American Institute of Mathematical Sciences

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

Visualization analysis of traffic congestion based on floating car data

Jingmei Zhou 1, , Xiangmo Zhao 1, , Xin Cheng 1, and  Zhigang Xu 1,

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.
Keywords: FCD, map matching, speed estimation., Visualization analysis, traffic congestion.
Mathematics Subject Classification: Primary: 94A08, 90B20; Secondary: 60K3.
Citation: Jingmei Zhou, Xiangmo Zhao, Xin Cheng, Zhigang Xu. Visualization analysis of traffic congestion based on floating car data. Discrete and Continuous Dynamical Systems - S, 2015, 8 (6) : 1423-1433. doi: 10.3934/dcdss.2015.8.1423
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M. Bierlaire, J. Chen and J. Newman, A probabilistic map matching method for smartphone GPS data, Transportation Research Part C Emerging Technologies, 26 (2013), 78-98. doi: 10.1016/j.trc.2012.08.001.

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M. Y. Cao and D. Y. Yu, Pseudocolor coding of gray image based on perceptual color space, Optical Technology, 28 (2002), 338-367.

[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), 22-38. doi: 10.1080/13658816.2013.816427.

[4]

F. Chen, M. Shen and Y. Tang, Local path searching based map matching algorithm for floating car data, Procedia Environmental Sciences, 10 (2011), 576-582. doi: 10.1016/j.proenv.2011.09.093.

[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.

[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), 160-172. doi: 10.1016/j.trc.2014.07.005.

[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, International Society for Optics and Photonics, 2012, 842519. doi: 10.1117/12.977197.

[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), 638-644.

[9]

Y. Lou, C. Zhang and Y. Zheng, et al., Map-matching for low-sampling-rate GPS trajectories, ACM SIGSPATIAL GIS 2009, 2009, 352-361. doi: 10.1145/1653771.1653820.

[10]

S. Messelodi, M. C. Modena and M. Zanin, Intelligent extended floating car data collection, Expert Systems with Applications, 36 (2009), 4213-4227. doi: 10.1016/j.eswa.2008.04.008.

[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), 132-145. doi: 10.1016/j.trc.2012.01.005.

[12]

M. A. Quddus, W. Y. Ochieng and L. Zhao, A general map matching algorithm for transport telematics applications, GPS Solutions, 7 (2003), 157-167. doi: 10.1007/s10291-003-0069-z.

[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), 672-683. doi: 10.1016/j.trc.2009.05.008.

[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), 1670-1678. doi: 10.1016/j.sbspro.2013.08.189.

[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), 2561-2573.

[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), 114-127. doi: 10.1016/j.trc.2014.08.001.

[17]

Y. C. Zhang, X. Q. Zuo and L. T. Zhang, Traffic congestion detection based on GPS floating-car data, Procedia Engineering, 15 (2011), 5541-5546.

[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.

[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.

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), 78-98. doi: 10.1016/j.trc.2012.08.001.

[2]

M. Y. Cao and D. Y. Yu, Pseudocolor coding of gray image based on perceptual color space, Optical Technology, 28 (2002), 338-367.

[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), 22-38. doi: 10.1080/13658816.2013.816427.

[4]

F. Chen, M. Shen and Y. Tang, Local path searching based map matching algorithm for floating car data, Procedia Environmental Sciences, 10 (2011), 576-582. doi: 10.1016/j.proenv.2011.09.093.

[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.

[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), 160-172. doi: 10.1016/j.trc.2014.07.005.

[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, International Society for Optics and Photonics, 2012, 842519. doi: 10.1117/12.977197.

[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), 638-644.

[9]

Y. Lou, C. Zhang and Y. Zheng, et al., Map-matching for low-sampling-rate GPS trajectories, ACM SIGSPATIAL GIS 2009, 2009, 352-361. doi: 10.1145/1653771.1653820.

[10]

S. Messelodi, M. C. Modena and M. Zanin, Intelligent extended floating car data collection, Expert Systems with Applications, 36 (2009), 4213-4227. doi: 10.1016/j.eswa.2008.04.008.

[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), 132-145. doi: 10.1016/j.trc.2012.01.005.

[12]

M. A. Quddus, W. Y. Ochieng and L. Zhao, A general map matching algorithm for transport telematics applications, GPS Solutions, 7 (2003), 157-167. doi: 10.1007/s10291-003-0069-z.

[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), 672-683. doi: 10.1016/j.trc.2009.05.008.

[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), 1670-1678. doi: 10.1016/j.sbspro.2013.08.189.

[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), 2561-2573.

[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), 114-127. doi: 10.1016/j.trc.2014.08.001.

[17]

Y. C. Zhang, X. Q. Zuo and L. T. Zhang, Traffic congestion detection based on GPS floating-car data, Procedia Engineering, 15 (2011), 5541-5546.

[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.

[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.

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