutively. It was concluded that the panel scores for distress and surface condition of sample unit and section differed from forest road pavement condition index (FRPCI) and PCI. Linear regression was used to derive equations between distress and PCI score to determine effective FRPCI parameters that provide a numerical rating for the condition of road segments within the road network, where 0 worlds are the worst possible condition, and 100 is the best possible condition best. Also, regression analysis showed the FRPCI model with a 0.87 correlation for the total of the road is a performance index used for the first time in forest roads. This study showed a range of FRPCI from 7.8 to 96.3, different from PCI and URCI ratings (0.85-45 and 1.2-53). The FRPCI index helps forest managers in road maintenance, harvesting, and planning to use road information.

Evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, evaluation and analysis of important contributing factors affecting the number of crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA) are employed for evaluating the number of vehicles involved in the accident based on the seven factors affecting transport safety including the Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS) and Annual average daily traffic (AADT) of rural roads of Cosenza in southern Italy. In this study, 564 data sets of rural areas were investigated and relevant effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation for rural areas. The results obtained demonstrated that "Average speed" has the highest level and "Weekday" has the lowest level of importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

Globally, there are 1.35 million road fatalities every year, which are estimated to cost governments approximately US$ 518 billion, making road fatalities the 8th leading cause of death across all age groups and the leading cause of death of children and young adults. In South Africa, despite tremendous governmental efforts to curb the soaring trajectory of road accidents, the annual number of road fatalities has increased by 26% in recent years. By fitting a structural equation model (SEM) and a GARCH Model (Generalized Auto-Regressive Conditional Heteroskedastic-ity) to analyze and predict future trend of road accidents (number of road accidents, number of casualties, number of fatal crashes and number of persons killed) in South Africa, we propose and test a complex metamodel that integrates multiple causality relationships. We show an increasing trend of road accidents over time, a trend that is predictable by number of vehicles in the country, the population of the country and the total distance travelled by vehicles. We further show that death rate linked to road accidents is on average 23.14 deaths per 100,000 persons and is pre-dictable following the equation: y = -0.0114x2+1.2378x-2.2627 (R2=0.76) with y = death rate and x = year. Finally, in the next decade, the number of road accidents is predicted to be roughly constant at 617,253 accidents but can reach 1 896 667 accidents in the worst-case scenario. The number of casualties was also predicted to be roughly constant at 93 531 over time, although this number may reach 661 531 in the worst-case scenario. However, although the number of fatal crashes may decrease in the next decade, it is forecasted to reach 11 241 within the next 10 years with the worse scenario estimated at 19 034 within the same period. At the same time, the number of persons killed in fatal crashes is also predicted to be roughly constant at 14 739 but may also reach 172 784 in the worse scenario. Overall, the present study reveals perhaps the positive effects of government initiatives to curb road accidents and their consequences; we call for more stronger actions for a drastic reduction in road accident events in South Africa.

Road alleys, especially on areas of Warmia (north-east Poland), are multifunctional features in open landscapes. They serve as ecological corridors connecting habitats and play an important role in sustaining ecological stability. However, multiple road authorities claim that tree-lined routes are a threat for traffic safety and therefore should be removed. The aspect of safety seems to be crucial to them, significantly overwhelming alley benefits. The vitality problems of the trees (which are mainly mature and aging) deliver arguments to cutting them down. The aim of this paper is examination of environmental and natural value of the road alleys (based on a 14 km long-distance of the Gamerki – Jonkowo road), and verification of the degree of hazard posed by trees designated for cutting because of safety reasons. The six-examination framework for the research was developed. Tree risk assessment and vitality evaluation, pulling tests, examination of protected beetle species - hermit beetle, the lichens species, and examination of bat fauna were performed. The results revealed no trees in the resignation phase and confirmed that the alley is a unique natural and landscape habitat with protected species of lichens, a few bats species and valuable insect species, among others hermit beetle (Osmoderma barnabita) existing there. The environmental value of the alley is, therefore, hard to overestimate and cannot be perceived only as a component of the road infrastructure. The maintenance of the trees seems to be essential when taking into account environmental stability of the region.

Road markings play an important role in road safety because they provide significant information to drivers about the road. For that reason, they must be replaced when they are not correctly perceived by road users. To analyse which are the main factors that affect road marking perception over time, a test section was designed in a two-lane rural highway, running actual traffic over 18 different types of markings fabricated with different combinations of drop-on materials. Chromatic coordinates, luminance and retroreflectivity of each sample were measured during 18 months in order to study their evolution over time. The results obtained show different behaviours depending on the aggregates and application method used. An increment of the durability has been observed with the use of different layers and mixtures of glass microbeads with different sizes.

In order to apply RAP fine aggregate to High Modulus Asphalt Concrete(HMAC), this study first extracts aged asphalt from RAP fine aggregate through extraction and distillation, and adds it to high modulus modified asphalt in proportions of 20%, 40%, 60%, and 80%. The performance changes of the recycled asphalt were investigated using conventional performance tests and dynamic shear rheological tests (DSR). Microscopic techniques such as four‐component analysis and infrared spectroscopy were employed to explain macroscopic performance changes in terms of component and functional group changes in the asphalt. Using HMAC‐20 gradation as an example, uniaxial compression dynamic modulus tests, rutting tests, freeze‐thaw splitting tests, and low‐temperature bending tests were conducted to evaluate the performance changes of recycled asphalt mixture with different RAP fine aggregate contents through four indices: dynamic modulus, dynamic stability, tensile strength ratio (TSR), and maximum bending strain. The results indicate that for every 20% increase in aged asphalt, the penetration at 25°C, ductility at 25°C, and dynamic viscosity at 60°C decrease by approximately 6.2%, 15.4%, and 3.9%, respectively, while the softening point increases by approximately 13.3%. It is concluded that the addition of aged asphalt significantly affects the ductility at 25°C and the softening point. As the aged content increases, the complex shear modulus (G*) and phase angle (δ) of the recycled asphalt decrease, while the rutting factor increases. The four‐component analysis and infrared spectroscopy of the asphalt demonstrate that the incorporation of aged asphalt is merely a physical blending phenomenon, involving changes in the content of the four components without the formation of new functional groups (new peaks). When RAP fine aggregate is applied to high modulus recycled asphalt mixture, compared to high modulus modified asphalt mixture, the recycled mixtures exhibit higher dynamic modulus and dynamic stability, but significantly reduced water stability and low‐temperature performance.

The physiological impact of walking in nature was quantified via continuous heart rate variabil-ity (HRV), pre- and post-walk saliva cortisol measures, and self-reported mood and mindfulness scores for N=17 participants who walked “The Green Road” at Walter Reed National Military Medical Center in Bethesda, Maryland. For N=15 of the participants, HRV analysis revealed two main groups: group 1 individuals who had a 104% increase (mean) in root mean square standard deviation (RMSSD) and 47% increase (mean) in standard deviation of NN values (SDNN) indi-cating an overall reduction in physiological stress from walking the Green Road, and group 2 in-dividuals who had a decrease (mean) of 42% and 31% in these respective HRV metrics signaling an increase in physiological stresses. Post-walk self-reported scores for vigor and mood disturb-ance were more robust for the Green Road than for a comparable urban road corridor and showed that higher HRV during the walk was associated with improved overall mood. Saliva cortisol was lower after taking a walk for all participants and showed that walking the Green Road elicited a significantly larger reduction in cortisol of 53% on average when compared with 37% of walking along an urban road. It is also observed that the order in which individuals walked the green road, and urban road also impacted their cortisol response with those walking the urban road before the green showing a substantial reduction in cortisol, suggesting a possible attenuation effect of walking the green road first. These findings provide quantitative data demonstrating the stress-reducing effects of being in nature, thus supporting the health benefit value of providing access to nature more broadly in many settings.

The increasing number of registered road crashes involving cyclists during the last decade, and the high proportion of road crashes resulting in severe injuries and fatalities among cyclists constitutes a global issue for community health, urban development, and sustainability. Nowadays, the incidence of many risk factors for road crashes of cyclists remains largely unexplained. Given the importance of this issue, the present study has been conducted with the aim of determining relationships between infrastructural, human factors and safety outcomes of cyclists. Objectives: This study aimed, first, to examine the relationship between key infrastructural and human factors present in cycling, bicycle-user characteristics, and their self-reported experience with road crashes. And second, to determine whether a set of key infrastructural and human factors may predict their self-reported road crashes. Methods: For this cross-sectional study, a total of 1064 cyclists (38.8% women, 61.2% men; M = 32.8 years of age) from 20 different countries across Europe, South America and North America, participated in an online survey composed of four sections: demographic data and cycling-related factors, human factors, perceptions on infrastructural factors, and road crashes suffered. Results: The results of this study showed significant associations between human factors, infrastructural conditions and self-reported road crashes. Also, a logistic regression model found that self-reported road crashes of cyclists could be predicted through variables such as age, riding intensity, risky behaviors, and problematic user/infrastructure interactions. Conclusions: The results of this study suggest that self-reported road crashes of cyclists are influenced by features related to the user and their interaction with infrastructural characteristics of the road.

Recycled asphalt mixture is a material remixed with old asphalt recycled material (RAP) and new aggregate, and its application is of great significance in environmental protection.Due to the wear and tear of the old asphalt mixture, the road performance of the recycled asphalt mixture will decrease. This paper uses IPP software to obtain the shape characteristics of the old and new aggregates, and found that the roundness of the old aggregate of 9.5mm-16mm is the most serious wear. Therefore, the 30% RAP, and the influence of the roundness of the recycled asphalt mixture on the road performance is studied.

Non-exhaust road transport emissions in cities contribute to poor air quality and create health impacts. This paper presents a new study of the emitted particles due to the tyres-road wear ‘TRWP’ in real driving and gives their emission factors EF. The most frequently observed particles were <1 µm; followed by particles size between [1µm, 4µm] for urban, suburban and highway areas. These particle sizes are well-known to have a higher degree of toxicity. An overall analysis of all the measured pollutants gave EF equal to 30 10+12 #/km and 35 10+12 #/km, respectively for urban and sub-urban areas; and 5 10+12 #/km on highway. The analysis of the 19 most emitted pollutants, taken individually, showed that their EFs ranged from 0.003 to 18.142 10+12#/km. The obtained EF are above the value of the Euro standard for vehicles, but are below the values for vehicles unequipped with a particle filter. Significant test analysis confirmed that inertia of chemical pollutants is homogeneous. EF assessment of the PM10 and PM2.5 emitted is equal to 1.45 mg/km and 0.35 mg/km respectively. These results should contribute to the emergence of future regulations of non-exhaust emissions and should help to analyse the exposure-impact relationship to TRWP.

Personal Mobility Vehicle (PMV) which has an inward tilting angle turns with the lateral force due to large camber angle, so it is necessary to consider the lateral movement of the tire vertical load axis during turning. Although the steering torque mechanism are very different from those of automobiles, there are not many examples which studied the steering torque mechanism of PMV. In this paper, based on the effects of six components force acting on the tires, a method for setting the steering axis specifications is derived, including the geometrical minimization of steering moment disturbance due to the vertical load reaction force during turning. Although, automobile tires have a significant ground camber angle when traveling on rutted roads, they do not have it on slant roads because the vehicle body tilts along the road surface. On the other hand, in PMV, the vehicle body always keeps upright when traveling both on slanted roads and on rutted roads. Therefore, the tires have ground camber angles on both types of road surface. We study the straight running ability under such road surface disturbances based on the geometrical minimization of steering moment disturbance due to the vertical load reaction force during turning. This straight running ability can be a remarkable strong point of PMV with an inward tilt mechanism.

The primary objective of this experiment is to enhance road safety and mitigate the occurrence of accidents by leveraging Low Power Wide Area Networks (LPWAN), specifically focusing on the Long Range (LoRa) network. Through comprehensive research into LPWANs and their various applications, we have designed and implemented a novel approach employing two mobile robots. These robots are utilized to emulate a typical traffic scenario, with one robot leading the way, skillfully maneuvering around obstacles, while the other robot closely follows behind, tracing the same path. The leading robot is tasked with collecting data from its internal environment, the external surroundings, and its operator through an array of sensors. Subsequently, this data is transmitted to the trailing robot. The follower robot's role is to analyze the data received and formulate decisions based on this information. Its objective is to mimic safe driving behavior, particularly when the received data deviates from the expected norms. In such instances, the follower robot will opt to decelerate significantly, thereby minimizing the risk of collision. By implementing this innovative system, we aim to significantly enhance driving safety and reduce the occurrence of accidents on the road.

Abstract Several studies have been conducted to strengthen weak road layers, regardless of whether they are un-der construction or are undergoing maintenance. Using substrates filled with different materials has proven effective for increasing the strength of these layers. In this study, I evaluated the coefficient of lateral soil pressure under various conditions when using reinforced and unreinforced piles filled with in-expensive material and the extent of their contribution to strengthening road layers. The results con-firmed that there was a difference in the coefficient of lateral soil pressure when the sand-filled piles were reinforced. The sand-filled piles were divided into several parts by arming. The maximum coeffi-cient of lateral soil pressure was 0.38 in the case of sand (1 mm) with reinforcement, whereas it was 0.29 in the same case without reinforcement as shown in Figure 1. The maximum coefficient of lateral soil pressure was 0.27 in the case of granite grain (2 mm) with reinforcement, whereas it was 0.17 in the same case without reinforcement. Moreover, the maximum lateral pressure coefficient was 0.26 in the case of granite grain (3 mm) with reinforcement, and it was 0.30 in the same case without reinforce-ment.

Addressing sustainable mobility in urban areas has become a priority in today’s society, given the growing population and increasing vehicular flow in these areas. Intelligent Transportation Systems have emerged as innovative and effective technological solutions to address these challenges. Research in this area has become crucial, as it contributes not only to improve mobility in urban areas, but also to positively impact the quality of life of its inhabitants. To address this, a dynamic clustering methodology for vehicular trajectory data is proposed which can provide an accurate representation of the traffic state. Data was collected for the city of San Francisco, a dynamic clustering algorithm was applied and then an indicator was applied to identify areas with traffic congestion. Several experiments were also conducted with different parameterizations of the forgetting factor of the clustering algorithm. The results showed in terms of precision that the dynamic clustering methodology achieved high match rates compared to the congestion indicator applied to static cells.

While studies that examined risk perceptions in various environments have been previously conducted, the relationship between risk perceptions measured in such environments had not been examined. Such a relationship, if found, may indicate that when one learns to identify risks in one environment, they may identify risks more easily and quickly in another.This study examined the relationship between measures of risk perception in several separate environments. The findings suggest that when learning to identify and assess risks in a particular environment, one may better identify and assess risks in a similar environment – for example, learning to identify and assess risks while driving makes it easier to identify and assess risks when crossing a road. Conversely, the findings also indicate that learning to identify and assess risks in one environment does not make it possible to better identify and assess risks in a disparate environment – for example, learning to identify and assess risks while driving does not make it easier to identify and assess risks in a workplace environment. This may also suggest that while disparate environments such as the road environment and the workplace environment are perceived as two separate environments, the road environment is experienced as one single environment by both drivers and pedestrians.

Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to navigate a given environment. Much like a human, this ability is dependent on the robot's understanding of a given scene. For Autonomous Vehicles (AV's), detailed 3D maps created beforehand are widely used to augment the perceptive abilities and estimate pose based on current sensor measurements. This approach however is less suited for rural communities that are sparsely connected and cover large areas. Topological maps such as OpenStreetMaps have proven to be a useful alternative in these situations. However, vehicle localization using these maps is non-trivial, particularly for the global localization task, where the map spans large areas. To deal with this challenge, we propose road descriptors along with an initialization technique for localization that allows for fast global pose estimation. We test our algorithms on (real world) maps and benchmark them against other map based localization as well as SLAM algorithms. Our results show that the proposed method can narrow down the pose to within 50 centimeters of the ground truth significantly faster than the state of the art methods.

The volume of vehicular traffic in large cities has increased in recent years, the devices that collect vehicular GPS data such as cameras, GPS receivers and others generate millions of records at every instant of time generating problems in processing and storage of these data which becomes important for researchers. Intelligent Transportation Systems perform vehicle monitoring and control by collecting GPS trajectories, this large volume of information is necessary to have an optimal storage process. Its processing by means of compression techniques and simplification algorithms allow to reduce the necessary storage space. This paper presents a GPS trajectory simplification algorithm that considers noise reduction, point simplification and analysis of road network information. The results obtained on two data sets from the cities of California and Beijing are satisfactory, achieving a higher compression ratio without affecting data quality

This paper deals with the analysis of trends in road accidents on major highways in Brazil. Using updated time series techniques, our results indicate that a low degree of long memory was detected in the series with shocks having transitory effects over time. We further find that the number of accidents taking place in Brazil has been reducing over time, though in the presence of negative shocks, the recovery is not going to be immediate due to the long memory nature of the data. Despite the absence of relevant investment relating to infrastructure expansion, it is worth mentioning the consolidation of a nationwide tolled road system in Brazil involving concessions to private administrators, alongside more severe traffic laws that can impose limitations on driving licences.

This research deals with the possibility for construction of the database on the braking friction coefficient for actual roads from the viewpoint of traffic safety especially for automated driving such as level 4 or higher. In an automated driving such levels, the controller needs to control the vehicle, but the road surface condition, especially the road friction coefficient on wet roads, snowy or icy roads, changes greatly, and in some cases, changes by almost one order. Therefore, it is necessary for the controller to constantly collect environment information such as the road friction coefficients and prepare for emergencies such as obstacle avoidance. However, at present, the measurement of the road friction coefficients is not systemically performed, and a method for accurately measuring has not been established. In order to improve this situation, this study examines a method for continuously measurement for the road friction characteristics such as μ-s characteristics.

Active contour models present a robust segmentation approach which make efficient use of specific information about objects in the input data rather than processing all the data. They have been widely used in many applications including image segmentation, object boundary localisation, motion tracking, shape modelling, stereo matching and object reconstruction. In this paper, we investigate the potential of active contour models in extracting roads from Mobile Laser Scanning (MLS) data. The categorisation of active contours based on their mathematical representation and implementation are discussed in detail. We discuss an integrated version in which active contour models are combined to overcome their limitations. We review various active contour based methodologies which have been developed to extract roads and other features from LiDAR and digital imaging datasets. We present a small case study in which an integrated version of active contour models is applied to automatically extract road edges from MLS dataset. An accurate extraction of left and right edges from the tested road section validates the use of active contour models. The present study provides a valuable insight on the potential of active contours for extracting roads and other infrastructures from 3D LiDAR point cloud data.

: Nanotechnology options to road surface maintenance offers several advantages compared to traditionally used materials. The small particle sizer of hydrophobic Nano-Silane modified Nano-Polymers (NSNP) enables these nanotechnology products to deeply penetrate existing road surfaces, sealing micro-cracks and render surfacings to be water-resistant for extended periods of time. In comparison, traditionally used products contain minimum partial sizes of about 1 – 5 microns, that provide a superficial protection that wears off in a relatively short period of time. These traditional products are often associated with vehicle contamination while drying and requires the re-instatement of road markings. None of these disadvantages are associated with applicable NSNP technologies that are quick drying, with no vehicle contamination risks and is equivalent to a “clear-seal” requiring no reinstatement of road markings. In a similar vein, pot-hole repairs can be done using applicable, easy to use, pre-packed and treated pot-hole repair kits that are water-repellent and quick-drying at a fraction of the costs of conventional cold-mix products. Resurfacing using NME binder slurries can be done labour-intensively on a pre-treated NSNP surfacing, restoring cracked surfacing and providing a water-resistant long-lasting protective layer without the removal of existing cracked areas. The implementation of nanotechnology solutions for road surface maintenance operations is directly associated with ease of use, labour-intensive operations, prevention of considerable deterioration in riding quality due to removal and manual re-instatement of cracked surfaces, time and cost savings and a reduction in the risk of water damage to the sub-structure. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back

In recent years, the introduction of electric vehicles as a means of transportation has been gradually growing in most developing and developed countries, especially in terms of energy efficiency, energy diversification, and environmental sustainability. While developed countries have been trying to keep up with electric vehicle usage and buying trends, most developing countries, especially African countries, are still riddled with severe power outages and electricity load shedding. The growing adoption of electric vehicles (EVs) has led to a corresponding increase in the availability of charging infrastructure, thereby exerting notable impacts on the power grids of most African countries. Recent evidence suggests that various charging strategies and grid integration approaches are being developed to mitigate the negative impacts of electric vehicle (EV) charging and enhance the advantages of integrating EVs with the grid. However, transportation researchers have struggled to develop applicable and sensible mitigation strategies to tackle this issue, which has seriously rendered the sustainability of electric vehicle usage in Africa in serious doubt. The primary aim of this research is to provide a conceptual theoretical framework based on previous and current literature reviews on electric vehicles to examine the challenges facing the adoption of electric vehicles in Africa. In addition, we explored the environmental and economic benefits of electric vehicles in Africa regarding urban sustainability and charging infrastructure on the existing transportation infrastructure in the African transportation network. This study contributes to the expanding body of literature on electric vehicles by emphasising the significance of incorporating sustainability principles in the adoption and usage of electric vehicles in Africa. It also examines the effects of electric vehicle usage on the existing transportation infrastructure.

Bicycle transport stands at the forefront of sustainable mobility initiatives, a cornerstone of European Union policy. While support for cycling is crucial, its advancement hinges upon the presence of high-quality, secure infrastructure for cyclists. Safety emerges as a paramount concern, influencing the viability of cycling as a mode of transportation, yet its perception varies among user demographics. Considering this, this article endeavours to examine and contrast cycling infrastructure across various European nations, alongside analysing statistics pertaining to fatal traffic accidents involving cyclists. In our paper, we have chosen the Netherlands as a representative of building quality cycling infrastructure with many years of experience, and two countries that are developing conditions for cyclists, Slovakia and Poland. We have applied a correlation between the length of the transport infrastructure and the number of cyclists killed. The findings point to the substantial correlation between the quality of cycling infrastructure and the incidence of accidents. This correlation underscores the pressing need for robust infrastructure development and safety measures to bolster cycling as a safe and viable transportation option. We have employed the Shapiro-Wilk test for normal distribution to verify the input data and process the Pearson correlation to investigate the correlation between cycling infrastructure and road accident fatalities of cyclists.

Bicycle transport stands at the forefront of sustainable mobility initiatives, a cornerstone of European Union policy. While support for cycling is crucial, its advancement hinges upon the presence of high-quality, secure infrastructure for cyclists. Safety emerges as a paramount concern, influencing the viability of cycling as a mode of transportation, yet its perception varies among user demographics. In light of this, this article endeavors to examine and contrast cycling infrastructure across various European nations, alongside analyzing statistics pertaining to fatal traffic accidents involving cyclists. The findings underscore a substantial correlation between the quality of cycling infrastructure and the incidence of accidents. This correlation underscores the pressing need for robust infrastructure development and safety measures to bolster cycling as a safe and viable transportation option. By understanding the intricate interplay between infrastructure quality and safety outcomes, policymakers can better prioritize resources and enact measures to foster safer cycling environments, thus furthering the objectives of sustainable mobility on a continental scale.

The process involved in preparing driving simulator tests is a labor intensive one, and the nature of this complexity depends on the sophistication of the tests to be carried out, as well as the number of participants. Which has an impact on the duration of the research itself. The very solution of using driving simulators is a research tool that has been in use for many years and with great success. They are used in a very wide range of areas of human functioning. They replicate in a very realistic way the conditions that can occur in the external environment, offer the possibility of repetition of particular conditions and insight into certain behaviors that could be very difficult to initiate in real life. This article also describes the dis- ease of using simulators to conduct research activities, along with the workflow from assumptions to finalizing the research. An algorithm is also presented for the study of individual participants, with examples for separate research scenarios concerning driver behavior in the vicinity of pedestrian crossings.

In order to achieve the elimination of the negative impacts of transport on road safety, the European Union is taking various measures resulting from its commitment to improve road safety. One of the instruments for achieving the objectives set is social legislation for the work of drivers in road freight and passenger transport. In line with the objective of improving the quality of working conditions for road transport drivers and road safety, the European Union applies Regulation (EC) No 561/2006 on the harmonisation of certain social legislation relating to road transport, which lays down rules on driving times, breaks and rest periods for drivers operating in road freight and passenger transport. The main objective of the paper is to assess the impact of social legislation on road transport safety by means of real research and to identify the factors influencing the violation of the legislation depending on the type of transports carried out in domestic or international road freight transport.

Vehicular traffic in urban areas continues to be a constant challenge today, due to the continuous population growth and the increase of vehicles in circulation. Vehicular congestion, as a recurrent problem, generates a negative impact on urban mobility and citizens’ quality of life. It is hypothesized that a dynamic clustering method of vehicle trajectory data can provide an accurate and up-to-date representation of real-time traffic behavior. To evaluate this hypothesis, data were collected from three different cities: San Francisco, Rome and Guayaquil. A dynamic clustering algorithm was applied to identify traffic congestion patterns and an indicator was applied to identify and evaluate the congestion conditions of the areas. The results show a high precision and recall in congestion classification compared to a method based on static cells.

Road pavement monitoring represents the baseline to the pavement maintenance process. To quickly fix a damaged road, relevant authorities need a high-efficiency methodology that allows them to obtain data describing the current conditions of a network of roads. In urban areas, large-scale monitoring campaigns may be more expensive and not fast enough to describe how pavement degradation has evolved over time. Furthermore, at low speeds, many technologies are inadequate for monitoring the streets. In such a context, employing black box equipped vehicles to perform a routine inspection could be an excellent starting point. However, the vibration-based methodologies used to detect road anomalies are strongly affected by the speed of these vehicles. In this study, a statistical method was to analyze the effects of speed on road pavement conditions at different severity levels through the data recorded by taxi vehicles. Likewise introduced was a process that overcomes the speed effect when considering road surface conditions. The methodology proposed has succeeded in predicting the right damage severity level based on only two recorded parameters: speed and pavement deterioration index.

Permeable asphalt pavement refers to an asphalt mixture layer with an air void content of more than 18% and internal water permeability and drainage capabilities, which can quickly drain away water on the road surface, improve rainy day travel safety and ride comfort. This paper aims to explore the optimal asphalt mixture mix design for long longitudinal slope roads. By using CT scanning technology to analyze the air void content of different rotated and compacted asphalt mixture specimens, and extensively testing and evaluating the performance of permeable pavement mixtures, the following conclusions are drawn: Based on the research philosophy of functional integration, a new asphalt mixture gradation suitable for long longitudinal slope roads is proposed, with the optimal key factor composition being: 0.075mm passing rate of 7%, 2.36mm passing rate of 20%, 9.5mm passing rate of 55%, and oil-stone ratio of 4.8%. The FAM mixture was divided into three parts for air void analysis, with the upper part having a slightly higher air void content than the lower part. The air void distribution diagram of the FAM mixture is concave, with higher air void rate curves on both sides and a lower middle curve. Compared with laboratory dry measurement method, CT scanning test showed slightly higher air void content in specimens. Through dynamic modulus testing, the strength requirement for road asphalt mixture in pavement structure design was evaluated. It was found that at high temperature conditions (50℃), the minimum dynamic modulus value of the FAM mixture was 323 MPa, with a peak value of 22746MPa at a temperature of -10℃ and a frequency of 25HZ. The dynamic modulus value at high temperature conditions is lower than at low temperature conditions, while the dynamic modulus value at high frequency conditions is higher than at low frequency conditions. This study provides useful information and experimental data for the design of new asphalt mixtures for long longitudinal slope roads, and has conducted in-depth research on the air void distribution and performance of the mixture, providing strong support for related research fields and practical applications.

The aim of innovations in road safety is to contribute to better protection of road users and to minimize damage to their property. The main objective of the article is to identify disparities in the perception of an innovation element in road safety by road users based on their type of employment and the number of kilometers driven per year. The research will evaluate the attitudes of selected groups of road users based on the number of kilometers driven in a calendar year and whether driving a motor vehicle is their main job duty. The analysis involved 239 respondents using a paper questionnaire. Disparities in the perception of innovations in road safety were evaluated using contingency tables, chi-square tests, non-parametric tests, and Cramer's V. Road users feel much safer with a front braking light when crossing traffic of vehicles and pedestrians (71%); at pedestrian crossings (74%); when turning left (63.4%); as well as when crossing traffic of multiple vehicles (62.5%). Disparities among respondents based on the type of employment of the road user were not confirmed. Conversely, road users with a higher number of kilometers driven per year have a more positive perception of the innovation element at pedestrian crossings.

The possibility of using some warnings inside modern vehicles should be an aid to the driving activity. However, the information transferred to users is not always received in the expected way due to the variability and complexity of the road environment. This study, therefore, aims to identify a procedure that allows to ascertain whether drivers receive the data in an appropriate way even during particular manoeuvres, such as passing cyclists on a winding road or, on the contrary, if they represent an unnecessary overload. To answer this question, an experimentation in a simulated environment was set up with which the drivers’ visual behaviour was recorded in presence and absence of a driving aid device (On- Board Unit, OBU). The results show that, in some situation, the information provided by the OBU helps to maintain a more virtuous driving behaviour but, in the most complex ones, drivers acquire information from a smaller number of sources, excluding the aid devices present inside the cockpit. This procedure is useful for ADAS designers in order to refine these instruments but also for road managers who can improve safety by inserting appropriate signs or speed limits.

The estimation of the efficiency of road transport vehicles remains a significant problem for contemporary transport companies, as the technological process is influenced by numerous stochastic impacts, such as demand stochasticity, road conditions uncertainty, transport market fluctuations, etc. To consider the uncertainty related to the estimation of the vehicles’ fleet efficiency, we propose a fuzzy-logic approach, where the efficiency of a given vehicle is described by a membership function. The efficiency of the whole fleet and its rational structure in that case can be evaluated as a fuzzy set. To demonstrate the developed approach, we depict a case study of using cargo vehicles for deliveries of agricultural products in the Republic of Kazakhstan. The numeric results are presented for the selected models of vehicles that a transport company uses to service a set of clients located in Northern Kazakhstan.

This paper deals with identifying the accident black spots and the influencing factors causing accidents using factored analysis in the medium-sized city (Tirunelveli) in India. From the literature review, the geospatial technique to identify the black spots and the factors causing accidents was used for analysis. The most influencing factors driving the accident were identified and ranked based on the repetitive occurrence of accidents in the black spot area. The spearman ranking system obtained the correlation among the factors causing accidents. The factor analysis technique was utilized to identify the key factors driving the repetitive accidents and group them. This study will help transportation planners to understand the factors causing accidents and take appropriate measures to reduce the casualties in the road construction planning stage and existing conditions.

This study addresses the peculiarities of the generation of slope movements in the context of road infrastructures and provide a predictive mapping of susceptibility to movements on slopes adjacent to road infrastructures (rockfalls). An inventory of slopes movements was mapped. From the development of the inventory of constant cases of mobilization that is used as a dependent variable, two statistical models can be obtained and compared for the same study area. One of them is based on the concept of frequency, whilst the other one is based on the application of a logistic regression. The results reveal the preponderant importance of lithology as a predictive variable, followed, at a considerable distance, by the slope gradient. Likewise, the importance of an unnatural and characteristic variable area of study, such as the presence of artificial cuts, is indicated as a causative factor. The results show a high degree of coincidence between the tendency of susceptibility predicted by the model, and the effective presence of empirical mobilization signs on the slopes, with Area Under Curve (AUC) values for Receiver Operating Characteristics (ROC) around 0.8.

The purpose of this paper is to evaluate the acceptance and the utilization of GPS/GPRS-based telematics technology in road transport companies registered in Poland. Telematics technologies are essential for management of energy saving and emissions reduction in road transport. It is in line with the European Union policy of sustainable transportation. The evaluation is based on a survey designed and carried out in 2020. The issues concerning the scope of telematics systems utilization as well as the internal and external factors affecting their use are analysed. The methodology is based on Technology Acceptance Model (TAM) and Structural Equation Modelling (SEM). The results are checked for robustness. Based on the results, it can be reasoned that as a result of the COVID19 pandemic, the companies started to use telematics systems more widely than they did before. Furthermore, the companies employing more people recognize the higher usefulness of telematics systems and are motivated to have the systems more than smaller enterprises; however, TAMs estimated separately for small and medium-sized enterprises did not reveal any significant differences in the parameter estimates.

Multigranulation is a new approach to the Rough Set Theory, where several separability relationships are used to obtain different granulations of the universe. The Multigranulation starts from the existence of different contexts or subsets of features to characterize the objects of the universe. In this paper, a method for the generation of contexts from the construction of similarity relations is proposed.The proposed solution was evaluated in an international database using the KNN classifier. It was also applied in the solution of a real problem in Civil Engineering specifically in Traffic Engineering, the contexts generated from the proposal used to determine the features of higher incidence in the service level of the road. The results achieved both in the international database and in the proposed application demonstrate the applicability of the proposed method.

This study compared the use of chip and slash to minimize the loss of sediment on newly constructed forest road slopes and investigated the annual amount of sediment loss on bare forest road slopes. A runoff block (sample field) was established for each of the four designated test sites (two cutslopes and two fillslopes). Each block had three runoff plots. One of the runoff plots was left empty for the control (CNT), while chip (C) and slash (S), respectively, were deposited in the other two. A total of 108 water samples were taken from the test sites and the amount of their suspended sediment calculated in the laboratory. As a result of this study, it was determined that the amount of soil loss in the control plots was about 1.26 times higher than in the slash plots and 2.21 times higher than in the chip plots. According to the results of variance analysis on the amounts of sediment, a statistically significant difference was found between the suspended sediment quantities transported on the road slopes (P <0.05). However, no statistically significant difference between the suspended sediment quantities transported in the plots and the other variables of aspect, gradient or road slope was revealed by the t-test (P >0.05).

Freight price prediction is a complex problem with multiple determinants. Identifying the most important variables can be used to develop more accurate prediction models and increase the competitiveness of Brazilian soybeans. This study aims to assess the influence of various exogenous variables on the price of soybean road freight and how this influence varies across different distance ranges. A combination of machine learning techniques was employed to evaluate a dataset containing variables related to freight, region, production, fuel, storage, and commercialization. The results indicate that distance is the most significant variable in determining freight costs, aligning with operational expenses such as fuel and labor. Additionally, the exchange rate and export volume emerge as key factors, reflecting the macroeconomic context of Brazilian soybean exports. The stratified analysis highlights the differentiation between short, medium, and long-distance freights, showing that different variables influence the price. Short-distance transportation is mainly geared toward the domestic market, while longer distances are more related to export logistics.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with various sensors are expensive and not suitable for mountainous roads due to poor conditions. To address these challenges, this study proposes a customized Unmanned Aerial Vehicles (UAV) inspection system designed for automatic crack detection. The system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method is proposed to enhance the autonomous navigation of UAV flights by generating path planning in mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the YOLOv8 algorithm is introduced to conduct autonomous crack detection from UAV imagery in the offboard module. To validate our UAV inspection system's performance, we conducted several experiments to assess its accuracy, robustness, and efficiency. The experimental results demonstrate that our UAS inspection system accurately estimates the flight trajectory along the mountainous road for autonomous navigation. Furthermore, the experimental detection results also demonstrate that the YOLOv8 algorithm achieves higher recognition accuracy compared to the YOLOv5, and YOLOv7-tiny algorithms, exceeding 3.1%,and 6.8% of mAP, respectively. In summary, the UAV inspection system proposed in this study can provide a useful tool and technological guidance for the regular inspection of mountainous roads.

Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRU) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system’s performance. The lateral distance between the right side of the commercial vehicle and the VRU as well as obstacles between them and the speed difference can take great influence on the reliability of the assistance system. Depending on the concrete time of the system’s warning signal the accident can be avoided or not by the driver when reacting on this signal.

Acceleration rates of vehicles are crucially important statistics needed in two main aspects: first, they are essential to incident reconstructor professionals as having a verified database on where basing the cinematic reconstructions is definitely difficult and moreover, they are useful in order to understand how to create the safest possible environment on the road. This key topic gives es-sential information for road design like intersection schemes, acceleration and deceleration lane length as well as traffic simulation modelling and furthermore, to educate vehicle drivers when entering the traffic flow and to provide limit acceleration values needed to develop autonomous vehicle’s software in order to guarantee a comfort drive. Literature reports tons of linear acceleration stats mainly referred to vehicle performances claimed by car manufacturers, but these data don’t recreate real drivers’ behaviour when entering the road by turning left or right or crossing an intersection. This work aims to integrate the most reliable data from literature with a hybrid and an electric vehicles statistic, adding also two contemporary thermal engine vehicles, extracted conducting probes that recreates real road environment. The entirety of these experiments reconstructs the scenario in which a car is entering the traffic of a two-lanes road, from a stationary position at a stop sign and the acceleration rates are collected using a M.E.M.S. (Microelectromechanical system). Finally, the space needed between the car that is about to enter or crossing the road and the car that is approaching the intersection is calculated, basing the computations on the acceleration values extracted from the probes. These spaces are pictured, as a way to give a concrete idea to readers.

Human beings naturally move from one location to another in search of basic necessities. Road transportation is a low-cost, versatile, and widely accessible mode of transportation worldwide. However, using fossil-based (FB) fuels in transportation vehicles is inefficient, generates hazardous gases, and worsens environmental degradation. The current work assesses the application of clean fuels such as biofuels, natural gas, electricity, propane, and other emerging fuels to replace FB fuels in transport vehicles. Unlike FB fuels, the use of biohydrogen, biomethane, biodiesel, and bioethanol in transport vehicles resulted in emission reduction by 70 %, 63 %, 41 %, and 54 %, respectively, while electric vehicles lower maintenance costs, ensuring better engine performance, and better engine efficiency. The adoption of natural gas and propane as alternative fuels for transport vehicles is cost-effective, and promotes environmental sustainability. There is a need for targeted financial, infrastructural, and technical investments to further promote the application of these clean fuels for road transport application. The outcome of the study provides updated information and sensitizes automobile engineers, environmentalists, transport fleet operators, governments, and other stakeholders on the benefits derivable in the adoption of clean fuels as alternatives to FB fuels as transport vehicles fuel.

Stemming from the object overlap and undertraining from the few samples, the road dense object detection is confronted with the poor object identification performance and the inability to recognize edge objects. Based on this, one transfer learning-based YOLOv3 approach for identifying dense objects in the road has been proposed. Firstly, Darknet-53 network structure is adopted to obtain pre-trained YOLOv3 model, then the transfer training is introduced as the output layer for the special dataset of 2000 images containing vehicles; in the proposed model, one random function is adapted to intialize and optimize the weights of the transfer training model, which is seperately designed from the pre-trained YOLOv3; and the object detection classifier replaces the fully connected layer, which further improves the detection effect, the reduced size of the network model can further reduce the training and detection time, and can be better applied to actual scenarios. The experimental results demonstrate that the object detection accuracy of the presented approach is 87.75% for the Pascal VOC 2007 dataset, which is superior to the traditional YOLOv3 and the traditional YOLOv2 by 3.05% and 11.15%, respectively. Besides, the test was carried out using UA-DETRAC, a public road vehicle detection dataset, the object detection accuracy of the presented approach reaches 79.23% in detecting images, which is 4.13% better than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the detection accuracy is 1.36% better. Moreover, the detection speed of the proposed YOLOv3 method reaches 31.2 Fps/s in detecting images, which is 7.6 Fps/s faster than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the speed is 4.3 Fps/s faster; the proposed YOLOv3 performs 79.38Bn of floating point operations per second in detecting video, which obviously surpasses the traditional YOLOv3 and the newer object detection algorithm YOLOv5.

Substantial reductions of the human and economic activities like road traffic for several months in 2020 were one of the consequences of the Coronavirus disease. This unprecedented change in urban metabolism also affected temperature and air pollutants. This study investigates the effects of the first COVID-19 lockdown over 43 cities in Europe. It determines the influence of anthropogenic activities on nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5) as well as on land surface temperature (LST), and the surface urban heat island intensity (SUHII), using satellite, modelled, and mobility data. Our findings show that there are great temporal and spatial differences and distinct patterns between the cities regarding the magnitude of change of the variables under study. In general, the results indicate a significant decrease in NO2 concentrations in most of the studied cities compared to the reference period 2015-2019. However, reductions could not be attributed to mobility changes like less traffic at transit stations, contrary to the results of previous studies. O3 levels increased during the first lockdown mainly influenced by the decreasing NO2 concentrations. The PM pattern was inconsistent over time and space. Similar as the NO2 results, no relation to the altered mobility behavior was found. No clear signal could be detected for LST and the SUHII, likely due to dominating meteorological influences. Therefore, single city case studies may be misleading.

In the European Union, the eCall system has been mandatory since March 31, 2018. The system enabled a significant increase in safety on roads and highways, making help faster in the event of an accident. However, based on circuit-switched 2G/3G communications and an onboard device called IVS, it is generally unavailable on legacy vehicles. Some of its limitations tend to be remedied by the future NG eCall based on 4G/5G packet-switched communications. This paper discusses why IVS may be an Achilles’ heel of any future IVS-based eCall and analyzes the advantages of a smartphone-based system. The TEZE system, implemented and installed in Italian highway tunnels, is a first general-purpose safety system, and allows highly reliable smartphone-based emergency calls. It is based on a dedicated low-cost ground infrastructure that allows monitoring of the availability of emergency call service through the mobile radio networks of the operators present inside the tunnels. The system complies with the ISO/IEC 30141:2018 reference standard. Identifying functional/non-functional requirements and their verification criteria provide an overall validation of the analyzed system. The paper concludes by examining some key functionalities for vehicular services that can be implemented in an integrated system based on smartphones and heterogeneous networks.

We are living in a world where people always strive for the luxuries they can get with less hard work and for these smart systems are developed and established all over the world to make life easier for the people. However, in Pakistan being a third-world country we are unable to achieve better results due to the lack of proper transit from one area to another. Sometimes the roads are not in their optimum conditions and hence people have to face a lot of problems while traveling. In urban areas, people cannot reach their destination in time, in some cases, they damage their vehicles due to the cracks on the roads, in medical emergency patient dies most of the time in their transit from rescuing point to the hospital. Similarly, in rural areas where farmers face countless problems while bringing their yield of the season towards the markets. Therefore, having described the severe damage the bad quality of roads is making. A solution is proposed to solve the problems of both the rural and urban population in our project. The project aims to provide the solution to their problem to a certain extent by monitoring the conditions of the roads. The sensors in the system will calculate the values and send them to the cloud server. The cloud server used is the blynk platform where the data will be stored. Moreover, data can be provided to the government in the future for the timely maintenance of the roads, and hence the citizens and lifestyle of the people will be changed. It is expected that the problems of both urban and rural population will be solved to a greater percentage.

Introduction: Road traffic accidents are a real pandemic and incur expenses amounting to 1-2% of every country’s GDP. AESLEME (Association for the Study of Spinal Cord Injuries) – devoted to teaching road safety and health to prevent road accidents – has celebrated its 30th anniversary. AESLEME’s instructors are health workers and people with spinal cord injuries caused by road accidents: their presentations – teaching road safety and sharing information on irreversible injuries – are enhanced by personal stories that help schoolchildren to acquire knowledge on this matter. However, until now, we had not assessed how far this acquisition of knowledge had reached. Methods used: Two multiple-choice tests were given to each of the 8,106 students (12-14 years) who took part. Of the four possible answers, only one of them was correct. The first multiple-choice test was taken before the presentation and the second was taken one month later. Results: After assessing the answers, there was a change in the tendency of the number of correct answers before/after answers for the multiple-choice test, and the number of correct one´s rose one month after the presentation. This increase is statistically significant (p&lt;0.01) and represents a national increase of 61% in the number of correct answers, although this varies from 8% to 278% depending on the region. Conclusions: The assessment, involving over 8,000 people, showed that there has been an improvement in road safety knowledge thanks to education provided by AESLEME’s instructors, and a statistically significant increase was obtained throughout Spain, with an average of 61% (confidence level 95: 53% and 64%).

Abstract: Traffic collisions cause a huge problem of public health in low and middle income countries.. The safe system approach is generally considered as the leading concept on the way to road safety. Based on the fundamental premise that humans make mistakes, the overall traffic system should be ‘forgiving’. Sustainable safe road design is one of the key elements of the safe system approach. However, the road design principles behind the safe system approach are certainly not leading in today’s infrastructure developments in most LMICs. Cities are getting larger and road networks are expanding. In many cases, existing through-roads in local communities are up-graded, resulting in heavy traffic loads and high speeds on places, that are absolutely not suited for this kind of through-traffic. Furthermore a safe system would require that functional design properties of cars and roads would be conceptually integrated, which is not the case at all. Although advanced driver assistance systems are on their way of development for quite a long period, their potential role in the safe system concept is mostly unclear and at least strongly underexposed. The vision on future cars is dominated by the concept of automation. This paper argues that the way to self-driving cars, should take a route via the concept of guidance, i.e. vehicles that guide drivers, both on self-explaining roads and on more or less unsafe roads. Such an in-vehicle support system may help drivers to choose transport mode, route and speed, based on criteria related to safety and sustainability. It is suggested to develop a driver assistance system using relatively simple and cheap technologies, particularly for the purpose of use in LMICs. Such a GUIDE (Generic User Interface for Driving Evolution) may make roads self-explaining - not only by their physical design characteristics - but also by providing in-car guidance for drivers. In future the functional characteristics of both cars and roads should be conceptualized into one integrated safe system, in which the user plays the central role. As such GUIDE may serve as the conceptual bridge between vehicle and roadway characteristics. It is argued that GUIDE is necessary to bring a breakthrough in road safety developments in LMICs and also give an acceleration towards zero fatalities in HICs.

(1) Background: A growing number of communities are re-discovering the value of their streets as important public spaces for many aspects of daily life, thus creating the need for a transformation in the quality of streets. An emerging concept is to accommodate all users of the transportation system, a concept that has been labelled ‘complete streets’. (2) Methods: In this paper, we present sustainable complete streets design criteria that integrate complete streets by adding in socio-environmental design criteria related to aesthetics, environment, liveability, and safety. (3) Results: Proposed design criteria provide a street network which provides improvements in aesthetics, to recover historical urban character and realize historical area planning goals; environment, to increase permeable surfaces, reduce the heat island, and absorb traffic-related air pollution; liveability, to create a public space destination in the urban landscape; and safety, to improve the safety of all road users. and (4) Conclusions: The case study of the urban rehabilitation of the “Mostra d’Oltremare” area and of its cultural and architectural assets in Naples, Italy, highlights the practical application of the proposed criteria and the possibility of using these criteria in other urban contexts.

This paper presents the results of hot smoke tests, conducted in a real road tunnel. The tunnel is located within the expressway S69 in southern Poland between cities Żywiec and Zwardoń. Its common name is Laliki tunnel. It is a bi-directional non-urban tunnel. The length of the tunnel is 678 m and it is inclined by 4%. It is equipped with the longitudinal ventilation system. Two hot smoke tests have been carried out according to Australian Standard AS 4391-1999. Hot smoke tests corresponded to a HRR (Heat Release Rate) equal to respectively 750 kW and 1500 kW. The fire source was located in the middle of the road lane imitating an initial phase of a car fire (respectively 150 m and 265 m from S portal). The temperature distribution was recorded during both tests using a set of fourteen thermocouples mounted at two stand poles located at the main axis of the tunnel on windward. The stand poles were placed at distances of 5 m and 10 m. The recorded data were applied to validate of a numerical model built and solved using Ansys Fluent. The calculated temperature distribution matched the measured values.

Accidents involving heavy road vehicles are often destructive, causing operational losses, human casualties, infrastructure losses, and negative environmental impacts. The risk is especially high in wintertime traffic. Eureka Xecs SafeTrucks-project (Heavy traffic safety improvements by advanced dynamics and road weather services) develops real-time vehicle-specific weather and safety services tailored to each vehicle, based on the vehicle’s own sensor observations combined with data from the service systems and an analysis of the vehicle's own dynamics. The services are also analyzed by Digital Twin modelling, in order evaluate and refine them in a controlled environment. The pilot services are ultimately tested in pilot system within operative heavy traffic. This paper presents the concept and architecture of the platform, with preliminary results of pilot services operation and system evaluation.

In this paper, particular attention is paid to an advanced variant of the plug-in hybrid engine, known as PHEV, which combines two functionalities: the vehicle's internal combustion engine (ICEV) and the electric engine (BEV). The paper also presents the assumptions of the DEVS model, which allows for the estimation of vehicle’s energy consumption. The study described herein also presents the influence of factors such as ambient temperature and constant speed on the PHEV’s energy consumption. It has been shown that the vehicle’s range estimated based on its electronic control module (ECU) is about 20% shorter per annum on average for its year-round operation in every-day driving conditions. When analyzing the energy consumption based on the vehicle’s unit energy consumption model, attention was paid to values that are strongly correlated with the traffic volume and weather conditions affecting the moving vehicle. In addition, the authors emphasized that the estimated total energy consumption of an electric (BEV) or hybrid (PHEV) vehicle, relative to the normative values arising from the type approval test cycle, deviate from the actual values arising from real driving conditions and often vary substantially. In extreme cases, the actual range of a hybrid vehicle is 75% shorter.

Road infrastructure is a key public asset because it benefits the social and economic development of any country. It plays an important role for the development of the industrial complex, the production sector and the road surfaces of transport roads should be of high quality, have a long service life. Road infrastructure, as well as all infrastructure, requires preservation, maintenance and repair. There are special requirements for the roadway that must be observed during construction or repair. This article is devoted to a detailed study of the use of fiber optics sensors (FOS) based on the fiber Bragg grating (FBG) for road surface monitoring. Such a fiber sensor consisting of a fiber Bragg grid and a pair of grids can offer the possibility of simultaneous measurement of deformation and temperature for monitoring the pavement. Temperature and deformation measurements were carried out by installing a sensor on the surface of a made asphalt sample. The built-in fiber sensor based on FBG provides important information about how the pavement structure can withstand the load, subsidence of soil, timely implement road safety and stability measures evaluate and predict the service life of the pavement. The results of the study showed that the synchronicity, repeatability and linearity of the characteristics of the fiber sensor are excellent. The difference between the experimental and theoretical results is about 7%. Thus, based on the results of the obtained data, the fiber sensor on the FBG can be used for monitoring, designing road surfaces and in general transport infrastructure.

This study examines the Health Silk Road (HSR) as a critical component of China's Belt and Road Initiative (BRI) and its implications for global health and international relations. The HSR aims to enhance public health and foster international cooperation in the healthcare sector, with objectives including strengthening healthcare infrastructure, expanding China's global health leadership, and enhancing international health cooperation. Through an in-depth analysis of the China-Pakistan collaboration on healthcare under the China-Pakistan Economic Corridor (CPEC) as a case study, this research explores the motives, implications, and potential concerns associated with the HSR. This research posits that the HSR has a mix of positive and negative implications, making it challenging to categorize the initiative as simply good or bad. While the HSR contributes to improved healthcare services, infrastructure, and capacity-building in participating countries, it also raises concerns about debt sustainability, transparency, and China's geopolitical influence. This research contributes to a nuanced understanding of the HSR's multifaceted impacts and underscores the importance of open dialogue, cooperation, and the sharing of best practices among stakeholders in order to maximize the initiative's benefits and minimize potential negative consequences. By assessing the motives, implications, and concerns of the HSR, this study offers valuable insights for policymakers, global health practitioners, and scholars, highlighting the significance of international collaboration in addressing shared health challenges and promoting sustainable development.

In this study, by employing the think-aloud model, schema theory and the perceptual cycle model (PCM) from the perspective of vulnerable road traffic participants, the current traffic imperfections of a target section in Wudaokou were analyzed to explore potential improvement measures to enhance safety. Specifically, the present study recruited 30 local volunteers, which were equally divided into a pedestrian group, bicyclist group and E-cyclist group. The research procedure was mainly divided into three steps. By using the think-aloud method, we analyzed and recorded the contents of oral reports of the experimental road section. Then, through translation, segmentation, coding, analysis and other techniques, we sorted and studied the report data and drew a conclusion that was universal in improving the current road traffic conditions. It is hoped that through this study, on the one hand, the applicability of the oral report method, schema theory and perceptual cycle model in road traffic safety can be verified, and, on the other hand, some better improvement measures can be extracted for the road traffic state of the Wudaokou section.

Pyrolyzed waste biomass, or biochar, has been suggested as a means to increase plant growth and to mitigate soil salinization, which is a widespread agricultural issue and can reach extreme levels in urban soils impacted by de-icing salts. Soil mixing is enhanced by reduced biochar particle size; however, biochar properties vary with particle size and recent studies suggest that plant growth responses may be maximized at intermediate particle sizes. We examined the responses of two plant species (cowpea (Vigna unguiculata) and velvetleaf (Abutilon theophrasti)) to biochar amendments that spanned a wide range of particle sizes obtained by sieving, with and without de-icing salt additions. The smallest size fractions of biochar reduced plant growth relative to unamended controls. Plant biomass production was generally maximized at intermediate biochar particle size treatments, with particles sizes of 0.5-2.0 mm showing the best response. Mitigation of salt effects was also improved at intermediate biochar particle sizes in this particles size range. Our results emphasize the importance of optimizing biochar particle size to best enhance plant responses to biochar, with particular reference to saline soils.

As an important component of modern service industry, tourism is regarded as a "green" driver to regional economic growth and industrial upgrading. Applying the "quasi-natural experiment" advocated by the “The Belt and Road” Initiative, this paper, based on the method of DID, as-sessed the influence of this initiative on regional tourism development by analyzing panel data collected from 286 prefecture-level cities in China over the period of 2007-2018. It is found that the "The Belt and Road" initiative has stimulated market dynamics and promoted tourism de-velopment in the regions along the route, and has significantly improved indicators such as per capita domestic and inbound tourism revenue and per capita domestic and inbound tourist re-ceipt. Also, the degree of contribution of domestic tourists to the tourism economy of the re-gions where the policy has been implemented is stronger than that of inbound tourists. In sum, the results of this paper not only confirm that the “The Belt and Road” Initiative has significantly contributed to the high-quality tourism development of Chinese provinces along the routes, but also provide important policy enlightenment for the upgrading of regional tourism industry and regional economic balance.

Understanding and evaluating urban investment environment is essential for effectively improving the efficiency of resource allocation between cities and promoting overall development of the regional economy. This paper takes 15 node cities on maritime Silk Road covered by the “Belt and Road” as the research object, establishes a dynamic evaluation index system for investment environment, and uses projection pursuit cluster to analyze and evaluate the investment environment of the cities. It is found that the investment environment potential of a city is directly related to the level of social development, economic development, and the degree of opening to the outside world. It is recommended that node cities should seize the important opportunity of the construction of the Maritime Silk Road, introduce world-wide human, financial and material resources to promote regional resources allocation and flow, and continuously improve and upgrade the investment environment quality.

Spatial data conflation plays a fundamental role in in many aspects of modern Geographic Information Systems (GIS) research and development such as geospatial data visualization, incremental updating of databases and disaster evaluation. The primary objective of conflation is to derive valuable information based on the comparison of multiple spatial data sources of homogeneous or heterogeneous nature. This paper reviews the state of art of the concept of conflation, feature matching, the most important progresses of conflation between image and road network data, and the complexity of spatial conflation.

Laser scanning systems make use of Light Detection and Ranging (LiDAR) technology to acquire accurately georeferenced sets of dense 3D point cloud data. The information acquired using these systems produces better knowledge about the terrain objects which are inherently 3D in nature. The LiDAR data acquired from mobile, airborne or terrestrial platforms provides several benefit over conventional sources of data acquisition in terms of accuracy, resolution and attributes. However, the large volume and scale of LiDAR data have inhibited the development of automated feature extraction algorithms due to the extensive computational cost involved in it. Moreover, the heterogeneously distributed point cloud, which represents objects with varying size, point density, holes and complicated structures pose a great challenge for data processing. Currently, geospatial database systems do not provide a robust solution for efficient storage and accessibility of raw data in a way that data processing could be applied based on optimal spatial extent. In this paper, we present Global LiDAR and Imagery Mobile Processing Spatial Environment (GLIMPSE) system that provides a framework for storage, management and integration of 3D LiDAR data acquired from multiple platforms. The system facilitates an efficient accessibility to the raw dataset, which is hierarchically represented in a geographically meaningful way. We utilise the GLIMPSE system to automatically extract road median from Airborne Laser Scanning (ALS) point cloud. In the first part of this paper, we detail an approach to efficiently retrieve the point cloud data from the GLIMPSE system for a particular geographic area based on user requirements. In the second part, we present an algorithm to automatically extract road median from the retrieved LiDAR data. The developed road median extraction algorithm utilises the LiDAR elevation and intensity attributes to distinguish the median from the road surface. We successfully tested our algorithms on two road sections consisting of distinct road median types based on concrete and grass-hedge barriers. The use of GLIMPSE improved the efficiency of the road median extraction in terms of fast accessibility to ALS point cloud data for the required road sections. The developed system and its associated algorithms provide a comprehensive solution to the user's requirement for an efficient storage, integration, retrieval and processing of large volumes of LiDAR point cloud data. These findings and knowledge contribute to a more rapid, cost-effective and comprehensive approach to surveying road networks.

Rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can obtain images with centimeter or even subcentimeter ground resolution, which provides a new efficient and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we propose a real-time edge reconstruction crack detection network (ERNet) which adopts multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and background. To capture global dependencies across spatial and channel levels, we propose an efficient bilateral decomposed convolutional attention module (BDAM) that combines depth separable convolution and dilated convolution to capture global dependencies across spatial and channel levels. To enhance the accuracy of crack detection, we use a coordinate-based fusion module that integrates spatial, semantic, and edge reconstruction information. In addition, we propose an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. Experimental results demonstrate that our network achieves the best balance between accuracy and inference speed.

In an effort to enhance the efficiency and safety of open-pit mines, this study explores the optimization of end slope road parameters and slope structures, specifically focusing on unmanned driving lanes.A significant aspect of the study is the development of a truck trajectory offset model, which considers the different reaction times between automated sensors and human drivers in adapting to environmental changes. To test these concepts, the study uses numerical simulations to confirm the stability of the proposed end slope designs. Using Victory West Mine No. 1 as a case study, the research determines the optimized width for unmanned driving lanes and the maximum angle for safe steepening of end slopes. The findings indicate that the optimized unmanned lane width for NTE240 mining dump trucks is 1743 mm, allowing for a 2-degree increase in the slope angle at the south end slope. This optimization leads to a steep mining stripping volume of 3.2735 million m³ and a coal output of 2.49628 million tons, maintaining a stripping ratio of 1.31m³/t. These results demonstrate that the unmanned driving road width optimization not only ensures slope safety but also significantly boosts the economic benefits of steep mining in open-pit mines.

Autonomous vehicle technology, employing image sensors for road marking recognition, is largely dependent on the Lane Departure Warning System (LDWS) to prevent vehicular deviations from driving lanes. The effectiveness of image-based lane recognition, however, is susceptible to environmental influences like weather conditions and the surrounding road environment. This study investigates the impact of road marking retro-reflectivity on LDWS under varied environmental conditions. Conducted at the Yeoncheon SOC Demonstration Research Center, the experiments encompassed a spectrum of weather scenarios, incorporating rainfall and shifts between day and night lighting. Controlled wear was applied to white, yellow, and blue road markings, followed by measuring their retro-reflectivity at different stages of degradation. The LDWS's performance was evaluated by assessing the recognition rates of these markings under these diverse environmental settings. The study reveals that enhanced retro-reflectivity significantly boosts LDWS detection, especially in challenging weather conditions. Furthermore, the research led to the development of a simulation framework to analyze the cost-effectiveness of road marking maintenance, aligning it with the safety needs of autonomous vehicles. This framework suggests the necessity for updated road marking guidelines that cater to the advanced needs of driver assistance systems and autonomous vehicle technology.

As the lifespan of porous asphalt pavement extends, it invariably experiences performance degradation and an increase in tire/pavement noise compared with the original surface when it is newly built. To understand the medium-term performance of porous asphalt pavement during service and reveal the influence of different pavement performance indicators on tire/pavement noise generation, a seven-year continuous observation and data analysis study was conducted. Porous asphalt with different designed material parameters was taken into account, including thin-layer porous asphalt overlay with a maximum nominal aggregate size of 10mm (PUC-10), porous asphalt with a maximum nominal aggregate size of 13mm (PAC-13), and a two-layer porous asphalt incorporating both PUC-10 and PAC-13 (PUC-10+PAC-13). Using automated pavement technology testing equipment, key performance indexes, including damage rate (DR), rut depth (RD), international roughness index (IRI), and sideway force coefficient (SFC), were measured and calculated. The on-board sound intensity (OBSI) method was used to test noise levels in three types of porous asphalt pavement structures and on a dense graded easy compact asphalt concrete (ECA) for comparison. The progressions of the medium-term performances were analyzed according to the measured results. The correlations between tire/pavement noise levels and testing speed, testing lane, pavement structure, and road condition indicators were investigated by regression. The research results revealed that the DR and SFC of the three types of porous asphalt pavement displayed more significant attenuation in the service period, while the IRI and RD showed small changes. Among them, the SFC demonstrated significant attenuation over the first 4 years, decreasing between 13.8% and 17.0%. From the 3-7 years of operation, the transverse crack disease in porous asphalt pavement progressed rapidly. The three porous asphalt pavement structures tested exhibited commendable noise reduction capabilities. Compared to ECA, the OBSI noise levels of these structures were lower by 2.09 dB, 1.53 dB, and 2.88 dB, respectively. Compared to other pavement condition indicators, RD had a more significant impact on the OBSI value of the PUC-10 porous asphalt pavement. For the PUC-10+PAC-13 porous asphalt pavement, there was a significant linear relationship between the OBSI value and SFC, mainly due to polishing of the coarse aggregate surface. It leads to a decrease of the micro-texture, which lies in increasing tire/pavement noise at high-frequency ranges and decreasing SFC. This study will have guiding significance for the research of the attenuated law and durability improvement of porous asphalt tire/pavement noise.

Pedestrians, bicyclists, scooterists are Vulnerable Road Users (VRU) in traffic accidents. The number of fatalities and injuries in traffic accidents involving vulnerable road users has been steadily increasing in the last two decades in the U.S. even though road vehicles now have perception sensors like camera to detect risk and issue collision warnings or apply emergency braking. Perception sensors like camera are highly affected by lighting and weather conditions. Camera, radar and lidar cannot detect vulnerable road users in partially occluded and occluded situations. This paper proposes the use of Vehicle-to-VRU communication to inform nearby vehicles of VRUs having trajectories with potential collision risk. An Android smartphone app with low energy Bluetooth (BLE) advertising is developed and used for this communication. The same app is also used to collect motion data of VRUs for training. VRU motion data is smoothed using a Kalman filter and an LSTM neural network is used for future motion prediction. This information is used in an algorithm comparing Time-To-collision-Zone (TTZ) for the vehicle and VRU and issue a driver warning with different severity levels. The warning severity level is based on analysis of real data from a smart intersection for close vehicle and VRU interactions. The resulting driver warning system is demonstrated using proof-of-concept experiments. The method can easily be extended to a VRU collision mitigation system.

The rural road landscape is crucial in forming rural areas' landscape character (LC). As a platform for portraying the rural landscape, the rural roads demonstrate the area's unique natural and cultural characteristics to the visitors. However, with the continuous development of rural areas, the rural LC has been severely impacted, thus impacting visitors' visual experience. In order to preserve and protect the rural landscape, this study aims to assess the visual quality of rural road landscapes based on public preference and heatmap analysis. The results indicated that most of the participants had a higher level of preference for rural landscapes with open horizontal views represented by agricultural areas such as paddy fields. It was also found that different paddy field characters based on their planting stages can also positively affect the visual quality of rural road landscapes. The study also revealed that rural LCs with roadside settlements, commercial structures, mixed agricultural crops, and vegetation received low preference ratings. These characters negatively impact the visual quality of the rural road landscape. These findings provide significant insight for planners and decision-makers regarding protecting and preserving the essential rural road landscapes for the rural tourism experience.

(1) Background: This report examines the unique demands of off-road triathlon (XT) by presenting physiological, field, and race data from a national champion off-road triathlete using several years of laboratory and field data to detail training and race intensity. (2) Methods: Laboratory and field data were collected when the athlete was at near peak fitness and included oxygen consumption (VO2), heart rate (HR), power output (W), and blood lactate (BLC) during cycling and running, while HR, cycling W, and running metrics were obtained from training and race data files over a period of seven years. Intensity was described using % HR max zones (Z) 1 &lt; 75%, 2 = 75 - 87%, and Zone 3 &gt; 87%, and W. An ordinary least squares analysis was used to model differences between event types. (3) Results: Weather conditions were not different across events. XT events had twice the elevation change (p&lt;0.01) and two-three times greater W’ (p&lt; 0.001) than road triathlon (ROAD), but similar HR intensity profiles (max, avg, and zones); both events are predominately performed at &gt; Z2 or higher intensity. Championship XT events were longer (p&lt;0.01) , with higher kJ expenditure (p&lt;0.001). OLS modelling suggested three variables were strongly related (R2 = 0.84; p &lt; 0.0001) to cycling performance: event type (XT vs ROAD), total meters climbed, and total bike duration. Championship XT runs were slower than either regional (p&lt;0.05) or ROAD (p&lt;0.01) runs, but HR intensity profiles similar. OLS modelling indicates that slower running is linked to either greater total bike kJ expenditure (R2 = 0.57; p&lt;0.001), or total meters gained (R2 = 0.52; p&lt;0.001). Race simulation data support these findings but failed to produce meaningful differences in running. Conclusions: XT race demands are unique and mirror MTB and trail running demands. XT athletes must be mindful of developing anaerobic fitness, technical ability, and aerobic fitness, all of which contribute to off-road cycling economy. It is unclear whether XT cycling affects subsequent running performance different from ROAD cycling.

Aiming the characterization of road dusts on the surrounding villages of an active mine and to assess metal(loids) bioaccessible fractions, a dust samples survey was undertaken. To achieve these objectives, pseudototal element concentration, mineralogical composition, Enrichment factor and in vitro extraction UBM assay for both gastric (G) and gastrointestinal (GI) phases, were determined. Results obtained revealed that arsenic represents the higher risk to humans, with mean pseudototal values higher than the maximum reference value range. Enrichment factor points to As significant to very high enrichment in all villages. Particles enriched in As, Ca, Fe, Cu, Al, Ti were identified by SEM-EDS in weathered agglomerates, linked to mine wastes and long-distance transport, both by wind and/or traffic. Arsenic bioaccessibility fraction (%BAF) presents low values in the studied samples, possibly due to low Fe complexes solubility with adsorbed As, limiting arsenic release and reducing its bioaccessibility. Concentrations of Cd bioaccessible G and GI phases are within the reference values range, while for Cu are above and for Pb are lower, than reference value range. Results show that the pseudototal fraction risk is overestimated when comparing to BAF (bioaccessible fraction), nevertheless, total G and GI risk are above the carcinogenic target risk (1.00E-06) in most samples. Carcinogenic risk of the bioaccessible contaminants shows that As is the element that represents higher risk for developing cancer over a lifetime, being ingestion the main risk route.

Road dust resuspension, especially the particulate matter fraction below 10 µm (PM10), is one of the main air quality management challenges in Europe. Road dust samples were collected from representative streets (suburban and urban) of the city of Viana do Castelo, Portugal. PM10 emission factors (mg veh-1 km-1) ranging from 49 (asphalt) to 330 (cobble stone) were estimated by means of the United Stated Environmental Protection Agency method. Two road dust fractions (< 0.074 mm and from 0.0074 to 1 mm) were characterised for their geochemical, mineralogical and morphological properties. In urban streets, road dusts reveal the contribution from traffic emissions, with higher concentrations of e.g. Cu, Zn, and Pb. In the suburban area, agriculture practices likely contributed to As concentrations of 180 mg kg-1 in the finest road dust fraction. Samples are primarily composed of quartz, but also of muscovite, albite, kaolinite, microcline, Fe-enstatite, graphite and amorphous content. Particle morphology clearly shows the link with natural and traffic related materials, with well-formed minerals and irregular aggregates. The hazard quotient suggests a probability to induce non-carcinogenic adverse health effects in children by ingestion of Zr. Arsenic in the suburban street represents a human health risk of 1.58 x 10-4.

Prediction of the likely evolution in the traffic scenes is a challenging task because of high uncertainty of sensing technology and dynamic environment. It leads to failure of planning for intelligent agents like autonomous vehicles. In this paper, we propose a fluid-based physical model to present the influence of surrounding object's motion on driving safety. In our pipeline, the input sensor could be LiDAR, camera, or multi-modal data. We use a Kalman filter to estimate the state space of each detected object, and adopt the properties of stable fluid to build a riskmap based on the density field. The noisy state space are then modeled as the boundary conditions in the simulation of advection and diffusion process. We test our approach on the public KITTI dataset and find this model could handle the short-term prediction in case of misdetection and tracking failure caused by object occlusion, which shows promising in collision risk assessment on road scenes.