Predicting Passenger Car Drivers' Behavior when Encountering Traffic Jams Using a Neural Network Model

Document Type : Original Article

Authors
Seyed Hamed Seyed Matin 1
Ali Abdi kordani 2
1 Ph.D., Candidate, Department of Civil Engineering, Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran.
2 Professor, Department of Civil Engineering, Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran.
10.22034/road.2024.435500.2239
Abstract
The imbalance of supply and demand during rush hours causes traffic congestion on roads, which has a significant impact on safety, travel time, and fuel consumption. Investigating the behavior of drivers before congestion and traffic jams can help to reduce its effects while providing suitable solutions. Although the prediction of drivers' behavior using different models has been presented by researchers, the prediction of drivers' behavior before traffic jams has not been paid much attention. The main purpose of this study is to determine the model for predicting the behavior of drivers when they encounter traffic jams using a neural network model. In this research, the behavior of 124 drivers was processed by recording video films without attracting drivers' attention, and the demographic and behavioral characteristics of the drivers were extracted from the analysis of the questionnaires completed by the drivers. After descriptive analysis, the collected data were evaluated using confirmatory factor analysis of the relationships between the observed and latent (attitudinal or behavioral) variables in Amos v.24 software, and two latent variables of law evasion and aggressive driving were determined. According to the output of the neural network model that was formed in SPSS software, the most important in predicting the behavior of drivers to lane changes when encountering traffic jams is the attitudinal variable of law evasion and then the transverse distance to obstacles or cars on the left and right side of the target car. The accuracy of the model for the test data is equal to 93.9%.
Keywords
Driver Behavior
Congestion
Traffic Jams
Artificial Neural Network
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