A method in modeling interactive pedestrian crossing and driver yielding decisions during their interactions at intersections. (July 2022)

• Record Type: Journal Article
• Authors: Fu, Ting; Yu, Xiaochen; Xiong, Binglei; Jiang, Chaozhe; Wang, Junhua; Shangguan, Qiangqiang; Xu, Wenxiang
• Is Part Of: Transportation research. Volume 88(2022)
• Page Start: 37
• Page End: 53
• Publication Date: 2022-07
• Subjects: Pedestrian-vehicle interaction -- Distance-velocity framework -- Pedestrian crossing and driver yielding decision -- Interactive decision behavior; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2022.05.005
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: The interactive pedestrian crossing and vehicle yielding decisions are modeled. Impacts of their behavior on each other's decision (interactive impacts) are explored. A recent-proposed Distance-Velocity model is used to describe interaction process. The distance-velocity based model outperformed the typical gap-based model. Interactive impacts from each other contributed most on their decisions. Abstract: Investigating pedestrian crossing and driver yielding decisions should be an important focus considering the high risks of pedestrians in exposed to motorized traffic. Limitations, however, exist in previous studies – variables considered previously have been limited; how their behavior affect each other (defined as interactive impacts) were not sufficiently considered. This paper aims to provide a methodological approach for pedestrian crossing and driver yielding decisions during their interactions, considering of different variable types including interactive impact variables, traffic condition variables, road design variables, and environment variables. A Distance-Velocity (DV) framework proposed in an earlier study is introduced for definitions and concepts in studying pedestrian-vehicle interactions. Logistic regression, support vector machines, neural networks and random forests, are introduced as candidate models. A case study involving six crosswalk locations is conducted, focusing on interactions between pedestrians and right-turn vehicles. The proposed methodological approach is applied, with the performance of the four machine learning methods compared in terms of model generalization and confusion matrix. The model with the best performance is further compared to the typical gap-based model. Results show that random forest and logistic regression models performed the best in modeling pedestrian crossing and driver yielding decisions respective, in terms of model generalization. Besides, the DV-based modeling method (average accuracy of over 90% for pedestrians and 80% for drivers) outperformed the traditional gap-based method in all test seeds. As a key finding, interactive impacts from each other (the pedestrian and the driver) act as a key contributing variable on their decisions.