Dynamic Pricing Strategy for High-Occupancy Toll Lanes Considering the Tradeoffs between Traffic Efficiency and User Experience
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 1
Abstract
High-occupancy toll (HOT) lanes are emerging as a solution to resolve capacity waste from high-occupancy vehicle (HOV) lanes. This paper proposes a dynamic toll pricing strategy considering traffic efficiency and user experience to balance system and user optimum. First, a long short-term memory-convolutional neural network (LSTM-CNN) is developed to predict the lane-choice behavior of low-occupancy vehicles (LOVs). Real-world data from a 5.57 km segment of Interstate 405 in Bellevue, Washington, in the US are used for validation. Compared with the other four lane-changing models, the proposed model shows the lowest mean absolute error (MAE) at 1.15 for predicting the lane-changing ratio. Next, a toll pricing optimization model was developed. Total travel time and price-performance ratio obtained by traffic status estimation approaches (Greenberg’s traffic flow model and cumulative arrival-departure diagram) represent the two optimums in the objective function, respectively. Then, a dynamic weight function that varies with demand is constructed to realize the tradeoff. Based on the previously identified lane-choice behavior, an optimal toll can be calculated dynamically according to real-time transaction and traffic data. After adopting the new strategy, the total travel time reaches an average drop of 18.12%, and the performance-price ratio reaches an average increase of 28.12% during peak hours under deterministic conditions. The results show that the proposed dynamic pricing strategy helps to bring a significant reduction in the total travel time and at the same time provides a satisfactory toll rate for users.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
Many thanks to Professor Yinhai Wang and the STAR Lab for the data. Also, thanks to Professor Tangyi Guo for his great help during the revision stage of the paper. This study is supported by the National Key R&D Program of China (2019YFE0123800), Nanjing International Cooperation Project No. 202002013, and Jiangsu Transportation Science and Technology Project (2021Y).
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 1 • January 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Oct 18, 2021
Accepted: Jul 15, 2022
Published online: Oct 27, 2022
Published in print: Jan 1, 2023
Discussion open until: Mar 27, 2023
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