Version 1
: Received: 9 June 2024 / Approved: 11 June 2024 / Online: 11 June 2024 (06:57:42 CEST)
How to cite:
Elhenawy, M.; Mohammed, A.; Tahmasseby, S.; Salam, S.; Alhajyaseen, W.; Karakikes, I.; Tsouros, I.; Tsirimpa, A.; Polydoropoulou, A. Shared E-scooter Usage Analysis considering the Effect of Weather Conditions and Land Use: Qatar Case Study. Preprints2024, 2024060660. https://doi.org/10.20944/preprints202406.0660.v1
Elhenawy, M.; Mohammed, A.; Tahmasseby, S.; Salam, S.; Alhajyaseen, W.; Karakikes, I.; Tsouros, I.; Tsirimpa, A.; Polydoropoulou, A. Shared E-scooter Usage Analysis considering the Effect of Weather Conditions and Land Use: Qatar Case Study. Preprints 2024, 2024060660. https://doi.org/10.20944/preprints202406.0660.v1
Elhenawy, M.; Mohammed, A.; Tahmasseby, S.; Salam, S.; Alhajyaseen, W.; Karakikes, I.; Tsouros, I.; Tsirimpa, A.; Polydoropoulou, A. Shared E-scooter Usage Analysis considering the Effect of Weather Conditions and Land Use: Qatar Case Study. Preprints2024, 2024060660. https://doi.org/10.20944/preprints202406.0660.v1
APA Style
Elhenawy, M., Mohammed, A., Tahmasseby, S., Salam, S., Alhajyaseen, W., Karakikes, I., Tsouros, I., Tsirimpa, A., & Polydoropoulou, A. (2024). Shared E-scooter Usage Analysis considering the Effect of Weather Conditions and Land Use: Qatar Case Study. Preprints. https://doi.org/10.20944/preprints202406.0660.v1
Chicago/Turabian Style
Elhenawy, M., Athina Tsirimpa and Amalia Polydoropoulou. 2024 "Shared E-scooter Usage Analysis considering the Effect of Weather Conditions and Land Use: Qatar Case Study" Preprints. https://doi.org/10.20944/preprints202406.0660.v1
Abstract
Shared e-scooters are gaining popularity as a solution for first and last-mile connectivity in urban areas. This study conducted in Doha, Qatar, aimed to understand how weather and land use patterns affect e-scooter usage, utilizing various datasets. Given the novelty of micromobility systems in Qatar and their sensitivity to the local hot and humid climate, the study employed a comprehensive analytical approach. Analysis revealed that e-scooter demand spikes on weekends, particularly in the afternoon. Influential land use factors include proximity to universities and employment centers, while a higher presence of Qatari females in an area correlates with reduced usage, pointing to cultural influences. Weather conditions, especially humidity and extreme temperatures, significantly impact e-scooter demand. The study employed the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for trip clustering and the Random Forest (RF) algorithm to model trip counts, considering temperature and humidity. Insights showed humidity as a critical predictor of hourly e-scooter trip counts. These findings underscore the importance of considering weather, land use, the relationship between land use characteristics and weather variations, and finally cultural factors in optimizing e-scooter services. By integrating data analysis and machine learning, the study offers valuable insights for enhancing urban mobility and transportation planning in GCC countries, promoting sustainable urban mobility.
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
