IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i12p2722-d187358.html
   My bibliography  Save this article

Influence Factors on Injury Severity of Traffic Accidents and Differences in Urban Functional Zones: The Empirical Analysis of Beijing

Author

Listed:
  • Zhiyuan Sun

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Jianyu Wang

    (Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China)

  • Yanyan Chen

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Huapu Lu

    (Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China)

Abstract

The objective of this study was to identify influence factors on injury severity of traffic accidents and discuss the differences in urban functional zones in Beijing. A total of 3982 sets of accident data in Beijing were analyzed from the perspective of whole city and different urban functional zones. From the aspects of accident attribute, occurrence time, infrastructure, management status, and environmental condition, the influence factors set of injury severity of traffic accidents in Beijing are set up in this paper, which include 17 influence factors. Based on Pearson’s chi-squared test, factors are preselected. On the basis of binary logistic regression analysis, the impact of the value of influence factors on injury severity of traffic accidents is calibrated. Based on classification and regression tree analysis, the impact of influence factors is analyzed. Through Pearson’s chi-squared test and binary logistic regression analysis, it is found that there are similarities and differences among different urban functional zones. There are two common influence factors, including accident type and cross-section position, and six personalized influence factors, including lighting conditions, visibility, signal control, road physical isolation facility, occurrence period and road type, and the other nine weak influence factors. The results of binary logistic regression analysis and classification and regression tree analysis are basically the same. The factors that should be paid attention to in different urban functional zones and the value of the factors that need special attention are determined by synthesizing two methods.

Suggested Citation

  • Zhiyuan Sun & Jianyu Wang & Yanyan Chen & Huapu Lu, 2018. "Influence Factors on Injury Severity of Traffic Accidents and Differences in Urban Functional Zones: The Empirical Analysis of Beijing," IJERPH, MDPI, vol. 15(12), pages 1-16, December.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:12:p:2722-:d:187358
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/12/2722/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/12/2722/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yip, Tsz Leung, 2008. "Port traffic risks - A study of accidents in Hong Kong waters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(5), pages 921-931, September.
    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. Alireza Mohammadi & Behzad Kiani & Hassan Mahmoudzadeh & Robert Bergquist, 2023. "Pedestrian Road Traffic Accidents in Metropolitan Areas: GIS-Based Prediction Modelling of Cases in Mashhad, Iran," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    2. Seung-Hoon Park & Min-Kyung Bae, 2020. "Exploring the Determinants of the Severity of Pedestrian Injuries by Pedestrian Age: A Case Study of Daegu Metropolitan City, South Korea," IJERPH, MDPI, vol. 17(7), pages 1-16, March.
    3. Fanyu Wang & Junyou Zhang & Shufeng Wang & Sixian Li & Wenlan Hou, 2020. "Analysis of Driving Behavior Based on Dynamic Changes of Personality States," IJERPH, MDPI, vol. 17(2), pages 1-17, January.
    4. Jianyu Wang & Huapu Lu & Zhiyuan Sun & Tianshi Wang, 2020. "Exploring Factors Influencing Injury Severity of Vehicle At-Fault Accidents: A Comparative Analysis of Passenger and Freight Vehicles," IJERPH, MDPI, vol. 17(4), pages 1-12, February.
    5. Yuhuan Zhang & Huapu Lu & Wencong Qu, 2020. "Geographical Detection of Traffic Accidents Spatial Stratified Heterogeneity and Influence Factors," IJERPH, MDPI, vol. 17(2), pages 1-17, January.
    6. Chin-Chuan Shih & Chi-Jie Lu & Gin-Den Chen & Chi-Chang Chang, 2020. "Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals," IJERPH, MDPI, vol. 17(14), pages 1-11, July.
    7. Chi-Chang Chang & Chun-Chia Chen & Chalong Cheewakriangkrai & Ying Chen Chen & Shun-Fa Yang, 2021. "Risk Prediction of Second Primary Endometrial Cancer in Obese Women: A Hospital-Based Cancer Registry Study," IJERPH, MDPI, vol. 18(17), pages 1-9, August.

    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. Bing Wu & Huibin Tian & Xinping Yan & C. Guedes Soares, 2020. "A probabilistic consequence estimation model for collision accidents in the downstream of Yangtze River using Bayesian Networks," Journal of Risk and Reliability, , vol. 234(2), pages 422-436, April.
    2. Jomon Aliyas Paul & Leo MacDonald, 2017. "An empirical analysis of US vessel-related port accidents (2002–2012): Impact of union membership and port efficiency on accident incidence and economic damage," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(4), pages 723-748, December.
    3. Du, Lei & Goerlandt, Floris & Kujala, Pentti, 2020. "Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    4. Wang, Huanxin & Liu, Zhengjiang & Wang, Xinjian & Graham, Tony & Wang, Jin, 2021. "An analysis of factors affecting the severity of marine accidents," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    5. Zhou, Kaiwen & Xing, Wenbin & Wang, Jingbo & Li, Huanhuan & Yang, Zaili, 2024. "A data-driven risk model for maritime casualty analysis: A global perspective," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    6. Wencheng Huang & Yueyang Li & Xingyi Kou & Wenzhe Wang & Yifei Xu, 2021. "Using a FMEA–TIFIAD Approach to Identify the Risk of Railway Dangerous Goods Transportation System," Group Decision and Negotiation, Springer, vol. 30(1), pages 63-95, February.
    7. Gino J. Lim & Jaeyoung Cho & Selim Bora & Taofeek Biobaku & Hamid Parsaei, 2018. "Models and computational algorithms for maritime risk analysis: a review," Annals of Operations Research, Springer, vol. 271(2), pages 765-786, December.
    8. Bye, Rolf J. & Aalberg, Asbjørn L., 2018. "Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 174-186.
    9. Liye Zhang & Hua Wang & Qiang Meng & Hongbin Xie, 2019. "Ship accident consequences and contributing factors analyses using ship accident investigation reports," Journal of Risk and Reliability, , vol. 233(1), pages 35-47, February.
    10. Cao, Xinhu & Lam, Jasmine Siu Lee, 2019. "A fast reaction-based port vulnerability assessment: Case of Tianjin Port explosion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 128(C), pages 11-33.
    11. Jiang, Dan & Wu, Bing & Cheng, Zhiyou & Xue, Jie & van Gelder, P.H.A.J.M., 2021. "Towards a probabilistic model for estimation of grounding accidents in fluctuating backwater zone of the Three Gorges Reservoir," Reliability Engineering and System Safety, Elsevier, vol. 205(C).

    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:jijerp:v:15:y:2018:i:12:p:2722-:d:187358. 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.