Spatiotemporal Hashing Multigraph Convolutional Network for Service-Level Passenger Flow Forecasting in Bus Transit Systems

Abstract

Multistep service-level passenger flow forecasting is of great value in bus transit systems. This task is faced with great challenges due to complicated and dynamic spatial–temporal dependencies, such as interstation semantic dependencies, interline spatial dependencies, and interservice temporal dependencies, which are not effectively modeled by existing methods. To address these challenges, we propose a spatiotemporal hashing multigraph convolution network, called ST-HMGCN. ST-HMGCN constructs two types of subgraphs from perspectives of physical adjacency and semantic similarity to explicitly capture spatial–temporal dependencies among bus stations/lines, and integrates the interservice temporal correlations to achieve the service-level bus passenger flow forecasting. Moreover, it utilizes the hashing graph convolution to extract the dynamic spatial correlations among graph nodes. Furthermore, a temporal-attention block with residual connections is used to model the nonlinear temporal correlations between different time intervals of each station, which significantly reduces the error propagation among prediction time steps. Finally, we use a large-scale real bus operation data set to conduct an extensive evaluation of ST-HMGCN and 11 state-of-the-art baselines, and further leverage the passenger prediction results of our model to provide crowdedness-aware route recommendation. The experimental results verify the effectiveness of the proposed modeling method and its application value in intelligent transportation.