A Multi-criteria System for Recommending Taxi Routes with an Advance Reservation

Jie Yu Fang, Fandel Lin, Hsun Ping Hsieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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 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 probabilities, 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 a real-world dataset (NYC taxi datasets) have shown our proposed approach is more effective and robust. Moreover, our designed search scheme in J* 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.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationApplied Data Science Track - European Conference, ECML PKDD 2020, Proceedings
EditorsYuxiao Dong, Dunja Mladenic, Craig Saunders
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783030676667
Publication statusPublished - 2021
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
Duration: 2020 Sept 142020 Sept 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12460 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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