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Journal of Intelligent Systems

Editor-in-Chief: Fleyeh, Hasan


CiteScore 2018: 1.03

SCImago Journal Rank (SJR) 2018: 0.188
Source Normalized Impact per Paper (SNIP) 2018: 0.533

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2191-026X
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Volume 26, Issue 3

Issues

Relevance- and Frequency-Enabled Trip Planning Model Based on Socio-economic Status

Anand Sesham
  • Corresponding author
  • Department of Computer Science and Engineering, M.V.S.R Engineering College, Nadergul, Hyderabad 501510, India
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  • Other articles by this author:
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/ P. Padmanabham
  • Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Jawaharlal Nehru Technological University, Hyderabad, Telangana 500085, India
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/ A. Govardhan
  • School of Information Technology and Executive Council Member, Jawaharlal Nehru Technological University, Hyderabad, India
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/ Rajesh Kulkarni
Published Online: 2016-08-17 | DOI: https://doi.org/10.1515/jisys-2016-0012

Abstract

Planning a trip not only depends on the traveling cost, time, and path, but also on the socio-economic status of the traveler. This paper attempts to introduce a new trip planning model that is able to work on real-time data with multiple socio-economic constraints. The proposed trip planning model processes real-time data to extract the relevant socio-economic attributes; later, it mines the most frequent as well as the feasible attributes to plan the trip. The relevance of the socio-economic constraints is defined using correlations, whereas the frequent as well as the feasible attributes are mined through the sequential pattern mining approach. Real-time travel information of about 38,303 trips was acquired from the Indian city of Hyderabad, and the proposed model was subjected to experimentation. The proposed model maintained a substantial trade-off between multiple performance metrics, though the trip mean model performed statistically.

Keywords: Correlation; pattern; socio-economic; frequent; trip; planning; mining

Bibliography

  • [1]

    H. Al-Deek and E. B. Emam, New methodology for estimating reliability in transportation networks with degraded link capacities, J. Intell. Transp. Syst. 10 (2006), 117–129.CrossrefGoogle Scholar

  • [2]

    K. Chiew and S. Qin, Scheduling and routing of AMOs in an intelligent transport system, IEEE T. Intell. Transp. Syst. 10 (2009), 547–552.CrossrefWeb of ScienceGoogle Scholar

  • [3]

    V. Di Lecce and A. Amato, Route planning and user interface for an advanced intelligent transport system, IET Intell. Transp. Syst. 5 (2011), 149–158.CrossrefWeb of ScienceGoogle Scholar

  • [4]

    J. Dong and H. S. Mahmassani, Stochastic modeling of traffic flow breakdown phenomenon: application to predicting travel time reliability, in: 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2011 pp. 2112–2117, Washington, DC, 2011.Google Scholar

  • [5]

    L. Figueiredo, I. S. Jesus, J. A. T. Machado, J. R. Ferreira and J. L. Martins de Carvalho, Towards the development of intelligent transportation systems, in: IEEE Conference on Intelligent Transportation Systems, pp. 1206–1211, Oakland, CA, 2001.Google Scholar

  • [6]

    Z. Juan, J. Wu and M. McDonald, Socio-economic impact assessment of intelligent transport systems, Tsinghua Sci. Technol. 11 (2006), 339–350.CrossrefGoogle Scholar

  • [7]

    T. Korhonen, T. Väärämäki, V. Riihimäki, R. Salminen and A. Karila, Selecting telecommunications technologies for intelligent transport system services in Helsinki municipality, IET Intell. Transp. Syst. 6 (2012), 18–28.Web of ScienceCrossrefGoogle Scholar

  • [8]

    N. Lathia, J. Froehlich and L. Capra, Mining public transport usage for personalised intelligent transport systems, in: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 887–892, Sydney, NSW, 2010.Google Scholar

  • [9]

    I. Masaki, Machine-vision systems for intelligent transportation systems, IEEE Intell. Syst. 13 (1998), 24–31.CrossrefGoogle Scholar

  • [10]

    R. B. Noland and J. W. Polak, Travel time variability: a review of theoretical and empirical issues, Transport Rev. 122 (2002), 39–54.CrossrefGoogle Scholar

  • [11]

    W. Peng, W. Jiang-Ping and X. Jing, The application of particle swarm optimization on intelligent transport system, International Colloquium on Computing, Communication, Control, and Management 4 (2009), 389–391, Sanya, China.Google Scholar

  • [12]

    L. Rokach and O. Maimon, Clustering methods, in: Data Mining and Knowledge Discovery Handbook, Springer US, pp. 321–352, 2005.Google Scholar

  • [13]

    S. Shankar and T. Purusothaman, A new utility-emphasized analysis for stock trading rules, Intelligent Data Analysis 17 (2013), 271–294.Google Scholar

  • [14]

    X. Yan, H. Zhang and C. Wu, Research and development of intelligent transportation systems, in: 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science (DCABES, 2012), pp. 321–327, Guilin, 2012.Google Scholar

  • [15]

    Y. Yu, The track research on the latest developments of intelligent transportation systems abroad, in: 11th World Congress on Intelligent Control and Automation (WCICA), 2014, pp. 5132–5137, Shenyang, 2014.Google Scholar

  • [16]

    N. Zeng, K. Qin and J. Li, Intelligent transport management system for urban traffic hubs based on an integration of multiple technologies, in: IEEE 17th International Industrial Engineering and Engineering Management (IE&EM), 29–31 October 2010, pp. 1178–1183, Xiamen, 2010.Google Scholar

  • [17]

    J. Zhang, F. Y. Wang, K. Wang, W. H. Lin, X. Xu and C. Chen, Data-driven intelligent transportation systems: a survey, IEEE T. Intell. Transp. Syst. 12 (2011), 1624–1639.CrossrefWeb of ScienceGoogle Scholar

About the article

Corresponding author: Anand Sesham, Associate Professor, Department of Computer Science and Engineering, M.V.S.R Engineering College, Nadergul, Hyderabad 501510, India


Received: 2016-02-03

Published Online: 2016-08-17

Published in Print: 2017-07-26


Citation Information: Journal of Intelligent Systems, Volume 26, Issue 3, Pages 545–559, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys-2016-0012.

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