Citation

Wang Y, Zhou Z, Chen W, Wang T, Wang L. Transp. Res. F Traffic Psychol. Behav. 2024; 102: 213-232.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.trf.2024.02.018

PMID

unavailable

Abstract

In developing countries like China, non-signalised crosswalks represent a considerable risk to pedestrian safety. This study introduces a "yielding dilemma zone" model to tackle the decision-making challenges encountered by drivers at these crosswalks. Field data collected via drones at three non-signalised crosswalks in Xi'an (China) facilitated the analysis of 1687 instances of driver yielding behaviour. The study employed Support Vector Machine (SVM), Artificial Neural Network (ANN), and Binary Logistic Regression (BLR) models to establish the boundaries of the dilemma zone. Additionally, it utilized surrogate safety metrics, such as the deceleration rate required to stop (DRS) and time to crossing (TC), to compare risks between dilemma and non-dilemma zones. Each modelling approach provided insightful findings. The SVM and ANN models identified the speed-distance dilemma zones, while the BLR model determined the multifactorial dilemma zones by considering characteristics such as pedestrian, vehicle, and traffic condition. The findings underscore the significance of the "yielding dilemma zone" for understanding driver-pedestrian interactions at non-signalised crosswalks. The innovative multi-model approach uncovered that pedestrian speed, vehicle type, vehicle approach speed, adjacent vehicle number, driving directions, and pedestrian crossing stage significantly influence this interaction. indicated a higher probability of collisions within dilemma zones compared to non-dilemma zones across all models. The study suggests that the SVM model could be instrumental in creating passive physical warning signs, while the ANN and BLR models could significantly contribute to real-time pedestrian and driver assistance systems. The "yielding dilemma zone" model, with its comprehensive approach, presents a promising avenue for enhancing pedestrian safety, intelligent transportation systems, and traffic management at non-signalised crosswalks.

Language: en

Keywords

Dilemma zone; Machine learning; Pedestrian-vehicle interaction; Yielding behaviour