Pengfei WANG, Chu ZHANG, Xiangyu WANG, Peng LIU, Jingpeng WANG
Three types of parking facilities, i.e., on-street, off-street, and shared parking facilities, often coexist in an urban region, and their service characteristics are significantly different. The traffic system within the region typically exhibits uncertainty both in its evolutionary processes and in the observation of its state indicators. This study aims to minimize the total travel cost of participants in the regional traffic system, including area transit costs, parking search costs, management costs, and walking costs. To achieve this, a dual-driven model based on rolling optimization and data fusion estimation is proposed to design dynamic supply strategies for multi-type parking services in the region. The effectiveness of the strategy is verified through Monte Carlo numerical simulations. As a result, it is found that: First, the proposed dynamic optimization problem can be equivalently transformed into a quadratic programming problem with inequality constraints, and if a solution exists, it is guaranteed to be the unique global optimum; second, when considering uncertainties in both process and observation, a significant discrepancy may arise between the system observation outcomes and the system's target trajectory; finally, the introduction of Kalman filter can effectively reduce the gap between the posterior state estimation and the target trajectory, thereby enhancing traffic efficiency in the region and reducing the total travel cost.
