Literature DB >> 27565043

Analysis of vehicle-bicycle interactions at unsignalized crossings: A probabilistic approach and application.

Ary P Silvano1, Haris N Koutsopoulos2, Xiaoliang Ma3.   

Abstract

In the last decades, bicycle usage has been increasing in many countries due to the potential environmental and health benefits. Therefore, there is a need to better understand cyclists' interactions with vehicles, and to build models and tools for evaluating multimodal transportation infrastructure with respect to cycling safety, accessibility, and other planning aspects. This paper presents a modeling framework to describe driver-cyclist interactions when they are approaching a conflicting zone. In particular, the car driver yielding behavior is modeled as a function of a number of explanatory variables. A two-level hierarchical, probabilistic framework (based on discrete choice theory) is proposed to capture the driver's yielding decision process when interacting with a cyclist. The first level models the probability of the car driver perceiving a situation with a bicycle as a potential conflict whereas the second models the probability of yielding given that a conflict has been perceived by the driver. The framework also incorporates the randomness of the location of the drivers' decision point. The methodology is applied in a case study using observations at a typical Swedish roundabout. The results show that the conflict probability is affected differently depending on the user (cyclist or driver) who arrives at the interaction zone first. The yielding probability depends on the speed of the vehicle and the proximity of the cyclist.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Keywords:  Cyclist safety; Logit model; Maximum likelihood estimation; Roundabout; Unsignalized intersection; Vehicle-bicycle interaction; Yielding behavior

Mesh:

Year:  2016        PMID: 27565043     DOI: 10.1016/j.aap.2016.08.016

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  1 in total

1.  Characteristics of cyclist crashes in Italy using latent class analysis and association rule mining.

Authors:  Gabriele Prati; Marco De Angelis; Víctor Marín Puchades; Federico Fraboni; Luca Pietrantoni
Journal:  PLoS One       Date:  2017-02-03       Impact factor: 3.240

  1 in total

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