December  2015, 8(6): 1423-1433. doi: 10.3934/dcdss.2015.8.1423

Visualization analysis of traffic congestion based on floating car data

1. 

School of Information Engineering, Chang’an University, Xi’an, Shaanxi 710064, China, China, China, China

Received  May 2015 Revised  September 2015 Published  December 2015

Traffic congestion visualization is an important part in traffic information service. However, the real-time data is difficult to obtain and its analysis method is not accurate, so the reliability of congestion state visualization is low. This paper proposes a visualization analysis algorithm of traffic congestion based on Floating Car Data (FCD), which utilizes the FCD to estimate and display dynamic traffic state on the electronic map. Firstly, an improved map matching method is put forward to match rapidly the FCD with road sections, which includes two steps of coarse and precise matching. Then, the traffic speed is estimated and classified to display different traffic states. Eventually, multi-group experiments have been conducted based on more than 8000 taxies in Xi’an. The experimental results show that FCD can be matched accurately with the selected road sections which accuracy can reach up to \({\rm{96\% }}\), and the estimated traffic real-time state can achieve \({\rm{94\% }}\) in terms of reliability. So this visualization analysis algorithm can display accurately road traffic state in real time.
Citation: Jingmei Zhou, Xiangmo Zhao, Xin Cheng, Zhigang Xu. Visualization analysis of traffic congestion based on floating car data. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1423-1433. doi: 10.3934/dcdss.2015.8.1423
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M. A. Quddus, W. Y. Ochieng and L. Zhao, A general map matching algorithm for transport telematics applications,, GPS Solutions, 7 (2003), 157.  doi: 10.1007/s10291-003-0069-z.  Google Scholar

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N. R. Velaga and A. L. Bristow, Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems,, Transportation Research Part C Emerging Technologies, 17 (2009), 672.  doi: 10.1016/j.trc.2009.05.008.  Google Scholar

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H. Yang, S. Cheng and H. Jiang, An enhanced weight-based topological map matching algorithm for intricate urban road network,, Procedia Social and Behavioral Sciences, 96 (2013), 1670.  doi: 10.1016/j.sbspro.2013.08.189.  Google Scholar

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J. S. Yang, S. P. Kang and K. S. Chon, The map matching algorithm of GPS data with relatively long polling time intervals,, Journal of the Eastern Asia Society for Transportation Studies, 6 (2005), 2561.   Google Scholar

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Q. Yuan, Z. Liu and J. Li, A traffic congestion detection and information dissemination scheme for urban expressways using vehicular networks,, Transportation Research Part C Emerging Technologies, 47 (2014), 114.  doi: 10.1016/j.trc.2014.08.001.  Google Scholar

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Y. C. Zhang, X. Q. Zuo and L. T. Zhang, Traffic congestion detection based on GPS floating-car data,, Procedia Engineering, 15 (2011), 5541.   Google Scholar

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Y. Zheng and M. A. Cheng, Weight-based shortest-path aided map matching algorithm for low-frequency positioning data,, Transportation Research Board Meeting, (2011).   Google Scholar

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S. Zhong, X. Y. Jiang and Z. Wei, Pseudo-Color Coding with phase modulated image density,, in International Conference on Micro/Nano Optical Engineering (ICOME), (2011).   Google Scholar

show all references

References:
[1]

M. Bierlaire, J. Chen and J. Newman, A probabilistic map matching method for smartphone GPS data,, Transportation Research Part C Emerging Technologies, 26 (2013), 78.  doi: 10.1016/j.trc.2012.08.001.  Google Scholar

[2]

M. Y. Cao and D. Y. Yu, Pseudocolor coding of gray image based on perceptual color space,, Optical Technology, 28 (2002), 338.   Google Scholar

[3]

B. Y. Chen, H. Yuan and Q. Lam, Map-matching algorithm for large-scale low-frequency floating car data,, International Journal of Geographical Information Science, 28 (2014), 22.  doi: 10.1080/13658816.2013.816427.  Google Scholar

[4]

F. Chen, M. Shen and Y. Tang, Local path searching based map matching algorithm for floating car data,, Procedia Environmental Sciences, 10 (2011), 576.  doi: 10.1016/j.proenv.2011.09.093.  Google Scholar

[5]

J. S. Greenfeld, Matching GPS observations to locations on a digital map,, in Proceedings of Annual Meeting of the Transportation Research Board Washington D C., (2002).   Google Scholar

[6]

J. Guo, W. Huang and B. M. Williams, Real time traffic flow outlier detection using short-term traffic conditional variance prediction,, Transportation Research Part C Emerging Technologies, 50 (2015), 160.  doi: 10.1016/j.trc.2014.07.005.  Google Scholar

[7]

J. Hu, X. Peng and Z. Xu, Study of gray image pseudo-color processing algorithms,, in Proc. SPIE 8415 International Symposium on Advanced Optical Manufacturing and Testing Technologies: Large Mirrors and Telescopes, (8415).  doi: 10.1117/12.977197.  Google Scholar

[8]

G. Y. Jiang, A. D. Chang and Q. Li, Estimation models for average speed of traffic flow based on GPS data of taxi,, Journal of Southwest Jiaotong University, 46 (2011), 638.   Google Scholar

[9]

Y. Lou, C. Zhang and Y. Zheng, et al., Map-matching for low-sampling-rate GPS trajectories,, ACM SIGSPATIAL GIS 2009, (2009), 352.  doi: 10.1145/1653771.1653820.  Google Scholar

[10]

S. Messelodi, M. C. Modena and M. Zanin, Intelligent extended floating car data collection,, Expert Systems with Applications, 36 (2009), 4213.  doi: 10.1016/j.eswa.2008.04.008.  Google Scholar

[11]

T. Miwa, D. Kiuchi and T. Yamamoto, Development of map matching algorithm for low frequency probe data,, Transportation Research Part C Emerging Technologies, 22 (2012), 132.  doi: 10.1016/j.trc.2012.01.005.  Google Scholar

[12]

M. A. Quddus, W. Y. Ochieng and L. Zhao, A general map matching algorithm for transport telematics applications,, GPS Solutions, 7 (2003), 157.  doi: 10.1007/s10291-003-0069-z.  Google Scholar

[13]

N. R. Velaga and A. L. Bristow, Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems,, Transportation Research Part C Emerging Technologies, 17 (2009), 672.  doi: 10.1016/j.trc.2009.05.008.  Google Scholar

[14]

H. Yang, S. Cheng and H. Jiang, An enhanced weight-based topological map matching algorithm for intricate urban road network,, Procedia Social and Behavioral Sciences, 96 (2013), 1670.  doi: 10.1016/j.sbspro.2013.08.189.  Google Scholar

[15]

J. S. Yang, S. P. Kang and K. S. Chon, The map matching algorithm of GPS data with relatively long polling time intervals,, Journal of the Eastern Asia Society for Transportation Studies, 6 (2005), 2561.   Google Scholar

[16]

Q. Yuan, Z. Liu and J. Li, A traffic congestion detection and information dissemination scheme for urban expressways using vehicular networks,, Transportation Research Part C Emerging Technologies, 47 (2014), 114.  doi: 10.1016/j.trc.2014.08.001.  Google Scholar

[17]

Y. C. Zhang, X. Q. Zuo and L. T. Zhang, Traffic congestion detection based on GPS floating-car data,, Procedia Engineering, 15 (2011), 5541.   Google Scholar

[18]

Y. Zheng and M. A. Cheng, Weight-based shortest-path aided map matching algorithm for low-frequency positioning data,, Transportation Research Board Meeting, (2011).   Google Scholar

[19]

S. Zhong, X. Y. Jiang and Z. Wei, Pseudo-Color Coding with phase modulated image density,, in International Conference on Micro/Nano Optical Engineering (ICOME), (2011).   Google Scholar

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