preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202407.1180.v1 (registering DOI)

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 12 July 2024 / Approved: 15 July 2024 / Online: 15 July 2024 (19:47:23 CEST)

With the increasing incidents of fatal road injuries, there is an urgent need for developing effective road safety management systems. The study aims to develop predictive models based on machine learning to forecast the likelihood of road collisions depending on factors like weather, road condition, time, and driver behaviour in Chicago, USA. A machine learning approach has been applied to the crash dataset to evaluate the factors affecting the prevalence of road accidents. Python programming and the Jupyter Notebook platform have been used for performing descriptive statistics, correlation and three classification algorithms (Random Forest, K-Nearest Neighbor (KNN), Decision Tree and MLP Classification). Obtained accuracy of the KNN classifier is slightly higher than the other two classification models. The research explored insights into collision patterns related to roads, locations, and intersections. The study helps to increase road safety through targeted interventions with resource prioritisation, reducing the frequency and severity of traffic incidents by leveraging historical accident data with diverse spatial analysis techniques.

Traffic Crashes; Machine Learning; Predictive Modeling; Road Safety; Crash Severity

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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