DRBO—A Regional Scale Simulator Calibration Framework Based on Day-to-Day Dynamic Routing and Bayesian Optimization

Highlights: What are the main findings? Developed the DRBO framework, which successfully integrates dynamic routing and Bayesian optimization to calibrate large-scale regional traffic simulators, significantly enhancing calibration efficiency and accuracy. Validated DRBO using SFCTA demand data, achieving close alignment between simulated speed distributions and real-world observations, thereby demonstrating the framework’s reliability and scalability for regional traffic network calibration. What is the implication of the main finding? Facilitates informed urban transportation planning by enabling accurate and efficient calibration of large-scale traffic simulators, thus supporting better infrastructure and policy decisions. Advances traffic simulation methodologies, providing a robust framework that can be adapted for various regional transportation networks, enhancing the reliability and scalability of traffic modeling efforts. Traffic simulation, a tool for recreating real-life traffic scenarios, acts as an important platform in transportation research. Considering the growing complexity of urban mobility, various large-scale regional simulators are designed and used for research and applications. Calibration is a key issue in the traffic simulation: it finds the optimal system pattern to decrease the gap between the simulator output and the real data, making the system much more reliable. This paper proposes DRBO, a calibration framework for large-scale traffic simulators. This framework combines the travel behavior adjustment with black box optimization, better exploring the structure of the regional scale mobility. The motivation of the framework is based on the decomposition of the regional scale mobility dynamic. We decompose the mobility dynamic into the car-following dynamic and the routing dynamic. The prior dynamic imitates how vehicles propagate as time flows while the latter one reveals how vehicles choose their route according to their own information. Based on the decomposition, the DRBO framework uses iterative algorithms to find the best dynamic combinations. It utilizes the Bayesian optimization and day-to-day routing update to separately calibrate the dynamic, then combine them sequentially in an iterative way. Compared to the prior arts, the DRBO framework is efficient for capturing multiple perspectives of traffic conditions. We further tested our simulator on SFCTA demand to further validate the speed distribution from our simulation and observed data.