LAUSR

Understanding and forecasting mobility patterns and travel demand are fundamental and critical to efficient transport infrastructure planning and service operation. However, most existing studies focused on deterministic demand estimation/prediction/analytics. Differently, this study provides confidence interval based demand forecasting, which can help transport planning and operation authorities to better accommodate demand uncertainty/variability. The proposed Origin-Destination (OD) demand prediction approach well captures and utilizes the correlations among spatial and temporal information. In particular, the proposed Probabilistic Graph Convolution Model (PGCM) consists of two components: (i) a prediction module based on Graph Convolution Network and combined with the gated mechanism to predict OD demand by utilizing spatio-temporal relations; (ii) a Bayesian-based approximation module to measure the confidence interval of demand prediction by evaluating the graph-based model uncertainty. We use a large-scale real-world public transit dataset from the Greater Sydney area to test and evaluate the proposed approach. The experimental results demonstrate that the proposed method is capable of capturing the spatial-temporal correlations for more robust demand prediction against several established tools in the literature.