Taxi demand forecasting is an important consideration in building up smart cities. However, complex nonlinear spatiotemporal relationships in demand data make it difficult to construct an accurate prediction model. Considering that a single time resolution may not enable accurate learning of the time pattern of taxi demand, we expand the time series prediction model in our proposed multitime resolution hierarchical attention-based recurrent highway network (MTR-HRHN) model, using three time resolutions to model temporal closeness, period, and trend properties of demand data to capture a more comprehensive time pattern. We evaluate the MTR-HRHN on a taxi trip record dataset and the results show that the forecasting performance of the MTR-HRHN exceeds that of eight well-known methods in the short-term demand prediction in some high-demand regions.
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