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

Feng Chen, Xiaoxiang Ma, Suren Chen and Lin Yang
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Feng Chen: 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
Xiaoxiang Ma: Department of Civil & Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA
Suren Chen: Department of Civil & Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA
Lin Yang: College of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, China

IJERPH, 2016, vol. 13, issue 11, 1-11

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.

Keywords: daily crash frequency; short-term driving environment; panel data; hurdle negative binomial; random effect (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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