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

doi:10.1109/TKDE.2017.2772907

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 Publications › Journal Article (refereed) › peer-review

58 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	585-598
Number of pages	14
Journal	IEEE Transactions on Knowledge and Data Engineering
Volume	30
Issue number	3
Early online date	13 Nov 2017
DOIs	https://doi.org/10.1109/TKDE.2017.2772907
Publication status	Published - Mar 2018
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 1989-2012 IEEE.

Funding

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).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

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

Access to Document

10.1109/TKDE.2017.2772907

Cite this

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title = "TaxiRec : Recommending Road Clusters to Taxi Drivers Using Ranking-Based Extreme Learning Machines",

abstract = "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.",

keywords = "Extreme learning machine, Passenger-finding potential, Recommender system, Taxi trajectory data analytics",

author = "Ran WANG and Chi-Yin CHOW and Yan LYU and LEE, \{Victor C. S.\} and Sam KWONG and Yanhua LI and Jia ZENG",

note = "Publisher Copyright: {\textcopyright} 1989-2012 IEEE.",

year = "2018",

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doi = "10.1109/TKDE.2017.2772907",

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AU - LEE, Victor C. S.

AU - KWONG, Sam

AU - LI, Yanhua

AU - ZENG, Jia

PY - 2018/3

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KW - Passenger-finding potential

KW - Recommender system

KW - Taxi trajectory data analytics

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ER -

TaxiRec : Recommending Road Clusters to Taxi Drivers Using Ranking-Based Extreme Learning Machines

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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