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:10.1145/2820783.2820843

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 Proceedings › Conference paper (refereed) › Research › peer-review

10 Citations (Scopus)

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.

Original language	English
Title of host publication	SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems
Editors	Mohamed ALI, Yan HUANG, Michael GERTZ, Matthias RENZ, Jagan SANKARANARAYANAN
Publisher	Association for Computing Machinery
Number of pages	4
ISBN (Print)	9781450339674
DOIs	https://doi.org/10.1145/2820783.2820843
Publication status	Published - Nov 2015
Externally published	Yes
Event	23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems - Seattle, United States Duration: 3 Nov 2015 → 6 Nov 2015

Conference

Conference	23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems
Abbreviated title	SIGSPATIAL'15
Country/Territory	United States
City	Seattle
Period	3/11/15 → 6/11/15

Keywords

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

Access to Document

10.1145/2820783.2820843

Cite this

WANG, R., CHOW, C.-Y., LYU, Y., LEE, V. C. S., KWONG, S., LI, Y., & ZENG, J. (2015). TaxiRec : Recommending road clusters to taxi drivers using ranking-based extreme learning machines. In M. ALI, Y. HUANG, M. GERTZ, M. RENZ, & J. SANKARANARAYANAN (Eds.), SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Article 53 Association for Computing Machinery. https://doi.org/10.1145/2820783.2820843

WANG, Ran ; CHOW, Chi-Yin ; LYU, Yan et al. / TaxiRec : Recommending road clusters to taxi drivers using ranking-based extreme learning machines. SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. editor / Mohamed ALI ; Yan HUANG ; Michael GERTZ ; Matthias RENZ ; Jagan SANKARANARAYANAN. Association for Computing Machinery, 2015.

@inproceedings{2bc809e9720d4328898b576021fc9790,

title = "TaxiRec : Recommending road clusters to taxi drivers using ranking-based extreme learning machines",

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.",

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

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

year = "2015",

month = nov,

doi = "10.1145/2820783.2820843",

language = "English",

isbn = "9781450339674",

editor = "Mohamed ALI and Yan HUANG and Michael GERTZ and Matthias RENZ and Jagan SANKARANARAYANAN",

booktitle = "SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems",

publisher = "Association for Computing Machinery",

note = "23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL'15 ; Conference date: 03-11-2015 Through 06-11-2015",

}

WANG, R, CHOW, C-Y, LYU, Y, LEE, VCS, KWONG, S, LI, Y & ZENG, J 2015, TaxiRec : Recommending road clusters to taxi drivers using ranking-based extreme learning machines. in M ALI, Y HUANG, M GERTZ, M RENZ & J SANKARANARAYANAN (eds), SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems., 53, Association for Computing Machinery, 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, Washington, United States, 3/11/15. https://doi.org/10.1145/2820783.2820843

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. ed. / Mohamed ALI; Yan HUANG; Michael GERTZ; Matthias RENZ; Jagan SANKARANARAYANAN. Association for Computing Machinery, 2015. 53.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - TaxiRec : Recommending road clusters to taxi drivers using ranking-based extreme learning machines

AU - WANG, Ran

AU - CHOW, Chi-Yin

AU - LYU, Yan

AU - LEE, Victor C. S.

AU - KWONG, Sam

AU - LI, Yanhua

AU - ZENG, Jia

PY - 2015/11

Y1 - 2015/11

N2 - 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.

AB - 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.

KW - Extreme learning machine

KW - Passenger-finding potential

KW - Recommender system

KW - Taxi trajectory data

UR - http://www.scopus.com/inward/record.url?scp=84961208487&partnerID=8YFLogxK

U2 - 10.1145/2820783.2820843

DO - 10.1145/2820783.2820843

M3 - Conference paper (refereed)

SN - 9781450339674

BT - SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

A2 - ALI, Mohamed

A2 - HUANG, Yan

A2 - GERTZ, Michael

A2 - RENZ, Matthias

A2 - SANKARANARAYANAN, Jagan

PB - Association for Computing Machinery

T2 - 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

Y2 - 3 November 2015 through 6 November 2015

ER -

WANG R, CHOW CY, LYU Y, LEE VCS, KWONG S, LI Y et al. TaxiRec : Recommending road clusters to taxi drivers using ranking-based extreme learning machines. In ALI M, HUANG Y, GERTZ M, RENZ M, SANKARANARAYANAN J, editors, SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Association for Computing Machinery. 2015. 53 doi: 10.1145/2820783.2820843

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

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