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

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

9 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationSIGSPATIAL '15 Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems
PublisherAssociation for Computing Machinery
ISBN (Print)9781450339674
Publication statusPublished - 3 Nov 2015

Conference

Title23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015
PlaceUnited States
CitySeattle
Period3 - 6 November 2015

Abstract

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.

Research Area(s)

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

Citation Format(s)

TaxiRec : Recommending road clusters to taxi drivers using ranking-based extreme learning machines. / Wang, Ran; Chow, Chi-Yin; Lyu, Yan et al.

SIGSPATIAL '15 Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems . Association for Computing Machinery, 2015. 53.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review