IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v547y2020ics0378437119321430.html
   My bibliography  Save this article

Crash prediction based on random effect negative binomial model considering data heterogeneity

Author

Listed:
  • Yan, Ying
  • Zhang, Ying
  • Yang, Xiangli
  • Hu, Jin
  • Tang, Jinjun
  • Guo, Zhongyin

Abstract

In order to improve traffic safety, a large amount of works focusing on crash prediction and identifying factors contributing to crashes. However, the ignorance of data unobserved heterogeneity in some traditional models may lead to biased parameter estimation and erroneous inferences. To investigate the relationship between crash and the potential contributing factors, the crash data occurred in 3-year survey period on Interstate highways in Washington, including 134 fatal crashes, 13936 injury crashes, and 34,084 property damage only (PDO) crashes were collected. A data quality control method based on sensitivity analysis is used to determine the road segments. Then a negative binomial (NB) model and a random negative binomial (RENB) model are constructed to predict crash number. The inverse stepwise procedure was applied to examine the significance of explanatory variable. The horizontal alignment type, speed limit, visibility, road surface condition, and AADT are identified as significant factors on the crash. In the comparison, four standard errors are designed as indicators, and the results show that the errors of RENB model are lower than that of NB model. The comparing results illustrate that the RENB model outperforms the NB model in crash number prediction and safety service level prediction

Suggested Citation

  • Yan, Ying & Zhang, Ying & Yang, Xiangli & Hu, Jin & Tang, Jinjun & Guo, Zhongyin, 2020. "Crash prediction based on random effect negative binomial model considering data heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    • Handle: RePEc:eee:phsmap:v:547:y:2020:i:c:s0378437119321430
    DOI: 10.1016/j.physa.2019.123858
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119321430
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.123858?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yajie Zou & Xinzhi Zhong & Jinjun Tang & Xin Ye & Lingtao Wu & Muhammad Ijaz & Yinhai Wang, 2019. "A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis," Sustainability, MDPI, vol. 11(2), pages 1-13, January.
    2. Zong, Fang & Yu, Ping & Tang, Jinjun & Sun, Xiao, 2019. "Understanding parking decisions with structural equation modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 408-417.
    3. Xiong, Yingge & Tobias, Justin L. & Mannering, Fred L., 2014. "The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 109-128.
    4. Yan, Ying & Zhang, Shen & Tang, Jinjun & Wang, Xiaofei, 2017. "Understanding characteristics in multivariate traffic flow time series from complex network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 477(C), pages 149-160.
    5. Tang, Jinjun & Chen, Xinqiang & Hu, Zheng & Zong, Fang & Han, Chunyang & Li, Leixiao, 2019. "Traffic flow prediction based on combination of support vector machine and data denoising schemes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    6. Buddhavarapu, Prasad & Scott, James G. & Prozzi, Jorge A., 2016. "Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 492-510.
    7. 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.
    8. Xin Ye & Ke Wang & Yajie Zou & Dominique Lord, 2018. "A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-17, May.
    9. Xiong, Yingge & Mannering, Fred L., 2013. "The heterogeneous effects of guardian supervision on adolescent driver-injury severities: A finite-mixture random-parameters approach," Transportation Research Part B: Methodological, Elsevier, vol. 49(C), pages 39-54.
    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. Mehzabin Tuli, Farzana & Mitra, Suman & Crews, Mariah B., 2021. "Factors influencing the usage of shared E-scooters in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 164-185.
    2. Sai Chand & Emily Moylan & S. Travis Waller & Vinayak Dixit, 2020. "Analysis of Vehicle Breakdown Frequency: A Case Study of New South Wales, Australia," Sustainability, MDPI, vol. 12(19), pages 1-14, October.
    3. Shahriar Mohammadi & Karim Ismail & Amir H. Ghods, 2021. "Investigating Wi-Fi, Bluetooth, and Bluetooth Low-Energy Signal Characteristics for Integration in Vehicle–Pedestrian Collision Warning Systems," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
    4. Cao, Jieyu & Chen, Junlan & Guo, Xiucheng & Wang, Ling, 2023. "Trajectory data-based severe conflict prediction for expressways under different traffic states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    5. Lu, Qing-Chang & Xu, Peng-Cheng & Zhang, Jingxiao, 2021. "Infrastructure-based transportation network vulnerability modeling and analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).

    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. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    2. Du, Zhouyang & Tang, Jinjun & Qi, Yong & Wang, Yiwei & Han, Chunyang & Yang, Yifan, 2020. "Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    3. Tang, Jinjun & Hu, Jin & Hao, Wei & Chen, Xinqiang & Qi, Yong, 2020. "Markov Chains based route travel time estimation considering link spatio-temporal correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    4. Junjie Fu & Xinqiang Chen & Shubo Wu & Chaojian Shi & Huafeng Wu & Jiansen Zhao & Pengwen Xiong, 2020. "Mining ship deficiency correlations from historical port state control (PSC) inspection data," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-19, February.
    5. Dong, Chunjiao & Shao, Chunfu & Clarke, David B. & Nambisan, Shashi S., 2018. "An innovative approach for traffic crash estimation and prediction on accommodating unobserved heterogeneities," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 407-428.
    6. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    7. Sun, Chenshuo & Pei, Xin & Hao, Junheng & Wang, Yewen & Zhang, Zuo & Wong, S.C., 2018. "Role of road network features in the evaluation of incident impacts on urban traffic mobility," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 101-116.
    8. Yajie Zou & Xinzhi Zhong & Jinjun Tang & Xin Ye & Lingtao Wu & Muhammad Ijaz & Yinhai Wang, 2019. "A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis," Sustainability, MDPI, vol. 11(2), pages 1-13, January.
    9. Ping Zhang & Chenzhu Wang & Fei Chen & Suping Cui & Jianchuan Cheng & Wu Bo, 2022. "A Random-Parameter Negative Binomial Model for Assessing Freeway Crash Frequency by Injury Severity: Daytime versus Nighttime," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    10. Tang, Jinjun & Zhang, Xinshao & Yu, Tianjian & Liu, Fang, 2021. "Missing traffic data imputation considering approximate intervals: A hybrid structure integrating adaptive network-based inference and fuzzy rough set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    11. Jingjing Xu & Behram Wali & Xiaobing Li & Jiaqi Yang, 2019. "Injury Severity and Contributing Driver Actions in Passenger Vehicle–Truck Collisions," IJERPH, MDPI, vol. 16(19), pages 1-16, September.
    12. Qian Duan & Xin Ye & Jian Li & Ke Wang, 2020. "Empirical Modeling Analysis of Potential Commute Demand for Carsharing in Shanghai, China," Sustainability, MDPI, vol. 12(2), pages 1-18, January.
    13. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    14. Behram Wali & Asad Khattak & Thomas Karnowski, 2020. "The relationship between driving volatility in time to collision and crash injury severity in a naturalistic driving environment," Papers 2010.04719, arXiv.org.
    15. Yichuan Peng & Yuming Jiang & Jian Lu & Yajie Zou, 2018. "Examining the effect of adverse weather on road transportation using weather and traffic sensors," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-14, October.
    16. Sai Chand & Zhuolin Li & Abdulmajeed Alsultan & Vinayak V. Dixit, 2022. "Comparing and Contrasting the Impacts of Macro-Level Factors on Crash Duration and Frequency," IJERPH, MDPI, vol. 19(9), pages 1-19, May.
    17. Xin Guan & Xin Ye & Cheng Shi & Yajie Zou, 2019. "A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China," Sustainability, MDPI, vol. 11(20), pages 1-19, October.
    18. Jibiao Zhou & Xinhua Mao & Yiting Wang & Minjie Zhang & Sheng Dong, 2019. "Risk Assessment in Urban Large-Scale Public Spaces Using Dempster-Shafer Theory: An Empirical Study in Ningbo, China," IJERPH, MDPI, vol. 16(16), pages 1-28, August.
    19. Laura Eboli & Carmen Forciniti, 2020. "The Severity of Traffic Crashes in Italy: An Explorative Analysis among Different Driving Circumstances," Sustainability, MDPI, vol. 12(3), pages 1-19, January.
    20. Medury, Aditya PhD & Vlachogiannis, Dimitris & Grembek, Offer PhD, 2020. "Assessing the Variation of Curbside Safety at the City Block Level," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt46n9669d, Institute of Transportation Studies, UC Berkeley.

    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:eee:phsmap:v:547:y:2020:i:c:s0378437119321430. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.