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Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand

Author

Listed:
  • Nopadon Kronprasert

    (Excellence Center in Infrastructure Technology and Transportation Engineering (ExCITE), Chiang Mai University, Chiang Mai 50200, Thailand
    Department of Civil Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Katesirint Boontan

    (Excellence Center in Infrastructure Technology and Transportation Engineering (ExCITE), Chiang Mai University, Chiang Mai 50200, Thailand)

  • Patipat Kanha

    (Excellence Center in Infrastructure Technology and Transportation Engineering (ExCITE), Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

The number of road crashes continues to rise significantly in Thailand. Curve segments on two-lane rural roads are among the most hazardous locations which lead to road crashes and tremendous economic losses; therefore, a detailed examination of its risk is required. This study aims to develop crash prediction models using Safety Performance Functions (SPFs) as a tool to identify the relationship among road alignment, road geometric and traffic conditions, and crash frequency for two-lane rural horizontal curve segments. Relevant data associated with 86,599 curve segments on two-lane rural road networks in Thailand were collected including road alignment data from a GPS vehicle tracking technology, road attribute data from rural road asset databases, and historical crash data from crash reports. Safety Performance Functions (SPFs) for horizontal curve segments were developed, using Poisson regression, negative binomial regression, and calibrated Highway Safety Manual models. The results showed that the most significant parameter affecting crash frequency is lane width, followed by curve length, traffic volume, curve radius, and types of curves (i.e., circular curves, compound curves, reverse curves, and broken-back curves). Comparing among crash prediction models developed, the calibrated Highway Safety Manual SPF outperforms the others in prediction accuracy.

Suggested Citation

  • Nopadon Kronprasert & Katesirint Boontan & Patipat Kanha, 2021. "Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9011-:d:612904
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    References listed on IDEAS

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    1. Lian Zhu & Linjun Lu & Wenying Zhang & Yurou Zhao & Meining Song, 2019. "Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks," Sustainability, MDPI, vol. 11(8), pages 1-17, April.
    2. Shively, Thomas S. & Kockelman, Kara & Damien, Paul, 2010. "A Bayesian semi-parametric model to estimate relationships between crash counts and roadway characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 699-715, June.
    3. Tasneem Miqdady & Juan de Oña, 2020. "Identifying the Factors That Increase the Probability of an Injury or Fatal Traffic Crash in an Urban Context in Jordan," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
    4. Longyu Shi & Nigar Huseynova & Bin Yang & Chunming Li & Lijie Gao, 2018. "A Cask Evaluation Model to Assess Safety in Chinese Rural Roads," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
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    Cited by:

    1. Olga Beatriz Barbosa Mendes & Ana Paula Camargo Larocca & Karla Rodrigues Silva & Ali Pirdavani, 2023. "Assessing the Performance of Highway Safety Manual (HSM) Predictive Models for Brazilian Multilane Highways," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    2. Yichi Zhang & Xuan Dou & Hanping Zhao & Ying Xue & Jinfan Liang, 2023. "Safety Risk Assessment of Low-Volume Road Segments on the Tibetan Plateau Using UAV LiDAR Data," Sustainability, MDPI, vol. 15(14), pages 1-19, July.

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