IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v94y2018i3d10.1007_s11069-018-3475-9.html
   My bibliography  Save this article

Spatial and factor analysis of vehicle crashes in Mississippi state

Author

Listed:
  • Lei Bu

    (Jackson State University)

  • Feng Wang

    (Texas State University)

  • Haitao Gong

    (Jackson State University)

Abstract

Traffic crash data from 2010 to 2014 were collected by Mississippi Department of Transportation (MDOT) and extracted for the study. Spatial and factor analysis was conducted including geographic distribution of crashes, descriptive statistics of crash data, and probability analysis of crash factors. Geographic Information System was applied for spatial analysis to show the historical crash data statewide distribution, crash distributions on primary and secondary road segments in the public road system, and crash distribution in MDOT maintenance districts. The results show a similar distribution pattern in the three crash severities in Mississippi as in other states. It also shows that large numbers of the crashes happened on specific locations and there are high crash frequencies on highway segments in metropolitan area and urban area. Based on the spatial analysis results, three comparison scenarios were investigated to estimate the probability of each possible causing factor to the crash severity level. The Type III analysis of variance approach was adopted to assess the significance level of each crash factor, and the multinomial logit model approach with maximum likelihood estimate was applied to conduct the probability analysis and evaluate the significance of each crash factor. The strategies that may potentially decrease the crash frequencies at crash severity levels were discussed based on the probability analysis results.

Suggested Citation

  • Lei Bu & Feng Wang & Haitao Gong, 2018. "Spatial and factor analysis of vehicle crashes in Mississippi state," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(3), pages 1255-1276, December.
    • Handle: RePEc:spr:nathaz:v:94:y:2018:i:3:d:10.1007_s11069-018-3475-9
    DOI: 10.1007/s11069-018-3475-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-018-3475-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-018-3475-9?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. 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.
    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. Ju He & Yunxiao Dang & Wenzhong Zhang & Li Chen, 2020. "Perception of Urban Public Safety of Floating Population with Higher Education Background: Evidence from Urban China," IJERPH, MDPI, vol. 17(22), pages 1-16, November.

    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. Najaf, Pooya & Thill, Jean-Claude & Zhang, Wenjia & Fields, Milton Greg, 2018. "City-level urban form and traffic safety: A structural equation modeling analysis of direct and indirect effects," Journal of Transport Geography, Elsevier, vol. 69(C), pages 257-270.
    2. 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.
    3. Bo Yang & Yao Wu & Weihua Zhang & Jie Bao, 2020. "Modeling Collision Probability on Freeway: Accounting for Different Types and Severities in Various LOS," Sustainability, MDPI, vol. 12(18), pages 1-13, September.
    4. 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.
    5. Lv, Jinpeng & Lord, Dominique & Zhang, Yunlong & Chen, Zhi, 2015. "Investigating Peltzman effects in adopting mandatory seat belt laws in the US: Evidence from non-occupant fatalities," Transport Policy, Elsevier, vol. 44(C), pages 58-64.
    6. Ye, Wei & Xu, Yueru & Shi, Xiaomeng & Shiwakoti, Nirajan & Ye, Zhirui & Zheng, Yuan, 2024. "A macroscopic safety indicator for road segment: application of entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    7. Ruru Xing & Zimu Li & Xiaoyu Cai & Zepeng Yang & Ningning Zhang & Tao Yang, 2023. "Accident Rate Prediction Model for Urban Expressway Underwater Tunnel," Sustainability, MDPI, vol. 15(13), pages 1-28, July.
    8. Ulak, Mehmet Baran & Ozguven, Eren Erman & Spainhour, Lisa & Vanli, Omer Arda, 2017. "Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida," Journal of Transport Geography, Elsevier, vol. 58(C), pages 71-91.
    9. Milhan Moomen & Amirarsalan Mehrara Molan & Khaled Ksaibati, 2023. "A Random Parameters Multinomial Logit Model Analysis of Median Barrier Crash Injury Severity on Wyoming Interstates," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    10. 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).
    11. Hwachyi Wang & S. K. Jason Chang & Hans De Backer & Dirk Lauwers & Philippe De Maeyer, 2019. "Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)," Sustainability, MDPI, vol. 11(13), pages 1-28, July.
    12. 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.
    13. Buckley, Cathal & Howley, Peter & Jordan, Phil, 2015. "The role of differing farming motivations on the adoption of nutrient management practices," International Journal of Agricultural Management, Institute of Agricultural Management, vol. 4(4), July.
    14. Kojiro Matsuo & Kosuke Miyazaki & Nao Sugiki, 2022. "A Method for Locational Risk Estimation of Vehicle–Children Accidents Considering Children’s Travel Purposes," IJERPH, MDPI, vol. 19(21), pages 1-16, October.
    15. Hana Naghawi, 2018. "Negative Binomial Regression Model for Road Crash Severity Prediction," Modern Applied Science, Canadian Center of Science and Education, vol. 12(4), pages 1-38, April.
    16. Rose Luke, 2023. "Current and Future Trends in Driver Behaviour and Traffic Safety Scholarship: An African Research Agenda," IJERPH, MDPI, vol. 20(5), pages 1-23, February.
    17. Guilong Xu & Jinliang Xu & Chao Gao & Rishuang Sun & Huagang Shan & Yongji Ma & Jinsong Ran, 2022. "A Novel Safety Assessment Framework for Pavement Friction Evolution Due to Traffic on Horizontal Curves," Sustainability, MDPI, vol. 14(17), pages 1-14, August.
    18. Chen Chen & Feng Guo, 2016. "Evaluating the influence of crashes on driving risk using recurrent event models and Naturalistic Driving Study data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(12), pages 2225-2238, September.
    19. 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.
    20. Dongkwan Lee & Jean-Michel Guldmann & Burkhard von Rabenau, 2018. "Interactions between the built and socio-economic environment and driver demographics: spatial econometric models of car crashes in the Columbus Metropolitan Area," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 22(1), pages 17-37, January.

    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:spr:nathaz:v:94:y:2018:i:3:d:10.1007_s11069-018-3475-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.