Coupling graph neural networks and travel mode choice for human mobility prediction

Abstract

Travel mode serves as the link for communication among people, between people and objects, and between people and places. The human mobility is closely related to travel mode. Scientifically predicting human mobility can help alleviate traffic congestion and provide flexible travel choices. However, in current predictions, only the travel demand or human mobility is taken into account, while the influence of residents on the travel mode choice is neglected. Therefore, taking Wuhan City as an example, this paper proposes a new method for predicting human mobility by employing graph neural network techniques and travel mode choice behavior. The prediction model established in this study utilizes network analysis to measure the accessibility time and road network distance of traffic travel mode. The graph neural network method is employed to capture the dynamic temporal and spatial relationships underlying human mobility. Furthermore, the performance of the prediction model is evaluated. The results indicate that at a fine spatial scale, the new method can more accurately reveal the spatial patterns of changes in human mobility, significantly improving the accuracy of predicting human mobility.