Abstract

As the demand of taxi reservation services has increased, the strategies of how to increase the income of taxi drivers with advanced service have attracted attention. However, the demand is usually unmet due to the imbalance of profit. 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 the driver has an advance reservation. Our framework consists of four components. First, we build a grid-based road network graph for modeling traffic network information during the search routes process. Next, we conduct two prediction modules that adopt advanced deep learning techniques to guide a proper search direction 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 …

Date

September 14, 2020

Authors

Jie-Yu Fang, Fandel Lin, Hsun-Ping Hsieh

Book

Joint European Conference on Machine Learning and Knowledge Discovery in Databases

Pages

308-322

Publisher

Springer International Publishing