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A bilevel model for multivariate risk analysis of pedestrians' crossing behavior at signalized intersections

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journal contribution
posted on 2014-05-21, 13:39 authored by Baibing LiBaibing Li
Pedestrians who cross streets during the red-man phase of traffic light signals expose themselves to safety and health hazards and hence are considered to be at risk. Pedestrians' street-crossing behavior is in general the outcome of interaction between pedestrians and vehicles: the gaps between vehicles provide an opportunity for pedestrians to cross the street, and pedestrians may or may not accept the street-crossing risk during the red-man phase. In this paper, we propose a multivariate method to investigate pedestrians' risk exposure associated with unsafe crossings. The proposed method consists of two hierarchically interconnected generalized linear models that characterize two different facets of the unsafe crossing behavior. It gauges pedestrians' attitudes toward risk-taking and also measures the impact of potential risk factors on pedestrians' intended waiting times during the red-man phase of the traffic lights. A Bayesian approach with the data augmentation method is used to draw statistical inference for the parameters associated with risk exposure. The proposed method is illustrated using field traffic data. © 2014 Elsevier Ltd.

History

School

  • Business and Economics

Department

  • Business

Published in

Transportation Research Part B: Methodological

Volume

65

Pages

18 - 30

Citation

LI, B., 2014. A bilevel model for multivariate risk analysis of pedestrians' crossing behavior at signalized intersections. Transportation Research Part B: Methodological, 65, pp. 18 - 30

Publisher

© Elsevier

Version

  • AM (Accepted Manuscript)

Acceptance date

2014-03-21

Publication date

2014-04-23

Copyright date

2014

Notes

This article was published in the journal, Transportation Research Part B: Methodological [© Elsevier]. The definitive version is available at: http://dx.doi.org/10.1016/j.trb.2014.03.003

ISSN

0191-2615

Language

  • en