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

Abstract

The 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, the evaluation and analysis of important contributing factors affecting the number of vehicles involved in 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. Hence, the number of vehicles involved in an accident is considered to be the output, and the seven factors affecting transport safety, including 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 in Cosenza, southern Italy, are selected as the inputs. In this study, 564 data sets from rural areas were investigated, and the relevant, effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation in rural areas. The results obtained demonstrated that the “Type of accident” has the highest level and “Location” has the lowest 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.

Transport accessibility assessment of rural tourism facilities

“…(1) construction labour costs; (2) material prices; (3) the demand for higher quality roads; (4) market concentration; (5) the ratio of urban to rural roads; and (6) the relative costs of maintenance and repairs. A related important finding of the authors related to our study is the established growing demand for higher-quality roads, which, among other things, positively affects road safety in rural areas [14]. In this way, transport infrastructure safety standards are being translated into rural areas.…”

Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment

“…In recent years, in line with a global goal to reduce road accident casualties, more extensive research and studies have been conducted to increase the level of road safety [69][70][71][72][73][74][75][76]. This target is one of the United Nations Sustainable Development Goals (SDGs) and is connected to wellness and urban living.…”

Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

“…Most importantly, the kernel function takes the dataset and transforms it into the appropriate format. The quality of a category may be influenced by an individual's understanding of how distinct kernel functions are used in related circumstances [129][130]. ( , )…”