Robust Truck Transit Time Prediction through GPS Data and Regression Algorithms in Mixed Traffic Scenarios

Adel Ghazikhani, Samaneh Davoodipoor, Amir M. Fathollahi-Fard (), Mohammad Gheibi and Reza Moezzi
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Adel Ghazikhani: Department of Computer Engineering, Imam Reza International University, Mashhad 178-436, Iran
Samaneh Davoodipoor: Department of Computer Engineering, Imam Reza International University, Mashhad 178-436, Iran
Amir M. Fathollahi-Fard: Département d’Analytique, Opérations et Technologies de l’Information, Université de Québec à Montreal, 315, Sainte-Catherine Street East, Montreal, QC H2X 3X2, Canada
Mohammad Gheibi: Institute for Nanomaterials, Advanced Technologies, and Innovation, Technical University of Liberec, 461 17 Liberec, Czech Republic
Reza Moezzi: Faculty of Mechatronics, Informatics and Interdisciplinary Studies, Technical University of Liberec, 461 17 Liberec, Czech Republic

Mathematics, 2024, vol. 12, issue 13, 1-26

Abstract: To enhance safety and efficiency in mixed traffic scenarios, it is crucial to predict freight truck traffic flow accurately. Issues arise due to the interactions between freight trucks and passenger vehicles, leading to problems like traffic congestion and accidents. Utilizing data from the Global Positioning System (GPS) is a practical method to enhance comprehension and forecast the movement of truck traffic. This study primarily focuses on predicting truck transit time, which involves accurately estimating the duration it will take for a truck to travel between two locations. Precise forecasting has significant implications for truck scheduling and urban planning, particularly in the context of cross-docking terminals. Regression algorithms are beneficial in this scenario due to the empirical evidence confirming their efficacy. This study aims to achieve accurate travel time predictions for trucks by utilizing GPS data and regression algorithms. This research utilizes a variety of algorithms, including AdaBoost, GradientBoost, XGBoost, ElasticNet, Lasso, KNeighbors, Linear, LinearSVR, and RandomForest. The research provides a comprehensive assessment and discussion of important performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R 2 ). Based on our research findings, combining empirical methods, algorithmic knowledge, and performance evaluation helps to enhance truck travel time prediction. This has significant implications for logistical efficiency and transportation dynamics.

Keywords: freight truck scheduling; cross-docking terminals; GPS data analysis; regression algorithms; traffic flow modelling; arrival time estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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