Surrogate‐Based Optimization of Expensive‐to‐Evaluate Objective for Optimal Highway Toll Charges in Transportation Network

This article adopts a family of surrogate‐based optimization approaches to approximate the response surface for the transportation simulation input–output mapping and search for the optimal toll charges in a transportation network. The computational effort can thus be significantly reduced for the expensive‐to‐evaluate optimization problem. Meanwhile, the random noise that always occurs through simulations can be addressed by this family of approaches. Both one‐stage and two‐stage surrogate models are tested and compared. A suboptimal exploration strategy and a global exploration strategy are incorporated and validated. A simulation‐based dynamic traffic assignment model DynusT (Dynamic Urban Systems in Transportation) is utilized to evaluate the system performance in response to different link‐additive toll schemes implemented on a highway in a real road transportation network. With the objective of minimizing the network‐wide average travel time, the simulation results show that implementing the optimal toll predicted by the surrogate model can benefit society in multiple ways. The travelers gain from the 2.5% reduction (0.45 minutes) of the average travel time. The total reduction in the time cost during the extended peak hours would be around US$65,000 for all the 570,000 network users assuming a US$15 per hour value of time. Meanwhile, the government benefits from the 20% increase of toll revenue compared to the current situation. Thus, applying the optimized pricing scheme in real world can be an encouraging policy option to enhance the performance of the transportation system in the study region.