GPS data based urban guidance

Yao-Hua Ho, Yao Chuan Wu, Meng Chang Chen, Tsun Jui Wen, Yeali S. Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In many metropolitan areas, traffic congestion is an escalating problem which causes a significant waste of money and time. Nowadays, cars equipped with GPS devices become widespread. The location information of those cars is very useful for estimate traffic condition in the complex city road network. Using the accurate and real time traffic condition, we can provide dynamic route guidance to ease traffic congestion. In this paper, we proposed a speed pattern model, called two phase piecewise linear speed model (2PEED), to estimate traffic condition and represent speed pattern in a road network using GPS data collected vehicles. With the estimated traffic condition and speed pattern, a proposed classification-based route guidance approach using machine learning technique provides dynamic routing for drivers. Using both current traffic data and the experience learned from history data, our route guidance approach is able to accurately predict the future traffic condition and selects a best route. We give simulation results to show that the proposed approach is able to select and dynamically update a route to prove drivers a best (e.g., less traffic and shortest travel time) route to their destination.

Original languageEnglish
Title of host publicationProceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
Pages703-708
Number of pages6
DOIs
Publication statusPublished - 2011 Sep 19
Event2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011 - Kaohsiung, Taiwan
Duration: 2011 Jul 252011 Jul 27

Other

Other2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
CountryTaiwan
CityKaohsiung
Period11/7/2511/7/27

Fingerprint

Global positioning system
Traffic congestion
Railroad cars
Travel time
Learning systems

Keywords

  • And machine learning
  • GPS
  • Route guidance
  • Speed pattern estimation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

Ho, Y-H., Wu, Y. C., Chen, M. C., Wen, T. J., & Sun, Y. S. (2011). GPS data based urban guidance. In Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011 (pp. 703-708). [5992685] https://doi.org/10.1109/ASONAM.2011.46

GPS data based urban guidance. / Ho, Yao-Hua; Wu, Yao Chuan; Chen, Meng Chang; Wen, Tsun Jui; Sun, Yeali S.

Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011. 2011. p. 703-708 5992685.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ho, Y-H, Wu, YC, Chen, MC, Wen, TJ & Sun, YS 2011, GPS data based urban guidance. in Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011., 5992685, pp. 703-708, 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, Kaohsiung, Taiwan, 11/7/25. https://doi.org/10.1109/ASONAM.2011.46
Ho Y-H, Wu YC, Chen MC, Wen TJ, Sun YS. GPS data based urban guidance. In Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011. 2011. p. 703-708. 5992685 https://doi.org/10.1109/ASONAM.2011.46
Ho, Yao-Hua ; Wu, Yao Chuan ; Chen, Meng Chang ; Wen, Tsun Jui ; Sun, Yeali S. / GPS data based urban guidance. Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011. 2011. pp. 703-708
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