Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-23T08:41:03.143Z Has data issue: false hasContentIssue false
  • English
  • Français

Determination of Routing Velocity with GPS Floating Car Data and WebGIS-Based Instantaneous Traffic Information Dissemination

Published online by Cambridge University Press:  25 March 2008

Chun Liu ,
Xiaolin Meng  and
Yeming Fan
Chun Liu
Affiliation:
(Tongji University, Shanghai, China)
Xiaolin Meng*
Affiliation:
(Institute of Engineering Surveying and Space Geodesy, The University of Nottingham, UK)
Yeming Fan
Affiliation:
(Tongji University, Shanghai, China)
*
(Email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

The acquisition of accurate and timely traffic information is a vital precondition to rational traffic decision making. Intelligent Transportation Systems (ITS) are bound to be the outcome when modern traffic systems develop to a high degree. In ITS, instantaneous traffic information can be collected by the Floating Car Data (FCD) method. Based on the establishment of the Shenzhen Urban Transportation Simulation System (SUTSS) in China, the authors explored how to use 4000 taxis as the data collection sensors in Shenzhen, a southern city in China which borders Hong Kong. The authors introduce the procedures and algorithms for the computation and map-matching of road segment velocities to a digital road network. To superimpose the near real-time traffic information onto a digital map, coordinate transformation is required and the transformation precision is analyzed using field testing data. Due to the nature of FCD, continuous GPS data such as routing velocities and coordinates can be collected by any GPS equipped vehicle. Therefore, relevant algorithms are developed and utilized for the map-matching according to probability and statistical theories. To evaluate the reliability of proposed map-matching method, the confidence levels are calculated statistically, from which it can be determined whether the positioning data is valid or not with predefined threshold values. Furthermore, road segment velocity matching methods based on the Metropolis criteria is extended and relevant validation is carried out through the comparison of estimated and measured results. The major objective of this method is to obtain more accurate road segment travel time through the combination of those estimated by FCD and historical ones. This can significantly improve the reliability of instantaneous traffic information before its web publication. The final part of this paper introduces the architecture and the realization of a web Geographical Information System (GIS) and FCD-based instantaneous traffic information dissemination system for the whole of Shenzhen City.

Keywords

Global Positioning SystemFloating Car DataMap-matchingWebGIS
Type
Research Article
Information
The Journal of Navigation , Volume 61 , Issue 2 , April 2008 , pp. 337 - 353
Copyright
Copyright © The Royal Institute of Navigation 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Brakatsoulas, S., Pfoser, D., Salas, R. and Wenk, C. (2005b). On Map-Matching Vehicle Tracking Data. Proceedings of the 31st international conference on Very large data base, Trondheim, Norway.Google Scholar
Brakatsoulas, S., Pfoser, D. and Tryfona, N. (2005a). Practical Data Management Techniques for Vehicle Tracking Data. Proceedings of the 21st International Conference on Data Engineering, Tokyo, Japan, IEEE.Google Scholar
Fouladvand, M. E. and Darooneh, A. H. (2005). “Statisitcal Analysis of Floating-car Data: An Empirical Study.” The European Physical Journal B, 47, 319–328.CrossRefGoogle Scholar
Fukui, M. and Sugiyama, Y. (2002). Traffic and Granular Flow. Tokyo, Springer.Google Scholar
Gössel, F., Michler, E. and Wrase, B. (2003). Spectral analysis of Floating Car Data, Advances in Radio Science – Kleinheubacher Berichte, 1, 139–142.CrossRefGoogle Scholar
Hodges, D. (2005). The 2005 Update of Florida's Intelligent Transportation System Strategic Plan. Tallabassee, Florida, Florida Department of Transportaion Tranffic Engineering and Operations Office-Intelligent Transportation System (ITS) Section.Google Scholar
Kerner, B. S. (2004). In Physics of Traffic, Springer.CrossRefGoogle Scholar
Kerner, B. S., Demir, C., Herrtwich, R. G., Klenov, S. L., Rehborn, H., Aleksic, M. and Haug, A. (2005). Traffic state detection with floating car data in road networks. Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, Vienna, Austria.CrossRefGoogle Scholar
Lorkowski, S., Mieth, P. and Schäfer, R.-P. (2005). New ITS applications for metropolitan areas based on Floating Car Data. ECTRI Young Researcher Seminar, Den Haag (NL).Google Scholar
Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A. and Teller, E. (1953). Equation of State Calculations by Fast Computing Machines., J. Chem. Phys, 21(6), 1087–1092.CrossRefGoogle Scholar
Ochieng, W. Y., Quddus, M. A. and Noland, R. B. (2003). Map-Matching in Complex Urban Road Networks. Brazilian Journal of Cartography, 55(2), 1–18.Google Scholar
Quddus, M. A., Noland, R. B. and Ochieng, Y. W. (2005). Validation of Map Matching Algorithms using High Precision Positioning with GPS, Journal of Navigation, 58(2), 257–271.CrossRefGoogle Scholar
Tanokura, Y. and Electronics, N.. (2006). Honda Shows Traffic Data Using “Google Earth”.2006-3-30. http://techon.nikkeibp.co.jp/english/NEWS_EN/20060330/115577/?ST=englishGoogle Scholar
Turksma, S. (2000). The various uses of floating car data. Road Transport Information and Control, 2000. Tenth International Conference on (Conf. Publ. No. 472), London, Uk.CrossRefGoogle Scholar
Zhao, Y. (1997). Vehicle Location and Navigation Systems. Boston, Artech House.Google Scholar