Large-Scale Freeway Traffic Flow Estimation Using Crowdsourced Data: A Case Study in Arizona

Abstract

Vehicular flow rate is an essential measure commonly collected by inductive-loop detectors for transportation agencies to evaluate freeways and highways. Loop detectors are typically located in urban areas due to installation and maintenance costs, and do not provide large spatial coverage. Crowdsourced data provide large spatial coverage, but typically do not capture vehicular flow rates. Therefore, a dynamically weighted ensemble (DWE) comprised of XGBoost and neural network models is proposed to expand the spatial coverage of vehicular flow rates by estimating flow rates for the Phoenix, AZ, metropolitan area using crowdsourced data. The model is evaluated using K-fold cross-validation methods, achieving a cross-validated mean absolute percent error of 21.74%, outperforming all other comparison models. The trained model is then used to estimate vehicular flow rates along highways and freeways throughout the state of Arizona. The proposed method provides transportation professionals with a transferable, cost-effective solution for large-scale flow rate estimation.