The taxi demand prediction is an important part in the construction of smart cities. In order to predict the taxi demand in the designated regions in the future, this paper proposes a multi-time resolution hierarchical attention-based recurrent highway networks (MTR-HRHN) through expanding existing prediction models. The MTR-HRHN integrates the extraction of spatiotemporal features of exogenous data and the spatiotemporal modeling of target data into a single framework, builds a model based on the different temporal properties of sequential data through multiple resolutions (such as every hour or every day), so as to capture a more comprehensive time pattern. Finally, this paper evaluates the prediction performance of MTR-HRHN on the New York City taxi dataset. The experimental results show that compared with other classical time series prediction methods, MTR-HRHN has better prediction performance in short-term demand prediction in multiple high-demand regions.
