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Discrete Dynamics in Nature and SocietyVolume 2013 2013, Article ID 172726, 8 pages

Research Article

Department of Traffic Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

School of Transportation, Southeast University, Nanjing 210096, China

Received 29 August 2013; Revised 12 November 2013; Accepted 25 November 2013

Academic Editor: Wuhong Wang

Copyright © 2013 Zhu-Ping Zhou et al. 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.


Pedestrian crashes, making up a large proportion of road casualties, are more likely to occur at signalized intersections in China. This paper aims to study the different pedestrian behaviors of regular users, late starters, sneakers, and partial sneakers. Behavior information was observed manually in the field study. After that, the survey team distributed a questionnaire to the same participant who has been observed, to acquire detailed demographic and socioeconomic characteristics as well as attitude and preference indicators. Totally, 1878 pedestrians were surveyed at 16 signalized intersections in Nanjing. First, correlation analysis is performed to analyze each factor’s effect. Then, five latent variables including safety, conformity, comfort, flexibility, and fastness are obtained by structure equation modeling SEM. Moreover, based on the results of SEM, a multinomial logit model with latent variables is developed to describe how the factors influence pedestrians’ behavior. Finally, some conclusions are drawn from the model: 1 for the choice of being late starters, arrival time, the presence of oncoming cars, and crosswalk length are the most important factors; 2 gender has the most significant effect on the pedestrians to be sneakers; and 3 age is the most important factor when pedestrians choose to be partial sneakers.

Autor: Zhu-Ping Zhou, Ying-Shun Liu, Wei Wang, and Yong Zhang



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