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Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models

Author

Listed:
  • Feng Chen

    (Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China)

  • Suren Chen

    (Department of Civil & Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA)

  • Xiaoxiang Ma

    (Department of Civil & Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA)

Abstract

Traffic and environmental conditions (e.g., weather conditions), which frequently change with time, have a significant impact on crash occurrence. Traditional crash frequency models with large temporal scales and aggregated variables are not sufficient to capture the time-varying nature of driving environmental factors, causing significant loss of critical information on crash frequency modeling. This paper aims at developing crash frequency models with refined temporal scales for complex driving environments, with such an effort providing more detailed and accurate crash risk information which can allow for more effective and proactive traffic management and law enforcement intervention. Zero-inflated, negative binomial (ZINB) models with site-specific random effects are developed with unbalanced panel data to analyze hourly crash frequency on highway segments. The real-time driving environment information, including traffic, weather and road surface condition data, sourced primarily from the Road Weather Information System, is incorporated into the models along with site-specific road characteristics. The estimation results of unbalanced panel data ZINB models suggest there are a number of factors influencing crash frequency, including time-varying factors (e.g., visibility and hourly traffic volume) and site-varying factors (e.g., speed limit). The study confirms the unique significance of the real-time weather, road surface condition and traffic data to crash frequency modeling.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:6:p:609-:d:72283
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    References listed on IDEAS

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    Cited by:

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    2. Sheng Dong & Afaq Khattak & Irfan Ullah & Jibiao Zhou & Arshad Hussain, 2022. "Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations," IJERPH, MDPI, vol. 19(5), pages 1-23, March.
    3. Chen Wang & Lin Liu & Chengcheng Xu & Weitao Lv, 2019. "Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework," IJERPH, MDPI, vol. 16(3), pages 1-18, January.
    4. Qiang Zeng & Wei Hao & Jaeyoung Lee & Feng Chen, 2020. "Investigating the Impacts of Real-Time Weather Conditions on Freeway Crash Severity: A Bayesian Spatial Analysis," IJERPH, MDPI, vol. 17(8), pages 1-15, April.
    5. Ming Lv & Xiaojun Shao & Chimou Li & Feng Chen, 2022. "Driving Performance Evaluation of Shuttle Buses: A Case Study of Hong Kong–Zhuhai–Macau Bridge," IJERPH, MDPI, vol. 19(3), pages 1-13, January.
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    7. Huiying Wen & Xuan Zhang & Qiang Zeng & Jaeyoung Lee & Quan Yuan, 2019. "Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data," IJERPH, MDPI, vol. 16(2), pages 1-12, January.
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    9. 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.

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