Abstract

In this paper, we propose a multi-criteria route recommendation framework that considers real-time spatial–temporal predictions and traffic network information, aiming to optimize a taxi driver’s profit when considering an advance reservation. Our framework consists of four components. First, we build a grid-based road network graph for modelling traffic network information during the search process. Next, we conduct two prediction modules that adopt advanced deep learning techniques to guide proper search directions in the final planning stage. One module, taxi demand prediction, is used to estimate the pick-up probabilities of passengers in the city. Another one is destination prediction, which can predict the distribution of drop-off probabilities and capture the flow of potential passengers. Finally, we propose J* (J-star) algorithm, which jointly considers pick-up probabilities, drop-off distribution, road network …

Date

January 2, 2022

Authors

Hsun-Ping Hsieh, Fandel Lin

Journal

International Journal of Urban Sciences

Volume

26

Issue

1

Pages

162-183

Publisher

Routledge