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

9 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	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
DOIs	https://doi.org/10.1145/2820783.2820843
Publication status	Published - 3 Nov 2015
Externally published	Yes

Keywords

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

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10.1145/2820783.2820843

Cite this

@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,

day = "3",

doi = "10.1145/2820783.2820843",

language = "English",

booktitle = "GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems",

}

TaxiRec : Recommending road clusters to taxi drivers using ranking-based extreme learning machines. / WANG, Ran; CHOW, Chi-Yin; LYU, Yan et al.
GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems. 2015. a53.

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 - 2015/11/3

Y1 - 2015/11/3

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

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U2 - 10.1145/2820783.2820843

DO - 10.1145/2820783.2820843

M3 - Conference paper (refereed)

BT - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

ER -

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

Abstract

Keywords

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