Adaptive Park-and-ride Choice on Time-dependent Stochastic Multimodal Transportation Network

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.