Optimizing Taxi Driver Profit Efficiency: A Spatial Network-Based Markov Decision Process Approach

Abstract

Taxi services play an important role in the public transportation system of large cities. Improving taxi business efficiency is an important societal problem. Most of the recent analytical approaches on this topic only considered how to maximize the pickup chance, energy efficiency, or profit for the immediate next trip when recommending seeking routes, therefore may not be optimal for the overall profit over an extended period of time due to ignoring the destination choice of potential passengers. To tackle this issue, we propose a novel Spatial Network-based Markov Decision Process (SN-MDP) with a rolling horizon configuration to recommend better driving directions. Given a set of historical taxi records and the current status (e.g., road segment and time) of a vacant taxi, we find the best move for this taxi to maximize the profit in the near future. We propose statistical models to estimate the necessary time-variant parameters of SN-MDP from data to avoid competition between drivers. In addition, we take into account fuel cost to assess profit, rather than only income. A case study and several experimental evaluations on a real taxi dataset from a major city in China show that our proposed approach improves the profit efficiency by up to 13.7 percent and outperforms baseline methods in all the time slots.