Modeling the Dynamic Choice of Travel Locations With the Spatial-Temporal Bounded Rationality

It is important to understand the mechanism of travel location choices, since the dynamic individual behavior would lead to the evolution of the network. The existing location choice researches usually based on the static and perfect information, and analyzed each step of the travel independently. This research considers the interaction between individuals on a series of trips in the travel chain by incorporating the spatial-temporal bounded rationality estimation. The Indifference Zone and information sharing in the travel process are defined to explore the spatial and temporal sides. A within-day location choice model is developed based on the spatial benefit and temporal cost. The proposed spatial-temporal bounded rationality LSTM model is verified in five cities networks in China, and it shows the 8.65% and 20.30% improvement, respectively, compared to the spatial bounded rationality LSTM models and perfect rationality LSTM models. In addition, the improvement becomes more pronounced when more alternative locations (the largest city improve 31.65%), more serious congestion (improve 27.45%), more complex chain (improve 13.85%), and more stable weight (improve 22.88%). The proposed dynamic decision model with bounded rationality would provide insights for travel chain prediction in the complex urban network.