IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i16p9011-d612904.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/16/9011/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/16/9011/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Spasoje Mićić & Radoje Vujadinović & Goran Amidžić & Milanko Damjanović & Boško Matović, 2022. "Accident Frequency Prediction Model for Flat Rural Roads in Serbia," Sustainability, MDPI, vol. 14(13), pages 1-14, June.
    2. Parry, Katharina & Hazelton, Martin L., 2013. "Bayesian inference for day-to-day dynamic traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 104-115.
    3. Antoni Wontorczyk & Stanislaw Gaca, 2021. "Study on the Relationship between Drivers’ Personal Characters and Non-Standard Traffic Signs Comprehensibility," IJERPH, MDPI, vol. 18(5), pages 1-19, March.
    4. Zeyang Cheng & Zhenshan Zu & Jian Lu, 2018. "Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections," Sustainability, MDPI, vol. 11(1), pages 1-17, December.
    5. Tianpei Tang & Senlai Zhu & Yuntao Guo & Xizhao Zhou & Yang Cao, 2019. "Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method," IJERPH, MDPI, vol. 16(7), pages 1-17, April.
    6. 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.
    7. Wang, Zhengli & Jiang, Hai, 2019. "Simultaneous correction of the time and location bias associated with a reported crash by exploiting the spatiotemporal evolution of travel speed," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 199-223.
    8. Laura Cáceres & Miguel A. Fernández & Alfonso Gordaliza & Aquilino Molinero, 2021. "Detection of Geometric Risk Factors Affecting Head-On Collisions through Multiple Logistic Regression: Improving Two-Way Rural Road Design via 2+1 Road Adaptation," IJERPH, MDPI, vol. 18(12), pages 1-13, June.
    9. Xiao Zhang & Xiaofeng Hu & Yiping Bai & Jiansong Wu, 2020. "Risk Assessment of Gas Leakage from School Laboratories Based on the Bayesian Network," IJERPH, MDPI, vol. 17(2), pages 1-18, January.
    10. Dongkwan Lee & Jean-Michel Guldmann & Choongik Choi, 2019. "Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    11. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9011-:d:612904. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.