Real-Time Route Recommendations for E-Taxies Leveraging GPS Trajectories

Wei Tu, Ke Mai, Yatao Zhang, Yang Xu, Jincai Huang, Min Deng, Long Chen, Qingquan Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Electric vehicles (EVs) currently face formidable challenges in promotion, i.e., short driving ranges, long charging times, and few charging stations, thereby limiting their acceptability to taxi drivers. Leveraging massive-scale taxi GPS trajectory data, we present a novel real-time route recommendation system for electric taxi (ET) drivers. Taxi travel knowledge, including the probability of picking up passengers and the distribution of destinations, is learned from the raw GPS trajectories. Considering the cascading effect of route decision making, consecutive ET actions are modeled with an action tree. The corresponding expected net revenue is estimated based on the learned knowledge. A prototype online system is developed for providing route recommendations, e.g., when to go to a charging station or cruise on certain roads. An experiment in Shenzhen demonstrates that the average daily net revenue of ET drivers is better than those of 76.2% of gasoline taxi drivers. The presented approach not only increases the revenue of ET drivers in the short term but also improves the viability of EVs in the long run.

Original languageEnglish
Article number9079205
Pages (from-to)3133-3142
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume17
Issue number5
DOIs
Publication statusPublished - May 2021

Keywords

  • Action tree search
  • electric taxies (ETs)
  • GPS trajectories
  • taxi recommendation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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