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A Universal Automated Data-Driven Modeling Framework for Truck Traffic Volume Prediction

By: Sam Mahdavian, Alireza Shojaei, Haluk Laman

Summarized Description:

Knowledge of the truck traffic volumes on state and interstate highways is critical for highway authorities and federal organizations. Increased urbanization, population growth, and economic development have led to an increased demand for freight travel. Several planning applications demand reliable and accurate truck traffic prediction. A review of the available literature indicated that limited research had been performed on the development and utilization of a universal automatic framework for truck traffic volume prediction. As a result, there is a gap to incorporate inclusive predictors, a broad dataset, a comprehensive feature selection approach, and a robust cross-validation method that utilizes both linear and non-linear algorithms. The present study uses a hyperparameter optimization framework to select the appropriate feature selection method and modeling approach among a comprehensive list of available state of the art approaches. Distinct from models based on individual case studies, the proposed framework allows for greater customization and minimized MAPE error. The developed framework automates much of the traffic count forecasting process, and the resulting method is less labor-intensive and may be utilized without the need for experienced data analysts. Florida's interstate highways historical traffic data were used to test the feasibility of the proposed framework. The results of the Florida Case Study revealed the superiority of non-linear models in the generalization and prediction of traffic volumes over linear models. The random forest algorithm results on the test dataset in this study demonstrate this model's ability to predict truck traffic with 86% accuracy. Spatial variables were the most significant variable group, followed by road characteristics.

Pic 1: 259 sites included in this study on the Florida interstates map

Pic 2: Nested cross-validation; Comparison of different models’ best performance on the test dataset for mixed trucks


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