Prescriptive Analytics for Intelligent Transportation Systems with Uncertain Demand
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 12
Abstract
Data-driven traffic modeling is revolutionizing transportation systems and provides numerous opportunities for achieving high-quality transportation services. A major challenge in optimizing transportation systems is uncertain transportation demand. With the availability of historical data on transportation demand, the uncertain transportation demand can be better modeled, and thereby practitioners can formulate well-informed transportation scheduling decisions. In this paper, we propose three effective and economical transport scheduling strategies using mathematical programming, leveraging big data to extract useful contextual information. Additionally, a perfect-foresight optimization model is proposed to evaluate our proposed data-driven strategies. Results show a negligible optimality gap (i.e., 0.47%) between the optimal solution derived by the perfect-foresight model and the scheduling plans derived by our data-driven strategies. Overall, this paper contributes to the field of transportation engineering by innovatively applying data science, mathematical modeling, and optimization techniques.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was supported by the National Natural Science Foundation of China (Grant Nos. 72201229 and 72361137006). All authors contributed equally to the work. All authors contributed equally to the work and are co-first author.
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Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 12 • December 2023
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© 2023 American Society of Civil Engineers.
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Received: May 4, 2023
Accepted: Jul 27, 2023
Published online: Sep 28, 2023
Published in print: Dec 1, 2023
Discussion open until: Feb 28, 2024
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