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Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm

Author

Listed:
  • Giuseppe Guido

    (Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende (CS), Italy)

  • Sina Shaffiee Haghshenas

    (Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende (CS), Italy)

  • Sami Shaffiee Haghshenas

    (Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende (CS), Italy)

  • Alessandro Vitale

    (Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende (CS), Italy)

  • Vincenzo Gallelli

    (Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende (CS), Italy)

  • Vittorio Astarita

    (Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende (CS), Italy)

Abstract

Evaluating road safety is an enduring research topic in Infrastructure and Transportation Engineering. The prediction of crash risk is very important for avoiding other crashes and safeguarding road users. According to this task, awareness of the number of vehicles involved in an accident contributes greatly to safety analysis, hence, it is necessary to predict it. In this study, the main aim is to develop a binary model for predicting the number of vehicles involved in an accident using Neural Networks and the Group Method of Data Handling (GMDH). For this purpose, 775 accident cases were accurately recorded and evaluated from the urban and rural areas of Cosenza in southern Italy and some notable parameters were considered as input data including Daylight, Weekday, Type of accident, Location, Speed limit and Average speed; and the number of vehicles involved in an accident was considered as output. In this study, 581 cases were selected randomly from the dataset to train and the rest were used to test the developed binary model. A confusion matrix and a Receiver Operating Characteristic curve were used to investigate the performance of the proposed model. According to the obtained results, the accuracy values of the prediction model were 83.5% and 85.7% for testing and training, respectively. Finally, it can be concluded that the developed binary model can be applied as a reliable tool for predicting the number of vehicles involved in an accident.

Suggested Citation

  • Giuseppe Guido & Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Alessandro Vitale & Vincenzo Gallelli & Vittorio Astarita, 2020. "Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6735-:d:401412
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    References listed on IDEAS

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    1. Zong Woo Geem & Sung Yong Chung & Jin-Hong Kim, 2018. "Improved Optimization for Wastewater Treatment and Reuse System Using Computational Intelligence," Complexity, Hindawi, vol. 2018, pages 1-8, April.
    2. Reza Mikaeil & Sina Shaffiee Haghshenas & Zoheir Sedaghati, 2019. "Geotechnical risk evaluation of tunneling projects using optimization techniques (case study: the second part of Emamzade Hashem tunnel)," 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. 97(3), pages 1099-1113, July.
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