In transportation networks with stochastic and dynamic travel times, park-and-ride decisions are often made adaptively considering the realized state of traffic. That is, users continue driving towards their destination if the congestion level is low, but may consider taking transit when the congestion level is high. This adaptive behavior determines whether and where people park-and-ride. We propose to use a Markov decision process to model the problem of commuters’ adaptive park-and-ride choice behavior in a transportation network with time-dependent and stochastic link travel times. The model evaluates a routing policy by minimizing the expected cost of travel that leverages the online information about the travel time on outgoing links in making park-and-ride decisions. We provide a case study of park-and-ride facilities located on freeway I-394 in Twin Cities, Minnesota. The results show a significant improvement in the travel time by the use of park-and-ride during congested conditions. It also reveals the time of departure, the state of the traffic, and the location from where park-and-ride becomes an attractive option to the commuters. Finally, we show the benefit of using online routing in comparison to an offline routing algorithm.

在具有随机和动态旅行时间的交通网络中，通常会根据已实现的交通状态自适应地做出停车换乘决策。也就是说，如果拥堵程度低，用户继续开车前往目的地，但在拥堵程度高时可以考虑乘坐公共交通工具。这种适应性行为决定了人们是否以及在哪里停车和乘车。我们建议使用马尔可夫决策过程来模拟交通网络中通勤者的自适应停车换乘选择行为问题，该交通网络具有时间依赖和随机链接旅行时间。该模型通过最小化旅行的预期成本来评估路线策略，该成本在做出停车换乘决策时利用有关传出链接上的旅行时间的在线信息。我们提供了位于明尼苏达州双城 I-394 高速公路上的停车换乘设施的案例研究。结果表明，在拥挤的情况下，使用停车换乘可以显着改善出行时间。它还揭示了出发时间、交通状况以及停车换乘成为对通勤者有吸引力的选择的位置。最后，我们展示了使用在线路由与离线路由算法相比的好处。