A spatio-temporal schedule-based neural network for urban taxi waiting time prediction

Abstract

Taxi waiting times is an important criterion for taxi passengers to choose appropriate pick-up locations in urban environments. How to predict the taxi waiting time accurately at a certain time and location is the key solution for the imbalance between the taxis’ supplies and demands. Considering the life schedule of urban residents and the different functions of geogrid regions, the research developed in this paper introduces a spatio-temporal schedule-based neural network for urban taxi waiting time prediction. The approach integrates a series of multi-source data from taxi trajectories to city points of interest, different time frames and human behaviors in the city. We apply a grid-based and functional structuration of an urban space that provides a lower-level data representation. Overall, the neural network model can dynamically predict the waiting time of taxi passengers in real time under some given spatio-temporal constraints. The experimental results show that the granular-based grids and spatio-temporal neural network can effectively predict and optimize the accuracy of taxi waiting times. This work provides a decision support for intelligent travel predictions of taxi waiting time in a smart city.