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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Feng Chen & Xiaoxiang Ma & Suren Chen & Lin Yang, 2016. "Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data," IJERPH, MDPI, vol. 13(11), pages 1-11, October.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:11:p:1043-:d:81480
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    References listed on IDEAS

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    1. Golob, Thomas F. & Recker, Wilfred W., 2004. "A method for relating type of crash to traffic flow characteristics on urban freeways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(1), pages 53-80, January.
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    3. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    4. Feng Chen & Suren Chen & Xiaoxiang Ma, 2016. "Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models," IJERPH, MDPI, vol. 13(6), pages 1-16, June.
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    Cited by:

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