IDEAS home Printed from https://ideas.repec.org/h/elg/eechap/13831_17.html
   My bibliography  Save this book chapter

A Large-scale Urban Traffic Decision Support System with Dynamic Traffic Assignment

In: New Developments in Transport Planning

Author

Listed:
  • Yusen Chen
  • Henk J. van Zuylen
  • Wim van der Hoeven

Abstract

Traffic assignment is a set of criteria through which the demand for mobility is distributed over the links of a transport network. Over the last 30 years, Dynamic Traffic Assignment (DTA) models have been developed to support time-dependent analyses in nascent fields that need to take into account the temporal distribution of demand and supply. In this book, leading international experts in the field provide a state-of-the-art overview of fundamental DTA research and practice, identifying weaknesses and major challenges for future research.

Suggested Citation

  • Yusen Chen & Henk J. van Zuylen & Wim van der Hoeven, 2010. "A Large-scale Urban Traffic Decision Support System with Dynamic Traffic Assignment," Chapters, in: Chris M.J. Tampere & Francesco Viti & Lambertus H. (Ben) Immers (ed.), New Developments in Transport Planning, chapter 17, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:13831_17
    as

    Download full text from publisher

    File URL: https://www.elgaronline.com/view/9781848449633.00027.xml
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ennio Cascetta & Domenico Inaudi & Gérald Marquis, 1993. "Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts," Transportation Science, INFORMS, vol. 27(4), pages 363-373, November.
    2. M. Bierlaire & F. Crittin, 2004. "An Efficient Algorithm for Real-Time Estimation and Prediction of Dynamic OD Tables," Operations Research, INFORMS, vol. 52(1), pages 116-127, February.
    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. Flurin S. Hänseler & Nicholas A. Molyneaux & Michel Bierlaire, 2017. "Estimation of Pedestrian Origin-Destination Demand in Train Stations," Transportation Science, INFORMS, vol. 51(3), pages 981-997, August.
    2. Yong Lin, 2023. "Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System," Sustainability, MDPI, vol. 15(2), pages 1-30, January.
    3. Kumarage, Sakitha & Yildirimoglu, Mehmet & Zheng, Zuduo, 2023. "A hybrid modelling framework for the estimation of dynamic origin–destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    4. Cantelmo, Guido & Qurashi, Moeid & Prakash, A. Arun & Antoniou, Constantinos & Viti, Francesco, 2020. "Incorporating trip chaining within online demand estimation," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 171-187.
    5. Chao Sun & Yulin Chang & Xin Luan & Qiang Tu & Wenyun Tang, 2020. "Origin-Destination Demand Reconstruction Using Observed Travel Time under Congested Network," Networks and Spatial Economics, Springer, vol. 20(3), pages 733-755, September.
    6. D'Acierno, Luca & Cartenì, Armando & Montella, Bruno, 2009. "Estimation of urban traffic conditions using an Automatic Vehicle Location (AVL) System," European Journal of Operational Research, Elsevier, vol. 196(2), pages 719-736, July.
    7. Huo, Jinbiao & Liu, Chengqi & Chen, Jingxu & Meng, Qiang & Wang, Jian & Liu, Zhiyuan, 2023. "Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    8. Osorio, Carolina & Punzo, Vincenzo, 2019. "Efficient calibration of microscopic car-following models for large-scale stochastic network simulators," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 156-173.
    9. A. Stathopoulos & T. Tsekeris, 2003. "Framework for analysing reliability and information degradation of demand matrices in extended transport networks," Transport Reviews, Taylor & Francis Journals, vol. 23(1), pages 89-103, January.
    10. M. Bierlaire & F. Crittin, 2004. "An Efficient Algorithm for Real-Time Estimation and Prediction of Dynamic OD Tables," Operations Research, INFORMS, vol. 52(1), pages 116-127, February.
    11. Van Der Zijpp, Nanne J. & De Romph, Erik, 1997. "A dynamic traffic forecasting application on the Amsterdam beltway," International Journal of Forecasting, Elsevier, vol. 13(1), pages 87-103, March.
    12. Chu, Lianyu & Liu, Henry X. & Recker, Will & Hague, Steve, 2003. "Evaluation of Potential ITS Strategies Under Non-Recurrent Congestion Using Microscopic Simulation," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt74f7f2x0, Institute of Transportation Studies, UC Berkeley.
    13. Osorio, Carolina, 2019. "High-dimensional offline origin-destination (OD) demand calibration for stochastic traffic simulators of large-scale road networks," Transportation Research Part B: Methodological, Elsevier, vol. 124(C), pages 18-43.
    14. Tangjian Wei & Feng Shi & Guangming Xu, 2019. "Estimation of Time-Varying Passenger Demand for High Speed Rail System," Complexity, Hindawi, vol. 2019, pages 1-24, March.
    15. Hu, Shou-Ren & Peeta, Srinivas & Chu, Chun-Hsiao, 2009. "Identification of vehicle sensor locations for link-based network traffic applications," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 873-894, September.
    16. K. Ashok & M. E. Ben-Akiva, 2000. "Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin–Destination Flows," Transportation Science, INFORMS, vol. 34(1), pages 21-36, February.
    17. Michel Bierlaire & Frank Crittin, 2006. "Solving Noisy, Large-Scale Fixed-Point Problems and Systems of Nonlinear Equations," Transportation Science, INFORMS, vol. 40(1), pages 44-63, February.
    18. Camus, Roberto & Cantarella, Giulio E. & Inaudi, Domenico, 1997. "Real-time estimation and prediction of origin--destination matrices per time slice," International Journal of Forecasting, Elsevier, vol. 13(1), pages 13-19, March.
    19. Wu, Jifeng & Chang, Gang-Len, 1996. "Estimation of time-varying origin-destination distributions with dynamic screenline flows," Transportation Research Part B: Methodological, Elsevier, vol. 30(4), pages 277-290, August.
    20. Anselmo Ramalho Pitombeira-Neto & Carlos Felipe Grangeiro Loureiro & Luis Eduardo Carvalho, 2020. "A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks," Networks and Spatial Economics, Springer, vol. 20(2), pages 499-527, June.

    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:elg:eechap:13831_17. 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: Darrel McCalla (email available below). General contact details of provider: http://www.e-elgar.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.