Econometric and Machine Learning Methods to Identify Pedestrian Crash Patterns

Abstract

Walking plays an important role in overcoming many challenges nowadays, and governments and local authorities are encouraging healthy and environmentally sustainable lifestyles. Nevertheless, pedestrians are the most vulnerable road users and crashes with pedestrian involvement are a serious concern. Thus, the identification of pedestrian crash patterns is crucial to identify appropriate safety countermeasures. The aims of the study are (1) to identify the road infrastructure, environmental, vehicle, and driver-related patterns that are associated with an overrepresentation of pedestrian crashes, and (2) to identify safety countermeasures to mitigate the detected pedestrian crash patterns. The analysis carried out an econometric model, namely the mixed logit model, and the association rules and the classification tree algorithm, as machine learning tools, to analyse the patterns contributing to the overrepresentation of pedestrian crashes in Italy. The dataset consists of 874,847 crashes—including 101,032 pedestrian crashes—that occurred in Italy from 2014 to 2018. The methodological approach adopted in the study was effective in uncovering relations among road infrastructure, environmental, vehicle, and driver-related patterns, and the overrepresentation of pedestrian crashes. The mixed logit provided a clue on the impact of each pattern on the pedestrian crash occurrence, whereas the association rules and the classification tree detected the associations among the patterns with insights on how the co-occurrence of more factors could be detrimental to pedestrian safety. Drivers’ behaviour and psychophysical state turned out to be crucial patterns related to pedestrian crashes’ overrepresentation. Based on the identified crash patterns, safety countermeasures have been proposed.

A Collaborative Monitoring Method for Traffic Situations under Urban Road Emergencies

“…The research in [44] achieves accident reduction by incorporating pedestrian safety considerations into the planning, design, implementation and management of urban transportation. There is also a systematic and team-based approach to redesigning transit streets to improve pedestrian, bicycle and public transport traffic and safety [45].…”

Exploring Factors Affecting Crash Injury Severity with Consideration of Secondary Collisions in Freeway Tunnels

“…Additionally, simple traffic control devices for driver attention guidance and speed reduction, such as warning signs, perceptual treatments, and delineation treatments, can also help to positively reduce injury severities and secondary collisions [ 89 ]. Moreover, as noted by Rella Riccardi, et al [ 90 ], educational campaigns can be an excellent tool to motivate drivers to undertake safety-oriented behavior in freeway tunnels. In addition, advanced road and vehicle technologies such as advanced driving assistant systems (ADASs) and intelligent transportation systems (ITS) will help to reduce secondary collisions in tunnel crashes, and they will, in turn, reduce injury severities.…”

Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method

“…Machine learning (ML) methods have shown increasing growth in safety analysis in recent years [27,28]. These methods involve processes for identifying hidden structures, associations, and patterns [29][30][31][32][33][34][35][36]. These techniques utilize complex structures and algorithms to understand patterns and relationships between input and output data, typically outperforming statistical models in comparative studies [37][38][39][40].…”