Short-Term Highway Traffic Volume Prediction: A Case Study of the I-94 Interstate in the Saint Paul–Minneapolis Metropolitan Area Using Hybrid Machine Learning Models and Multidimensional Temporal Features

Fallah, Pooyan; Keramati, Vadood

doi:10.22054/jdsm.2026.93135.1092

Short-Term Highway Traffic Volume Prediction: A Case Study of the I-94 Interstate in the Saint Paul–Minneapolis Metropolitan Area Using Hybrid Machine Learning Models and Multidimensional Temporal Features

Articles in Press

Document Type : Research Manuscript

Authors

Pooyan Fallah

Vadood Keramati

Allameh Tabataba’i University, Tehran, Iran.

10.22054/jdsm.2026.93135.1092

Abstract

This study investigates short-term highway traffic-volume prediction as a key component of Intelligent Transportation Systems (ITS), supporting traffic management, infrastructure planning, and reductions in delays and emissions. The Metro Interstate Traffic Volume dataset (48,204 records; 9 variables) was preprocessed through data-quality checks and removal of unrealistic observations. To capture temporal and weather-related effects, systematic feature engineering incorporated day/night status, working days, week position, hourly bins, seasons, lagged traffic volume, and environmental variables such as temperature, precipitation, and weather conditions. Several models were evaluated, including linear and negative binomial regression, nonparametric methods, regression trees, and random forests, with hyperparameters optimized using cross-validation-based randomized and grid search. The selected model achieved strong test performance ($R^2=0.9753$, SMAPE $=9.26\%$), substantially outperforming the raw-feature linear baseline ($R^2\approx0.0484$). Traffic volume at time $t$ was predicted using contemporaneously available environmental variables and lagged traffic observations. The main contribution is a lag-aware, multidimensional feature-engineering and ensemble-learning framework for data-driven intelligent traffic management.

Keywords

Traffic volume forecasting

Intelligent Transportation Systems (ITS)

Feature engineering

Subjects