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Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

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  • Vahid Najafi Moghaddam Gilani
  • Seyed Mohsen Hosseinian
  • Meisam Ghasedi
  • Mohammad Nikookar

Abstract

Modeling the severity of accidents based on the most effective variables accounts for developing a high-precision model presenting the possibility of occurrence of each category of future accidents, and it could be utilized to prioritize the corrective measures for authorities. The purpose of this study is to identify the variables affecting the severity of the injury, fatal, and property damage only (PDO) accidents in Rasht city by collecting information on urban accidents from March 2019 to March 2020. In this regard, the multiple logistic regression and the pattern recognition type of artificial neural network (ANN) as a machine learning solution are used to recognize the most influential variables on the severity of accidents and the superior approach for accident prediction. Results show that the multiple logistic regression in the forward stepwise method has R 2 of 0.854 and an accuracy prediction power of 89.17%. It turns out that the accidents occurred between 18 and 24 and KIA Pride vehicle has the highest effect on increasing the severity of accidents, respectively. The most important result of the logit model accentuates the role of environmental variables, including poor lighting conditions alongside unfavorable weather and the dominant role of unsafe and poor quality of vehicles on increasing the severity of accidents. In addition, the machine learning model performs significantly better and has higher prediction accuracy (98.9%) than the logit model. In addition, the ANN model’s greater power to predict and estimate future accidents is confirmed through performance and sensitivity analysis.

Suggested Citation

  • Vahid Najafi Moghaddam Gilani & Seyed Mohsen Hosseinian & Meisam Ghasedi & Mohammad Nikookar, 2021. "Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, May.
  • Handle: RePEc:hin:jnlmpe:9974219
    DOI: 10.1155/2021/9974219
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    Cited by:

    1. Te Ma & Mahdi Aghaabbasi & Mujahid Ali & Rosilawati Zainol & Amin Jan & Abdeliazim Mustafa Mohamed & Abdullah Mohamed, 2022. "Nonlinear Relationships between Vehicle Ownership and Household Travel Characteristics and Built Environment Attributes in the US Using the XGBT Algorithm," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    2. Huacai Xian & Yu Wang & Yujia Hou & Shunzhong Dong & Junying Kou & Huili Zeng, 2022. "Research on Influencing Factors of Urban Road Traffic Casualties through Support Vector Machine," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    3. Katsunori Tanaka & Yasuki Motozawa & Kentaro Takahashi & Tetsuo Maki & Mami Nakamura & Masahito Hitosugi, 2022. "Severity of Placental Abruption in Restrained Pregnant Vehicle Drivers: Correct Seat Belt Use Confirmed by Finite Element Model Analysis," IJERPH, MDPI, vol. 19(21), pages 1-12, October.
    4. Saeid Pourroostaei Ardakani & Xiangning Liang & Kal Tenna Mengistu & Richard Sugianto So & Xuhui Wei & Baojie He & Ali Cheshmehzangi, 2023. "Road Car Accident Prediction Using a Machine-Learning-Enabled Data Analysis," Sustainability, MDPI, vol. 15(7), pages 1-15, March.
    5. Nasrin, Sharmin & Bunker, Jonathan, 2024. "Gender equality through sustainable transport policy," Transport Policy, Elsevier, vol. 149(C), pages 59-79.

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