Multi-source information based short-term taxi pick-up demand prediction using deep-learning approaches

Abstract

Short-term demand prediction is of great importance to the on-demand ride-hailing services. Predicted demand information can facilitate efficient operations and improve service performance. This thesis proposes a multi-source information based spatiotemporal neural network (MSI-STNN) deep learning architecture to predict short-term taxi pick-up demand. It fuses pick-up and drop-off time-series data, weather information, location popularity data, using three deep-learning models, including stacked convolutional long short-term memory (Conv-LSTM) model, stacked long short-term memory (LSTM) model, and a convolutional neural network (CNN) model. Conv-LSTM captures the spatiotemporal features of pick-up and drop-off time series. LSTM extracts weather information while CNN incorporates popularity data. A case study is performed to predict short-term pick-up demand at zonal levels 15 minutes using Manhattan, New York taxi data. The results validate the superiority of the proposed approach compared with state-of-art time-series and deep learning approaches, including ARIMA, LSTM and Conv-LSTM.--Author's abstract