How to Optimize the Profit of Taxi Routes with an Advance Reservation? J*: A Multi-Criteria Approach Using Spatial-Temporal Predictions

Abstract

The diversity of taxi services has grown with the development of cities 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 approach 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 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 destination prediction which can predict the distribution of drop-off probabilities and capture the flow of potential passengers Finally we propose our J* (J-star) algorithm which jointly considers pick-up probability drop-off distribution road network distance and time factors based on the attentive heuristic function Compared with existing route planning methods the experimental results on real-world datasets (NYC taxi datasets) have shown our proposed approach is more effective and robust Moreover our designed search scheme can decrease the computing time and make the search process more efficient To the best of our knowledge this is the first work that focuses on designing a guiding route which can increase the income of taxi drivers when they have an advance reservation

Date of Award	2020
Original language	English
Supervisor	Hsun-Ping Hsieh (Supervisor)