Large-Scale Freeway Traffic Flow Estimation Using Crowdsourced Data: A Case Study in Arizona
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 7
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.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. The crowdsourced data used in this study were provided by a third-party vendor, and the data agreements do not allow for sharing the data.
Acknowledgments
The authors would like to acknowledge the Arizona Department of Transportation (ADOT) for graciously sharing the crowdsourced and loop detector data that made this paper possible.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 7 • July 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Sep 25, 2023
Accepted: Feb 13, 2024
Published online: Apr 30, 2024
Published in print: Jul 1, 2024
Discussion open until: Sep 30, 2024
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