Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Version 1 : Received: 17 December 2021 / Approved: 20 December 2021 / Online: 20 December 2021 (10:37:05 CET)

How to cite: Guido, G.; Shaffiee Haghshenas, S.; Shaffiee Haghshenas, S.; Vitale, A.; Astarita, V.; Park, Y.; Geem, Z.W. Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy. Preprints 2021, 2021120307 (doi: 10.20944/preprints202112.0307.v1). Guido, G.; Shaffiee Haghshenas, S.; Shaffiee Haghshenas, S.; Vitale, A.; Astarita, V.; Park, Y.; Geem, Z.W. Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy. Preprints 2021, 2021120307 (doi: 10.20944/preprints202112.0307.v1).

Abstract

Evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, evaluation and analysis of important contributing factors affecting the number of crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA) are employed for evaluating the number of vehicles involved in the accident based on the seven factors affecting transport safety including the Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS) and Annual average daily traffic (AADT) of rural roads of Cosenza in southern Italy. In this study, 564 data sets of rural areas were investigated and relevant effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation for rural areas. The results obtained demonstrated that "Average speed" has the highest level and "Weekday" has the lowest level of importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

Keywords

Road safety; Safety management; Road transportation; GMDH; GOA-SVM; Machine learning

Subject

ENGINEERING, Civil Engineering

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