IDEAS home Printed from https://ideas.repec.org/a/vrs/buogeo/v30y2015i30p123-134n9.html
   My bibliography  Save this article

Assessment of network traffic congestion through Traffic Congestability Value (TCV): a new index

Author

Listed:
  • Patel Nilanchal
  • Mukherjee Alok Bhushan

    (Birla Institute of Technology, Mesra, Ranchi, Department of Remote Sensing, 835215, Jharkhand, India)

Abstract

Traffic congestion is a major and growing problem in urban areas across the globe. It reduces the effective spatial interaction between different locations. To mitigate traffic congestion, not only the actual status of different routes needs to be known but also it is imperative to determine network congestion in different spatial zones associated with distinct land use classes. In the present paper, a new formula is proposed to quantify traffic congestion in the different spatial zones of a study area characterized by distinct land use classes. The proposed formula is termed the Traffic Congestability Value (TCV). The formula considers three major influencing factors: congestion index value, pedestrian movement and road surface conditions; since these parameters are significantly related to land use in a region. The different traffic congestion parameters, i.e. travel time, average speed and the proportion of time stopped, were collected in real time. Lower values of TCV correspond to a higher degree of congestion in the respective spatial zones and vice-versa and the results were validated in the field. TCV differs from the previous approaches to quantifying traffic congestion since it focuses on the causes of network congestion while in previous works the focus was generally on link flow congestion.

Suggested Citation

  • Patel Nilanchal & Mukherjee Alok Bhushan, 2015. "Assessment of network traffic congestion through Traffic Congestability Value (TCV): a new index," Bulletin of Geography. Socio-economic Series, Sciendo, vol. 30(30), pages 123-134, December.
    • Handle: RePEc:vrs:buogeo:v:30:y:2015:i:30:p:123-134:n:9
    DOI: 10.1515/bog-2015-0039
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/bog-2015-0039
    Download Restriction: no
    File URL: https://libkey.io/10.1515/bog-2015-0039?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Felipe Jiménez & Wilmar Cabrera-Montiel, 2014. "System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information," Energies, MDPI, vol. 7(6), pages 1-23, June.
    2. Zengwang Xu & Daniel Sui, 2007. "Small-world characteristics on transportation networks: a perspective from network autocorrelation," Journal of Geographical Systems, Springer, vol. 9(2), pages 189-205, June.
    3. Rajagopalan, S. & Yu, Hung-Liang, 2001. "Capacity planning with congestion effects," European Journal of Operational Research, Elsevier, vol. 134(2), pages 365-377, October.
    4. C. Robin Lindsey & Erik T. Verhoef, 2000. "Traffic Congestion and Congestion Pricing," Tinbergen Institute Discussion Papers 00-101/3, Tinbergen Institute.
    5. K. Triantis & S. Sarangi & D. Teodorović & L. Razzolini, 2011. "Traffic congestion mitigation: combining engineering and economic perspectives," Transportation Planning and Technology, Taylor & Francis Journals, vol. 34(7), pages 637-645, April.
    6. Ryley, Tim J. & Zanni, Alberto M., 2013. "An examination of the relationship between social interactions and travel uncertainty," Journal of Transport Geography, Elsevier, vol. 31(C), pages 249-257.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Richard Connors & David Watling, 2015. "Assessing the Demand Vulnerability of Equilibrium Traffic Networks via Network Aggregation," Networks and Spatial Economics, Springer, vol. 15(2), pages 367-395, June.
    2. Julie Bulteau & Thierry Feuillet & Sophie Dantan & Souhir Abbes, 2023. "Encouraging carpooling for commuting in the Paris area (France): which incentives and for whom?," Transportation, Springer, vol. 50(1), pages 43-62, February.
    3. Kamath, Narasimha B. & Roy, Rahul, 2007. "Capacity augmentation of a supply chain for a short lifecycle product: A system dynamics framework," European Journal of Operational Research, Elsevier, vol. 179(2), pages 334-351, June.
    4. Nilanchal PATEL & Alok Bhushan MUKHERJEE, 2014. "Categorization Of Urban Traffic Congestion Based On The Fuzzification Of Congestion Index Value And Influencing Parameters," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 9(4), pages 36-51, November.
    5. Tao Cheng & James Haworth & Jiaqiu Wang, 2012. "Spatio-temporal autocorrelation of road network data," Journal of Geographical Systems, Springer, vol. 14(4), pages 389-413, October.
    6. Tanzina Afrin & Nita Yodo, 2020. "A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System," Sustainability, MDPI, vol. 12(11), pages 1-23, June.
    7. Gregory Stock & Noel Greis & William Fischer, 2018. "Organisational Slack And New Product Time To Market Performance," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-34, May.
    8. Pascal Courty & Mario Pagliero, 2011. "Does responsive pricing smooth demand shocks?," Applied Economics, Taylor & Francis Journals, vol. 43(30), pages 4707-4721.
    9. Aldrich, Preston R. & El-Zabet, Jermeen & Hassan, Seerat & Briguglio, Joseph & Aliaj, Enela & Radcliffe, Maria & Mirza, Taha & Comar, Timothy & Nadolski, Jeremy & Huebner, Cynthia D., 2015. "Monte Carlo tests of small-world architecture for coarse-grained networks of the United States railroad and highway transportation systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 32-39.
    10. Beheshtian, Arash & Richard Geddes, R. & Rouhani, Omid M. & Kockelman, Kara M. & Ockenfels, Axel & Cramton, Peter & Do, Wooseok, 2020. "Bringing the efficiency of electricity market mechanisms to multimodal mobility across congested transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 58-69.
    11. Li, Shengxiao & Zhao, Pengjun & Zhang, He & Quan, Jing, 2019. "Walking behavior in the old downtown Beijing: The impact of perceptions and attitudes and social variations," Transport Policy, Elsevier, vol. 73(C), pages 1-11.
    12. Boffey, Brian & Galvao, Roberto & Espejo, Luis, 2007. "A review of congestion models in the location of facilities with immobile servers," European Journal of Operational Research, Elsevier, vol. 178(3), pages 643-662, May.
    13. Odeck, James & Aasness, Marie Aarestrup, 2024. "What are the determinants of road users' experiences with congestion: Econometric assessment using ordered response models," Research in Transportation Economics, Elsevier, vol. 104(C).
    14. Ronnie Schöb, 2005. "Not optimal, but Effective: the Multi-Mode Ticket for Reducing Urban Traffic Congestion in Medium-Sized Towns," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 3(03), pages 28-33, November.
    15. Börjesson, Maria & Brundell-Freij, Karin & Eliasson, Jonas, 2014. "Not invented here: Transferability of congestion charges effects," Transport Policy, Elsevier, vol. 36(C), pages 263-271.
    16. Li, Sen & Yang, Hai & Poolla, Kameshwar & Varaiya, Pravin, 2021. "Spatial pricing in ride-sourcing markets under a congestion charge," Transportation Research Part B: Methodological, Elsevier, vol. 152(C), pages 18-45.
    17. López, Fernando A. & Páez, Antonio & Carrasco, Juan A. & Ruminot, Natalia A., 2017. "Vulnerability of nodes under controlled network topology and flow autocorrelation conditions," Journal of Transport Geography, Elsevier, vol. 59(C), pages 77-87.
    18. Yingying Xing & Jian Lu & Shengdi Chen & Sunanda Dissanayake, 2017. "Vulnerability analysis of urban rail transit based on complex network theory: a case study of Shanghai Metro," Public Transport, Springer, vol. 9(3), pages 501-525, October.
    19. Farokhi, Farhad & Johansson, Karl H., 2015. "A piecewise-constant congestion taxing policy for repeated routing games," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 123-143.
    20. Andrew Allman & Qi Zhang, 2021. "Branch-and-price for a class of nonconvex mixed-integer nonlinear programs," Journal of Global Optimization, Springer, vol. 81(4), pages 861-880, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:buogeo:v:30:y:2015:i:30:p:123-134:n:9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.