Skip to main content
Springer Nature Link
Log in

Transport System Performance Analysis with Advanced Sensing Technology

  • Reference work entry
  • First Online:
Encyclopedia of GIS

  • 154 Accesses

Introduction

The ever-increasing demand for travel raises various problems and issues including congestion, energy, environmental impact, safety, and security. The UK Eddington study (Eddington 2006) states that the monetary cost due to road congestion will reach £22 billion (at 2002 prices) per annum for all road users by 2025, in which 13% of road traffic will be subject to stop-start travel conditions. In a report published in 2009, UK Department for Transport (DfT) also suggests that congestion across the English road network as a whole will increase from 2003 levels by 27% by 2025 and 54% by 2035 (HM Treasury 2011). Continuous construction of new roads will not be a sustainable solution due to the increasingly tight fiscal, physical, and environmental constraints. Consequently, governments, businesses, and research teams around the world want to explore alternative ways to effectively utilize and manage existing road infrastructure.

A detailed and reliable performance evaluation...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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 

Download references

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.

Author information

Authors and Affiliations

  1. Centre for Transport Studies, University College London, WC1E 6BT, London, UK

    Andy H. F. Chow

Authors
  1. Andy H. F. Chow
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andy H. F. Chow .

Editor information

Editors and Affiliations

  1. University of Minnesota, Minneapolis, MN, USA

    Shashi Shekhar

  2. Management Science and Information Systems Department, Rutgers Business School, Rutgers, The State University of New Jersey, Newark, NJ, USA

    Hui Xiong

  3. Department of Management Sciences, Tippie College of Business, University of Iowa, Iowa City, IA, USA

    Xun Zhou

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this entry

Cite this entry

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

Download citation

Publish with us

Policies and ethics