Journal of Transportation Research

Journal of Transportation Research

Traffic Volume Prediction Using Machine Learning Methods (Case Study: Kamal Street: Isfahan)

Document Type : Original Article

Authors
Mahsa Latifi 1
Jamshid Maleki 2
1 M.Sc., Student, Department of Geomatics Engineering, Faculty of Civil and Transportation Engineering‌, University of Isfahan‌, Isfahan, Iran.
2 Assistant Professor, Department of Geomatics Engineering, Faculty of Civil and Transportation Engineering‌, University of Isfahan‌, Isfahan, Iran.
10.22034/tri.2025.482957.3288
Abstract
The increasing demand for the use of private vehicles has turned traffic congestion into one of the most critical crises in major cities worldwide. The ability to predict road traffic volume can aid significantly in many traffic management and control programs. However, estimating traffic volume is challenging, as vehicle counting is typically only possible at a limited number of locations equipped with fixed traffic sensors. In this study, routing data from the "Neshan" service has been used to address this challenge. By recording travel time data at different hours of the day, it is possible to estimate the traffic volume of urban road segments. To assess the feasibility of the proposed method, travel time data from Kamal Street, located in Isfahan, was collected for various hours of the day over a period of 23 days. Additionally, machine learning methods, including Random Forest, Extreme Gradient Boosting, Long Short-Term Memory (LSTM) neural networks, and Bidirectional LSTM networks, were employed to predict hourly traffic volume. The Fast Fourier Transform (FFT) was also used to identify the primary frequencies of traffic volume fluctuations and to model and compare with other methods. In the methods used in this study, features such as spatial dependency (total duration of the incoming edges to the street), duration history, temporal features like the hour of the day and the day of the week, the number of incoming edges, and the number of traffic-influencing centers such as schools, banks, hotels, municipal buildings, pharmacies, emergency services, and hospitals, as well as the student population and total population within the street area, were utilized. Among the machine learning and deep learning methods, the random forest method, with a high R² value of 0.93, shows better performance compared to other methods.
Keywords
Hourly Traffic Volume Prediction
Spatial-Temporal Modeling
Machine Learning
Fast Fourier Transform
Subjects
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