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Transport System Performance Analysis with Advanced Sensing Technology

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References

  • Camps-Valls G, Gomez-Chova L, Munoz-Mari J, Rojo-Alvarez J, Martinez-Ramon M (2008) Kernel-based framework for multi-temporal and multi-source remote sensing data classification and change detection. IEEE Trans Geosci Remote Sens 46(6):1822–1835

    Article  Google Scholar 

  • Choi K, Chung Y (2002) A data fusion algorithm for estimating link travel time. J Intell Transp Syst 7(3):235–260

    Article  MATH  Google Scholar 

  • Chow AHF, Scarinci R, Heydecker BG (2013) Analysis of adaptive data fusion algorithm for urban network application. In: Proceedings of the 92nd annual meeting of the transportation research board, Washington, DC, pp 13–17

    Google Scholar 

  • Chow AHF, Santacreu A, Tsapakis I, Tanaksaranond G, Cheng T (2014) Empirical assessment of urban traffic congestion. J Adv Transp 48(8):1000–1016

    Article  Google Scholar 

  • Eddington R (2006) The Eddington transport study – transport’s role in sustaining the UK’s productivity and competitiveness. HMSO, London

    Google Scholar 

  • Her Majesty’s Treasury (HM Treasury, 2011) National Infrastructure Plan 2011

    Google Scholar 

  • Kwon J, Varaiya P (2005) The congestion pie: delay from collisions, potential ramp metering gain, and excess demand. In: Proceedings of the 84th annual meeting transportation research board, Washington, DC, 9–13 Jan 2005

    Google Scholar 

  • Lanckriet GR, Deng M, Cristianini N, Jordan M, Noble W (2004) Kernel-based data fusion and its application to protein function prediction in yeast. In: Proceedings of pacific symposium on biocomputing

    Google Scholar 

  • Olkin I (1992) Meta-analysis: methods for combining independent studies. Stat Sci 7(2):226–236

    Article  Google Scholar 

  • Robinson S, Polak J (2006) Overtaking rule method for the cleaning of matched license plate data. ASCE J Transp Eng 132(8):609–617

    Article  Google Scholar 

  • Tanaka H, Uejima S, Asai K (1982) Linear regression analysis with fuzzy model. IEEE Trans Syst Manag Cybern 12(6):903–907

    Article  MATH  Google Scholar 

  • Transport for London (TfL, 2011) What do I need to know about the central London congestion charge camera system? Technical report, Transport for London. www.tfl.gov.uk/assets/downloads/CC-Cameras.pdf. Accessed on: 20 Jan 2013

  • Treiber M, Helbing D (2002a) Reconstructing the spatio-temporal traffic dynamics from stationary detector data. Cooper Transp Dyn 1:3.1–3.24

    Google Scholar 

  • Treiber M, Helbing D (2002b) An adaptive smoothing method for traffic state identification from incomplete information. arXiv:cond-mat/0210050

    Google Scholar 

  • Treiber M, Kesting A, Wilson RE (2009) Reconstructing the traffic state from fusion of heterogeneous data. Comput-Aided Civil Infrastruct Eng 26(6):408–419

    Article  Google Scholar 

  • Tsapakis I, Turner J, Cheng T, Heydecker BG, Emmonds A, Bolbol A (2012) Effects of tube strikes on journey times in the transport network of London. Transp Res Rec 2274:84–92

    Article  Google Scholar 

Recommended Reading

  • Bolbol A, Tsapakis I, Cheng T, Chow AHF (2012) Sample size calculation for studying transportation modes from GPS data. Proc Soc Behav Sci 48:3040–3050

    Article  Google Scholar 

  • Chen C, Skabardonis A, Varaiya P (2004) Systematic identification of freeway bottlenecks. Transp Res Rec 1867:46–52

    Article  Google Scholar 

  • Department for Transport (DfT, 2012) Road lengths 2011 – statistical release. https://www.gov.uk/government/publications/road-lengths-statistics-in-great-britain-2011. Accessed on: 20 Jan 2013

  • Federal Highway Administration (FHWA, 2005) Traffic congestion and reliability – trends and advanced strategies for congestion mitigation. Final report prepared by Cambridge Systematics and Texas Transportation Institute

    Google Scholar 

  • Krishnamoorthy R (2008) Travel time estimation and forecasting on urban roads. Doctoral thesis, Imperial College London

    Google Scholar 

  • Kwon J, Mauch M, Varaiya P (2006) The components of congestion: delay from incidents, special events, lane closures, weather, potential ramp metering gain, and excess demand. In: Proceedings of the 85th annual meeting transportation research board, Washington, DC, 22–26 Jan

    Google Scholar 

  • MIDAS (2013) Motorway incident detection and automated signalling (MIDAS) system. http://www.midasinfo.co.uk/. Accessed on: 20 Jan 2013

  • Ou Q (2011) Fusing heterogeneous traffic data: parsimonious approaches using data-data consistency. PhD thesis, Delft University of Technology

    Google Scholar 

  • PeMS (2013) PeMS homepage. http://pems.dot.ca.gov/. Accessed on: 20 Jan 2013

  • Qiu T, Lu X, Chow AHF, Shladover S (2010) Estimation of freeway traffic density with loop detector and probe vehicle data. Transp Res Rec 2178:21–29

    Article  Google Scholar 

  • Robinson S (2005) The development and application of an urban link travel time model using data derived from inductive loop detectors. Doctoral thesis, Imperial College London

    Google Scholar 

  • Transport for London (TfL, 2008) Road network performance and research (RNPR) traffic note 1 – traffic levels on major roads in Greater London 1993–2007. www.tfl.gov.uk/assets/downloads/businessandpartners/traffic-levels-on-major-roads-1993-2007.pdf. Accessed on: 20 Jan 2013

  • Transport for London (TfL, 2012) Travel in London – report 5. www.tfl.gov.uk/assets/downloads/corporate/travel-in-london-report-5.pdf. Accessed on: 20 Jan 2013

  • Van Lint JWC (2010) Empirical evaluation of new robust travel time estimation algorithms. Transp Res Rec 2160:50–59

    Article  Google Scholar 

  • Van Lint JWC, Hoogendoorn SP (2009) A robust and efficient method for fusing heterogeneous data from traffic sensors on freeways. Comput-Aided Civil Infrastruct Eng 25(8):596–612

    Article  Google Scholar 

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Acknowledgements

The study was carried out under the STANDARD project (2009–2012) which was funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under Research Grant EP/G023212/1 led by Tao Cheng. The author would like to thank UK Transport for London (TfL) for providing the traffic data and the constructive comments. The contents of this report reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not reflect the official views or policies of TfL or any other organizations.

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Correspondence to Andy H. F. Chow .

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Chow, A.H.F. (2017). Transport System Performance Analysis with Advanced Sensing Technology. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_1612

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