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Sustainability
Volume 12
Issue 17
10.3390/su12176735
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Article

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

by
Giuseppe Guido
*,
Sina Shaffiee Haghshenas
,
Sami Shaffiee Haghshenas
,
Alessandro Vitale
,
Vincenzo Gallelli
and
Vittorio Astarita
Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende (CS), Italy
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(17), 6735; https://doi.org/10.3390/su12176735
Submission received: 31 July 2020 / Revised: 13 August 2020 / Accepted: 17 August 2020 / Published: 20 August 2020
(This article belongs to the Special Issue Algorithms, Models and New Technologies for Sustainable Traffic Management and Safety)
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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.
Keywords: road safety; transportation system; neural network; GMDH; binary model road safety; transportation system; neural network; GMDH; binary model

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MDPI and ACS Style

Guido, G.; Haghshenas, S.S.; Haghshenas, S.S.; Vitale, A.; Gallelli, V.; Astarita, V. Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm. Sustainability 2020, 12, 6735. https://doi.org/10.3390/su12176735

AMA Style

Guido G, Haghshenas SS, Haghshenas SS, Vitale A, Gallelli V, Astarita V. Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm. Sustainability. 2020; 12(17):6735. https://doi.org/10.3390/su12176735

Chicago/Turabian Style

Guido, Giuseppe, Sina Shaffiee Haghshenas, Sami Shaffiee Haghshenas, Alessandro Vitale, Vincenzo Gallelli, and Vittorio Astarita. 2020. "Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm" Sustainability 12, no. 17: 6735. https://doi.org/10.3390/su12176735

APA Style

Guido, G., Haghshenas, S. S., Haghshenas, S. S., Vitale, A., Gallelli, V., & Astarita, V. (2020). Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm. Sustainability, 12(17), 6735. https://doi.org/10.3390/su12176735

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