AbstractHigh-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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