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The Identification of Intersection Entrance Accidents Based on Autoencoder

Author

Listed:
  • Yingcui Du

    (Department of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Feng Sun

    (Department of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Fangtong Jiao

    (Department of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Benxing Liu

    (Department of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Xiaoqing Wang

    (Department of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Pengsheng Zhao

    (Department of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

Abstract

Traffic collisions are one of the leading causes of traffic congestion. In the case of urban intersections, traffic accidents can even result in widespread traffic paralysis. To solve this problem, we developed an autoencoder-based model for identifying intersection entrance accidents by analyzing the characteristics of traffic volume. The model uses the standard deviation of the intersection entrance lanes’ traffic volume as an input parameter and identifies intersection entrance accidents by comparing predicted data to actual measured data. In this paper, the detection rate and average detection time are chosen to evaluate the effectiveness of algorithms. The detection rate of the autoencoder model reaches 94.33%, 95.47%, and 81.64% during the morning peak, evening peak, and daylight off-peak periods, respectively. Compared to the support vector machine and the random forest, autoencoder has better performance. It is evident that the research presented in this paper can effectively enhance the detection effect and has a shorter detection time of intersection entrance accidents.

Suggested Citation

  • Yingcui Du & Feng Sun & Fangtong Jiao & Benxing Liu & Xiaoqing Wang & Pengsheng Zhao, 2023. "The Identification of Intersection Entrance Accidents Based on Autoencoder," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8533-:d:1154989
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    References listed on IDEAS

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    1. Rifkat Minnikhanov & Igor Anikin & Aigul Mardanova & Maria Dagaeva & Alisa Makhmutova & Azat Kadyrov, 2022. "Evaluation of the Approach for the Identification of Trajectory Anomalies on CCTV Video from Road Intersections," Mathematics, MDPI, vol. 10(3), pages 1-20, January.
    2. Danish Farooq & Sarbast Moslem & Szabolcs Duleba, 2019. "Evaluation of Driver Behavior Criteria for Evolution of Sustainable Traffic Safety," Sustainability, MDPI, vol. 11(11), pages 1-15, June.
    3. Romanika Okraszewska & Aleksandra Romanowska & Marcin Wołek & Jacek Oskarbski & Krystian Birr & Kazimierz Jamroz, 2018. "Integration of a Multilevel Transport System Model into Sustainable Urban Mobility Planning," Sustainability, MDPI, vol. 10(2), pages 1-20, February.
    4. Kun Wang & Xiaoyuan Feng & Hongbo Li & Yilong Ren, 2022. "Exploring Influential Factors Affecting the Severity of Urban Expressway Collisions: A Study Based on Collision Data," IJERPH, MDPI, vol. 19(14), pages 1-11, July.
    5. Daniel Albalate & Xavier Fageda, 2019. "Congestion, Road Safety, and the Effectiveness of Public Policies in Urban Areas," Sustainability, MDPI, vol. 11(18), pages 1-21, September.
    6. Tongqiang Ding & Lianxin Zhang & Jianfeng Xi & Yingjuan Li & Lili Zheng & Kexin Zhang, 2023. "Bus Fleet Accident Prediction Based on Violation Data: Considering the Binding Nature of Safety Violations and Service Violations," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    7. Xi Zhang & Shouming Qi & Ao Zheng & Ye Luo & Siqi Hao, 2023. "Data-Driven Analysis of Fatal Urban Traffic Accident Characteristics and Safety Enhancement Research," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    8. Nemanja Deretić & Dragan Stanimirović & Mohammed Al Awadh & Nikola Vujanović & Aleksandar Djukić, 2022. "SARIMA Modelling Approach for Forecasting of Traffic Accidents," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    9. Gi-Wook Cha & Won-Hwa Hong & Young-Chan Kim, 2023. "Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
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