A Prediction of Vehicles Entering The Traffic Cordons, Using Machine Learning (Case: Tehran City)

Document Type : Original Article

Authors
MahmoudReza Nateghi 1
Sepideh Shami 2
Amir Reza Mamdoohi 3
1 M.Sc., Student, Faculty of Civil & Environmental Engineering, Tarbiat Modares University, Tehran, Iran.
2 Ph.D., Student, Faculty of Civil & Environmental Engineering, Tarbiat Modares University, Tehran, Iran.
3 Associate Professor, Faculty of Civil & Environmental Engineering, Tarbiat Modares University, Tehran, Iran; and Adjunct Professor, Department of Civil, Geological & Mining Eng., Polytechnique Montréal, Montréal, Canada.
10.22034/road.2024.471470.2305
Abstract
Predicting the number of vehicles is crucial in intelligent transportation systems and is a key component of traffic management. Effective vehicle management significantly improves the efficiency of urban road networks and reduces congestion. This study utilizes vehicle entry data for Tehran's traffic cordons from 2017 to 2021 and employs three machine learning methods (Random Forest, XGBoost, and K-Nearest Neighbors) to predict the demand for incoming vehicles in each traffic cordon (Traffic Plan and Air Pollution Control Plan) on an hourly basis. In addition to conventional variables such as weather data and time, travel demand management policies are also used as predictor variables. Sensitivity analysis is conducted to evaluate the impact of different policies on predicting traffic volume. The results indicate that the XGBoost model and the Random Forest model, with a mean absolute percentage error (MAPE) of 8.2% and 11.16%, respectively, for traffic and air pollution cordons, perform better compared to the K-Nearest Neighbors model. Sensitivity analysis of variables related to travel demand management policies shows that the policies of "online schooling" and "tolls for entering the traffic and air pollution cordons" have the greatest impact, while the policy of "inter-provincial travel restrictions" has the least impact on improving prediction accuracy.
Keywords
Prediction
number of Vehicles
Travel Demand Management
Machine Learning
XGBoost
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