Abstract

As the demand for taxi reservation services has increased, increasing the income of taxi drivers with advanced services has attracted attention. In this article, we propose a path decision framework that considers real-time spatial-temporal predictions and traffic network information. The goal is to optimize a taxi driver's profit when considering a reservation. Our framework contains four components. First, we build a grid-based road network graph for modeling traffic network information for speeding up the search process. Next, we conduct two prediction modules that adopt advanced deep learning techniques to guide proper search directions for recommending cruising locations. One module of the 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 …

Date

2022

Authors

Hsun-Ping Hsieh, Fandel Lin, Nai-Yu Chen, Tzu-Hsin Yang

Journal

ACM Transactions on Management Information Systems (TMIS)

Volume

13

Issue

3

Pages

1-30

Publisher

ACM