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

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.

Assessing the System Vibration of Circular Sawing Machine in Carbonate Rock Sawing Process Using Experimental Study and Machine Learning

“…Artificial intelligence techniques are one of the most popular ways to solve complex problems in industry and economics sectors (Mahdevari et al 2017 Naderpour et al 2019;Zhang and Geem 2019;Kayabekir et al 2020;Daneshvar and Behnood 2020;Guido et al 2020a;Kandiri et al 2020). In recent years, several studies have been conducted on the application of artificial intelligence in engineering problems (Geem and Kim 2018;Mikaeil et al 2018cMikaeil et al , 2019bSalemi et al 2018;Gnawali et al 2019;Park et al 2020;Shaffiee Haghshenas et al 2020;Noori et al 2020;Fiorini Morosini et al 2020;Guido et al 2020b). One of the most efficient methods of artificial intelligence is the imperialist competitive algorithm (ICA), suggested by Atashpaz-Gargari and Lucas (Khabbazi et al 2009;Haghshenas et al 2017).…”

Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various urban facilities

“…Although there have been various studies on the behavior of pedestrians on issues such as sidewalks, design of walking systems, and safety of pedestrians, less attention has been paid to concepts including speed, density, flow rate for passing through and outside the crosswalk, and so on. Moreover, various researches have been performed by the use of group method of data handling (GMDH) and genetic programming (GP) methods in many engineering fields [19][20][21][22][23][24]. However, no studies have been done on the use of prediction models (for instance, GP and GMDH) to represent pedestrian density.…”

Prediction of Rockburst Intensity Grade in Deep Underground Excavation Using Adaptive Boosting Classifier

“…Machine learning (ML) algorithms have been widely used to tackle real-world problems in the last ten years, particularly in civil engineering. ML algorithms have been successfully used to a variety of real situations, paving the way for several promising opportunities in civil engineering and other domains such as environmental [6], geotechnical and geological [7][8][9][10][11][12][13][14][15][16][17][18][19][20], and other sciences [21][22][23][24] including rockburst hazards prediction [25][26][27]. Furthermore, a variety of machine learning methods have been used, for example, Support Vector Machine (SVM) [28], Artificial Neural Networks (ANNs) [29], Distance Discriminant Analysis (DDA) [30], Bayes Discriminant Analysis (BDA) [31], and Fisher Linear Discriminant Analysis (LDA) [32], and some systems are based upon hybrid (Zhou et al [33]; Adoko et al [34]; Liu et al [35]) or ensemble (Ge and Feng [36]; Dong et al [37]) analyzing long-term prediction of rockburst.…”