An Algorithm for Bus Trajectory Extraction Based on Incomplete Data Source

Article Contents

Article Navigation > Chinese Journal of Electronics > 2012 > 21(4): 599-603

“An Algorithm for Bus Trajectory Extraction Based on Incomplete Data Source,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 599-603, 2012,

Citation:

“An Algorithm for Bus Trajectory Extraction Based on Incomplete Data Source,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 599-603, 2012,

“An Algorithm for Bus Trajectory Extraction Based on Incomplete Data Source,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 599-603, 2012,

Citation:

“An Algorithm for Bus Trajectory Extraction Based on Incomplete Data Source,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 599-603, 2012,

PDF( 253 KB)

An Algorithm for Bus Trajectory Extraction Based on Incomplete Data Source

Received Date: 2011-12-01
Rev Recd Date: 2012-02-01
Publish Date: 2012-10-25

Abstract

Abstract

As the transportation system usage grows, there remains of some challenges. On the one hand, the emergence of intelligent transportation system becomes increasingly attractive; on the other hand, researchers are hardly to access real-time and dynamic information about traffics. In this paper, a new solved approach is designed. At first, real-time data on incomplete public traffics are collected via Internet. Then to achieve reliable extracting bus trajectory, the algorithms are proposed that consists of two aspects: trajectory fragments are generated based strong correlation of original data and trajectory fragments are connected with minimum connection distance. To the best of our knowledge, there are the first results for solving these problems, which will lay on data basis for subsequent traffic forecast. At the same time, the proposed algorithm on eliminating abnormal data and tackle with mass data effectively will provide new knowledge and experience for similar research areas.

FullText(HTML)

References(14)

References

Hilmi Berk Celikoglu, “Travel time measure specification byfunctional approximation: application of radial basis functionneural networks”, Procedia-Social and Behavioral Sciences,Vol.20, pp.613-620, 2011.

YibingWang, Markos Papageorgiou, AlbertMessmer et al., “Anadaptive freeway traffic state estimator”, Automatica, Vol.45,No.1, pp.10-24, 2009.

Ronghui Liu, Tony May, Simon Shepherd, “On the fundamentaldiagram and supply curves for congested urban networks”,Procedia-Social and Behavioral Sciences, Vol.17, pp.229-246,2011.

A. Sumalee, R.X. Zhong, T.L. Pan et al., “Stochastic cell transmissionmodel (SCTM): a stochastic dynamic traffic modelfor traffic state surveillance”, Transportation Research Part B:Methodological, Vol.45, No.3, pp.507-533, 2011.

Michael D. Fontaine, Brian L. Smith, “Investigation of the performanceof wireless location technology-based traffic monitorsystem”, Journal of Transportation Engineering, Vol.133, No.3,pp.157-165, 2007.

Henry X. Liu, Wenteng Ma, “A virtual vehicle probe modelfor time-dependent travel time estimation on signalized arterials”,Transportation Research Part C: Emerging Technologies,Vol.17, No.1, pp.11-26, 2009.

Karric Kwong, Robert Kavaler, Ram Rajagopal et al., “Arterialtravel time estimation based on vehicle re-identification usingwireless magnetic sensors”, Transportation Research PartC: Emerging Technologies, Vol.17, No.6, pp.586-606, 2009.

Qing Ou, R.L. Bertini, J.W.C. van Lint et al., “A theoreticalframework for traffic speed estimation by fusing low-resolutionprobe vehicle data”, IEEE Transactions on Intelligent TransportationSystems, Vol.12, No.3, pp.747-756, 2011.

Jie Li, Henk van Zuylen, Chunhua Liu et al., “Monitoring traveltimes in an urban network using video, GPS and Bluetooth”,Procedia-Social and Behavioral Sciences, Vol.20, pp.630-637,2011.

R.P.S. Padmanaban, K. Divakar, L. Vanajakshi et al., “Developmentof a real-time bus arrival prediction system for Indiantraffic conditions”, IET Intelligent Transport Systems, Vol.4,No.3, pp.189-200, 2010.

Bin Yu, William H.K. Lam, Mei Lam Tam, “Bus arrival timeprediction at bus stop with multiple routes”, Transportation ResearchPart C: Emerging Technologies, Vol.19, No.6, pp.1157-1170, 2011.

Bin Yu, Zhong Zhen Yang, Jing Wang, “Bus travel-time predictionbased on bus speed”, Proceedings of the Institution ofCivil Engineers: Transport, Vol.163, No.1, pp.3-7, 2010.

Bing Zhang, Huanzhang Lu, “The detection algorithm for movingpoint target trajectory in image sequences”, Acta ElectronicaSinica, Vol.32, No.9, pp.1524-1256, 2004. (in Chinese)

Zhipei Huang, Shuyan Sun, Jiankang Wu et al., “Dense multitargetadaptive video tracking based on signature”, Acta ElectronicaSinica, Vol.39, No.3A, pp.37-42, 2011. (in Chinese)

Relative Articles

Supplements(0)

Cited By

Proportional views