TaxiRec : Recommending road clusters to taxi drivers using ranking-based extreme learning machines

Ran WANG, Chi-Yin CHOW, Yan LYU, Victor C. S. LEE, Sam KWONG, Yanhua LI, Jia ZENG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

9 Citations (Scopus)


Utilizing large-scale GPS data to improve taxi services becomes a popular research problem in the areas of data mining, intelligent transportation, and the Internet of Things. In this paper, we utilize a large-scale GPS data set generated by over 7,000 taxis in a period of one month in Nanjing, China, and propose TaxiRec; a framework for discovering the passenger-finding potentials of road clusters, which is incorporated into a recommender system for taxi drivers to hunt passengers. In TaxiRec, we first construct the road network by defining the nodes and road segments. Then, the road network is divided into a number of road clusters through a clustering process on the mid points of the road segments. Afterwards, a set of features for each road cluster is extracted from real-life data sets, and a ranking-based extreme learning machine (ELM) model is proposed to evaluate the passenger-finding potential of each road cluster. Experimental results demonstrate the feasibility and effectiveness of the proposed framework.
Original languageEnglish
Title of host publicationGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
Publication statusPublished - 3 Nov 2015
Externally publishedYes


  • Extreme learning machine
  • Passenger-finding potential
  • Recommender system
  • Taxi trajectory data


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