Citation

Zhang X, Liu P, Chen Y, Bai L, Wang W. Traffic Injury Prev. 2014; 15(6): 645-651.

Affiliation

a School of Transportation , Southeast University , Si Pai Lou #2 , Nanjing , China , 210096 Phone: 01186-15295507682.

Copyright

(Copyright © 2014, Informa - Taylor and Francis Group)

DOI

10.1080/15389588.2013.860526

PMID

24215633

Abstract

OBJECTIVE: The primary objective of this study was to identify if the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. METHODS: Using data collected at thirty approaches at twenty signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. RESULTS: The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. CONCLUSIONS: The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The uses of conflict predictive models have potential to expand the uses of surrogate safety measures in safety estimation and evaluation. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

Language: en