Predictions of Taxi Demand Based on Neural Network Algorithms

To increase the profit both of taxi drivers and operators, this paper proposes an approach that efficiently collects the features of a customized-shape dispatch area to build the multivariate time-series prediction models for forecasting taxi demands. We also considered population distribution obtained from IMSI (International Mobile Subscriber Identity) data as the spatial correlations feature. The predictive models are built on some neural network algorithms and analyzed statistically. The experiments show that the predictions of the taxi demand in the next 30 minutes are successfully achieved. It is noteworthy that our approach outperforms the forecasting accuracy proved by a real-world error metric.