Simulation and Evaluation of Guidance Strategies in the Park and Ride Condition

Abstract

Traffic guidance strategies have been investigated in the studies of intelligent transportation systems. Most of the prevailing studies are carried out at the single-mode traffic network, while few studies consider the multimodal traffic network, which links with metro service in the Park and Ride (P&R) condition. To fill the gap, this paper builds a multimodal traffic network, which includes both the road and the metro, where the traffic simulator Paramics is conducted to run the simulation. Bang-bang, P, and PI strategies are selected as the guidance strategies, and the average travel cost is utilized as the evaluation indicator. The results indicate that PI strategy has better performance than P and bang-bang strategies in the normal period, while PI and P strategies have similar tends and work better than bang-bang strategy in the peak period. Furthermore, when taking parking spots into consideration, the more the parking spots, the better the guidance performance. These findings compare the performance of different guidance strategies in the P&R condition, and can be applied for transportation systems operations and control, thus to alleviate the urban traffic congestion.