IDEAS home Printed from https://ideas.repec.org/p/cdl/itsrrp/qt1t95h8s2.html
   My bibliography  Save this paper

Hybrid Traffic Data Collection Roadmap: Objectives and Methods

Author

Listed:
  • Bayen, Alexandre

Abstract

Traffic data is used to estimate current traffic conditions so that travelers and agencies can make better decisions about how to use and manage the transportation network. This research explores the fusion of probe data (vehicle speed and direction) with loop data (density, speed, and count) in the context of producing overall network speed and travel time estimates. Speed and travel time estimates are useful in many circumstances, but current system control strategies (ramp metering, for example) require density data. While it is difficult to significantly increase the quantity of loop detectors on state highways, the penetration rate of probe data is continually increasing. Multiple data sources with various characteristics were fused by running probe and loop data through the Mobile Millennium highway model, generating velocity maps and travel times. The performance of data sources both individually and when fused was evaluated. It was found that the highest quality estimates are achieved by combining probe data and loop detector data.

Suggested Citation

  • Bayen, Alexandre, 2013. "Hybrid Traffic Data Collection Roadmap: Objectives and Methods," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt1t95h8s2, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt1t95h8s2
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/1t95h8s2.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daganzo, Carlos F., 1995. "The cell transmission model, part II: Network traffic," Transportation Research Part B: Methodological, Elsevier, vol. 29(2), pages 79-93, April.
    2. Daganzo, Carlos F., 1994. "The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory," Transportation Research Part B: Methodological, Elsevier, vol. 28(4), pages 269-287, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Khan, Sakib Mahmud PhD & Fournier, Nicholas PhD & Mauch, Michael PhD & Patire, Anthony D PhD & Skabardonis, Alex PhD, 2020. "Hybrid Data Implementation: Final Report for Task Number 3643," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt32b6s0fk, Institute of Transportation Studies, UC Berkeley.
    2. Dion, Francois PhD & Yang, Mingyuan & Patire, Anthony PhD, 2023. "New Data and Methods for Estimating Regional Truck Movements," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4cw749bp, Institute of Transportation Studies, UC Berkeley.
    3. Mauch, Michael & Skabardonis, Alex, 2016. "California’s Freeway Service Patrol Program: FSP Beat Evaluation Model; Methodology and Parameter Estimation (FY 2014-15)," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt90r29164, Institute of Transportation Studies, UC Berkeley.

    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. Qixiu Cheng & Zhiyuan Liu & Feifei Liu & Ruo Jia, 2017. "Urban dynamic congestion pricing: an overview and emerging research needs," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(0), pages 3-18, August.
    2. Gentile, Guido & Meschini, Lorenzo & Papola, Natale, 2007. "Spillback congestion in dynamic traffic assignment: A macroscopic flow model with time-varying bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 41(10), pages 1114-1138, December.
    3. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    4. Huanping Li & Jian Wang & Guopeng Bai & Xiaowei Hu, 2021. "Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads," Energies, MDPI, vol. 14(12), pages 1-21, June.
    5. Herrera, Juan C. & Bayen, Alexandre M., 2010. "Incorporation of Lagrangian measurements in freeway traffic state estimation," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 460-481, May.
    6. Bellei, Giuseppe & Gentile, Guido & Papola, Natale, 2005. "A within-day dynamic traffic assignment model for urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 39(1), pages 1-29, January.
    7. Georgia Perakis & Guillaume Roels, 2006. "An Analytical Model for Traffic Delays and the Dynamic User Equilibrium Problem," Operations Research, INFORMS, vol. 54(6), pages 1151-1171, December.
    8. Malachy Carey & Paul Humphreys & Marie McHugh & Ronan McIvor, 2018. "Consistency and Inconsistency Between the Fundamental Relationships on Which Different Traffic Assignment Models Are Based," Service Science, INFORMS, vol. 52(6), pages 1548-1569, December.
    9. Flötteröd, G. & Osorio, C., 2017. "Stochastic network link transmission model," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 180-209.
    10. Flötteröd, Gunnar & Rohde, Jannis, 2011. "Operational macroscopic modeling of complex urban road intersections," Transportation Research Part B: Methodological, Elsevier, vol. 45(6), pages 903-922, July.
    11. Canepa, Edward S. & Claudel, Christian G., 2017. "Networked traffic state estimation involving mixed fixed-mobile sensor data using Hamilton-Jacobi equations," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 686-709.
    12. Jiang, Chenming & Bhat, Chandra R. & Lam, William H.K., 2020. "A bibliometric overview of Transportation Research Part B: Methodological in the past forty years (1979–2019)," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 268-291.
    13. Mohebifard, Rasool & Hajbabaie, Ali, 2019. "Optimal network-level traffic signal control: A benders decomposition-based solution algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 252-274.
    14. S. Waller & David Fajardo & Melissa Duell & Vinayak Dixit, 2013. "Linear Programming Formulation for Strategic Dynamic Traffic Assignment," Networks and Spatial Economics, Springer, vol. 13(4), pages 427-443, December.
    15. Blumberg, Michal & Bar-Gera, Hillel, 2009. "Consistent node arrival order in dynamic network loading models," Transportation Research Part B: Methodological, Elsevier, vol. 43(3), pages 285-300, March.
    16. Perrine, Kenneth A. & Levin, Michael W. & Yahia, Cesar N. & Duell, Melissa & Boyles, Stephen D., 2019. "Implications of traffic signal cybersecurity on potential deliberate traffic disruptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 120(C), pages 58-70.
    17. Zhang, Yihang & Ioannou, Petros A., 2018. "Stability analysis and variable speed limit control of a traffic flow model," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 31-65.
    18. Nie, Yu (Marco) & Zhang, H.M., 2008. "A variational inequality formulation for inferring dynamic origin-destination travel demands," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 635-662, August.
    19. S. Waller & Athanasios Ziliaskopoulos, 2006. "A Combinatorial user optimal dynamic traffic assignment algorithm," Annals of Operations Research, Springer, vol. 144(1), pages 249-261, April.
    20. Mazaré, Pierre-Emmanuel & Dehwah, Ahmad H. & Claudel, Christian G. & Bayen, Alexandre M., 2011. "Analytical and grid-free solutions to the Lighthill–Whitham–Richards traffic flow model," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1727-1748.

    More about this item

    Keywords

    Engineering; Automatic data collection; Algorithms; Cost effectiveness; Data fusion; Data quality; Detection; Traffic speed; Traffic density; Traffic volume;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cdl:itsrrp:qt1t95h8s2. 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: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucbus.html .

    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.