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Int. J. Environ. Res. Public Health 2016, 13(11), 1043; doi:10.3390/ijerph13111043

Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data

1
Department of Traffic Engineering and Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
2
Department of Civil & Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA
3
College of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
*
Author to whom correspondence should be addressed.
Academic Editor: Harry Timmermans
Received: 21 August 2016 / Revised: 13 October 2016 / Accepted: 19 October 2016 / Published: 26 October 2016
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
View Full-Text   |   Download PDF [314 KB, uploaded 26 October 2016]

Abstract

Random effect panel data hurdle models are established to research the daily crash frequency on a mountainous section of highway I-70 in Colorado. Road Weather Information System (RWIS) real-time traffic and weather and road surface conditions are merged into the models incorporating road characteristics. The random effect hurdle negative binomial (REHNB) model is developed to study the daily crash frequency along with three other competing models. The proposed model considers the serial correlation of observations, the unbalanced panel-data structure, and dominating zeroes. Based on several statistical tests, the REHNB model is identified as the most appropriate one among four candidate models for a typical mountainous highway. The results show that: (1) the presence of over-dispersion in the short-term crash frequency data is due to both excess zeros and unobserved heterogeneity in the crash data; and (2) the REHNB model is suitable for this type of data. Moreover, time-varying variables including weather conditions, road surface conditions and traffic conditions are found to play importation roles in crash frequency. Besides the methodological advancements, the proposed technology bears great potential for engineering applications to develop short-term crash frequency models by utilizing detailed data from field monitoring data such as RWIS, which is becoming more accessible around the world. View Full-Text
Keywords: daily crash frequency; short-term driving environment; panel data; hurdle negative binomial; random effect daily crash frequency; short-term driving environment; panel data; hurdle negative binomial; random effect
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. (CC BY 4.0).

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Chen, F.; Ma, X.; Chen, S.; Yang, L. Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data. Int. J. Environ. Res. Public Health 2016, 13, 1043.

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