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: Journal PublicationsJournal Article (refereed)peer-review

49 Citations (Scopus)


Utilizing large-scale GPS data to improve taxi services has become a popular research problem in the areas of data mining, intelligent transportation, geographical information systems, 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 evaluating and discovering the passenger-finding potentials of road clusters, which is incorporated into a recommender system for taxi drivers to seek passengers. In TaxiRec, the underlying road network is first segmented into a number of road clusters, a set of features for each road cluster is extracted from real-life data sets, and then a ranking-based extreme learning machine (ELM) model is proposed to evaluate the passenger-finding potential of each road cluster. In addition, TaxiRec can use this model with a training cluster selection algorithm to provide road cluster recommendations when taxi trajectory data is incomplete or unavailable. Experimental results demonstrate the feasibility and effectiveness of TaxiRec.
Original languageEnglish
Pages (from-to)585-598
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number3
Early online date13 Nov 2017
Publication statusPublished - Mar 2018
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61772344, Grant 61402460, Grant 61732011, Grant 61472257, and Grant 61373092, in part by the Guangdong Provincial Science and Technology Plan Project under Grant 2013B040403005, in part by the HD Video R&D Platform for Intelligent Analysis and Processing in the Guangdong Engineering Technology Research Centre of Colleges and Universities under Grant GCZX-A1409, in part by the Natural Science Foundation of SZU under Grant 2017060, and in part by CityU research grants (CityU Project No. 9231131). Yanhua Li was supported in part by NSF CRII grant CNS-1657350 and a research grant from Pitney Bowes Inc. % Generated by IEEEtran.bst, version: 1.13 (2008/09/30).


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


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