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Location recommendation based on location history and spatio-temporal correlations for an on-demand bus system

Authors:

Rudy Raymond,

Takamitsu Sugiura,

Kota TsubouchiAuthors Info & Claims

GIS '11: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 377 - 380

https://doi.org/10.1145/2093973.2094027

Published: 01 November 2011 Publication History

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Abstract

An on-demand bus is like a shared taxi that operates only when riders want to travel between the origin and destination locations. It offers many advantages over fixed-route buses, but the riders are bothered by the need to tediously enter such data as origins, destinations, and deadlines. A location recommendation system that predicts such data would help riders during the reservation process and help target potential riders when buses are idle. In this paper, a general and scalable framework for such location recommendation algorithms is presented. It is based on users' location histories and spatio-temporal correlations among the locations by combining prediction methods of the collaborative filtering algorithms, which are widely used in e-commerce, with a popular method in data mining called link propagation. Experiments on real-world data demonstrate that the accuracy of recommendations with the spatio-temporal information is better than those without.

References

[1]

H. Kashima, T. Kato, Y. Yamanishi, M. Sugiyama, and K. Tsuda. Link propagation: A fast semi-supervised learning algorithm for link prediction. In SDM'09, pages 1099--1110. SIAM, 2009.

Google Scholar

[2]

D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In CIKM '03, pages 556--559. ACM Press, 2003.

Digital Library

Google Scholar

[3]

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In CSCW94, pages 175--186, New York, NY, USA, 1994. ACM.

Digital Library

Google Scholar

[4]

B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl. Application of dimensionality reduction in recommender system -- a case study. In WebKDD 2000. ACM, 2000.

Google Scholar

[5]

H. Tong, C. Faloutsos, and J.-Y. Pan. Random walk with restart: Fast solutions and applications. Knowledge and Information Systems: An International Journal (KAIS), 2008.

Digital Library

Google Scholar

[6]

M. Ye, P. Yin, and W.-C. Lee. Location recommendation for location-based social networks. In ACM SIGSPATIAL GIS '10, pages 458--461, New York, NY, USA, 2010. ACM.

Digital Library

Google Scholar

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Singh PElmi ZLau YBorowska-Stefańska MWiśniewski SDulebenets M(2022)Blockchain and AI Technology Convergence: Applications in Transportation SystemsVehicular Communications10.1016/j.vehcom.2022.100521(100521)Online publication date: Sep-2022
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Gaur LSahoo BGaur LSahoo B(2022)Introduction to Explainable AI and Intelligent TransportationExplainable Artificial Intelligence for Intelligent Transportation Systems10.1007/978-3-031-09644-0_1(1-25)Online publication date: 9-Aug-2022
https://doi.org/10.1007/978-3-031-09644-0_1
Katarya R(2022)Towards the significance of taxi recommender systems in smart citiesConcurrency and Computation: Practice and Experience10.1002/cpe.747535:2Online publication date: 4-Nov-2022
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Location recommendation based on location history and spatio-temporal correlations for an on-demand bus system
1. Information systems
  1. Information systems applications

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Information

Published In

GIS '11: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2011

559 pages

ISBN:9781450310314

DOI:10.1145/2093973

Editors:
Divyakant Agrawal
University of California at Santa Barbara
,
Isabel Cruz
University of Illinois at Chicago
,
Christian S. Jensen
Aarhus University, Denmark
,
Eyal Ofek
Microsoft, USA
,
Egemen Tanin
University of Melbourne, Australia

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2011

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GIS '11

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SIGSPATIAL

GIS '11: 19th SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 1 - 4, 2011

Illinois, Chicago

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Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Singh PElmi ZLau YBorowska-Stefańska MWiśniewski SDulebenets M(2022)Blockchain and AI Technology Convergence: Applications in Transportation SystemsVehicular Communications10.1016/j.vehcom.2022.100521(100521)Online publication date: Sep-2022
https://doi.org/10.1016/j.vehcom.2022.100521
Gaur LSahoo BGaur LSahoo B(2022)Introduction to Explainable AI and Intelligent TransportationExplainable Artificial Intelligence for Intelligent Transportation Systems10.1007/978-3-031-09644-0_1(1-25)Online publication date: 9-Aug-2022
https://doi.org/10.1007/978-3-031-09644-0_1
Katarya R(2022)Towards the significance of taxi recommender systems in smart citiesConcurrency and Computation: Practice and Experience10.1002/cpe.747535:2Online publication date: 4-Nov-2022
https://doi.org/10.1002/cpe.7475
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Liyanage SDia HAbduljabbar RBagloee S(2019)Flexible Mobility On-Demand: An Environmental ScanSustainability10.3390/su1105126211:5(1262)Online publication date: 27-Feb-2019
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Abduljabbar RDia HLiyanage SBagloee S(2019)Applications of Artificial Intelligence in Transport: An OverviewSustainability10.3390/su1101018911:1(189)Online publication date: 2-Jan-2019
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