Modeling the Dynamic Impacts of Multi-modal Guidance Information on Commuters' Trip Chain

To study commuters' dynamic choice behavior under the multi-modal guidance information, based on utility theory and multi-stage decision method, a dynamic model was proposed to describe the choice behavior of the daily trip chain that starts and ends at home with the objective of maximizing the perceived utility of a trip chain, and a Dijkstra algorithm was used to solve the model. In the model, the trip chain was divided into some single but sequential trips, and each trip contained pre-trip and en-route decision nodes, on which the travel information was loaded dynamically. Commuters' reliance on information and the learning process were also taken into account to describe the actual decision process more accurately. Computational results show that multimodal guidance information contributes to increase the actual utility of the trip chain by an average of 0.88% for each commuter; in addition, as the reliance increases, commuters will benefit more and tend to shorten the length of trip chain as well as to choose metro or park-and-ride, in order to avoid the utility loss from traffic congestion.