IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v138y2020icp435-456.html
   My bibliography  Save this article

Towards real-time density estimation using vehicle-to-vehicle communications

Author

Listed:
  • Florin, Ryan
  • Olariu, Stephan

Abstract

Traffic state estimation is a fundamental task of Intelligent Transportation Systems (ITS). Recent advances in sensor technology and emerging computer and vehicular communications paradigms have brought the task of estimating traffic state parameters in real time within reach. Recognizing this, the US-DOT started promoting the Connected Vehicles (CV) initiative. By using wireless connectivity between the vehicles participating in the traffic, the CV initiative aims to promote an increased awareness of real-time traffic conditions and, as a result, to reduce the number and severity of crashes.

Suggested Citation

  • Florin, Ryan & Olariu, Stephan, 2020. "Towards real-time density estimation using vehicle-to-vehicle communications," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 435-456.
    • Handle: RePEc:eee:transb:v:138:y:2020:i:c:p:435-456
    DOI: 10.1016/j.trb.2020.06.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191261520303404
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.trb.2020.06.001?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yasuji Makigami & G. F. Newell & Richard Rothery, 1971. "Three-Dimensional Representation of Traffic Flow," Transportation Science, INFORMS, vol. 5(3), pages 302-313, August.
    2. Wang, Yibing & Papageorgiou, Markos, 2005. "Real-time freeway traffic state estimation based on extended Kalman filter: a general approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(2), pages 141-167, February.
    3. Wang, Yibing & Papageorgiou, Markos & Messmer, Albert, 2008. "Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(10), pages 1340-1358, December.
    4. Comert, Gurcan & Cetin, Mecit, 2009. "Queue length estimation from probe vehicle location and the impacts of sample size," European Journal of Operational Research, Elsevier, vol. 197(1), pages 196-202, August.
    5. Zheng, Fangfang & Jabari, Saif Eddin & Liu, Henry X. & Lin, DianChao, 2018. "Traffic state estimation using stochastic Lagrangian dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 143-165.
    6. Seo, Toru & Kawasaki, Yutaka & Kusakabe, Takahiko & Asakura, Yasuo, 2019. "Fundamental diagram estimation by using trajectories of probe vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 40-56.
    7. Yu, Chunhui & Sun, Weili & Liu, Henry X. & Yang, Xiaoguang, 2019. "Managing connected and automated vehicles at isolated intersections: From reservation- to optimization-based methods," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 416-435.
    8. van Erp, Paul B.C. & Knoop, Victor L. & Hoogendoorn, Serge P., 2018. "Macroscopic traffic state estimation using relative flows from stationary and moving observers," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 281-299.
    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. Chen, Yi-Ting & Sun, Edward W. & Chang, Ming-Feng & Lin, Yi-Bing, 2021. "Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0," International Journal of Production Economics, Elsevier, vol. 238(C).

    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. Varga, Balázs & Tettamanti, Tamás & Kulcsár, Balázs & Qu, Xiaobo, 2020. "Public transport trajectory planning with probabilistic guarantees," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 81-101.
    2. Blake Davis & Ang Ji & Bichen Liu & David Levinson, 2020. "Moving Array Traffic Probes," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    3. Xingmin Wang & Zachary Jerome & Zihao Wang & Chenhao Zhang & Shengyin Shen & Vivek Vijaya Kumar & Fan Bai & Paul Krajewski & Danielle Deneau & Ahmad Jawad & Rachel Jones & Gary Piotrowicz & Henry X. L, 2024. "Traffic light optimization with low penetration rate vehicle trajectory data," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    4. Čičić, Mladen & Johansson, Karl Henrik, 2022. "Front-tracking transition system model for traffic state reconstruction, model learning, and control with application to stop-and-go wave dissipation," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 212-236.
    5. Li, Li & Jabari, Saif Eddin, 2019. "Position weighted backpressure intersection control for urban networks," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 435-461.
    6. Wang, Chun & Zhang, Weihua & Wu, Cong & Hu, Heng & Ding, Heng & Zhu, Wenjia, 2022. "A traffic state recognition model based on feature map and deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    7. van Erp, Paul B.C. & Knoop, Victor L. & Hoogendoorn, Serge P., 2018. "Macroscopic traffic state estimation using relative flows from stationary and moving observers," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 281-299.
    8. Bae, Bumjoon & Liu, Yuandong & Han, Lee D. & Bozdogan, Hamparsum, 2019. "Spatio-temporal traffic queue detection for uninterrupted flows," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 20-34.
    9. Ernesto Cipriani & Lorenzo Giannantoni & Livia Mannini, 2023. "Integrated Variable Speed Limits and User Information Strategy," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    10. WU, Hao & LUO, Lyuzhou & OGUCHI, Takashi & TANG, Keshuang & ZHU, Hong, 2024. "Stochastic queue profile estimation using license plate recognition data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    11. Jin, Wen-Long, 2018. "Unifiable multi-commodity kinematic wave model," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 639-659.
    12. Gao, Yang & Levinson, David, 2024. "A multi-stage spatial queueing model with logistic arrivals and departures consistent with the microscopic fundamental diagram and hysteresis," Transportation Research Part B: Methodological, Elsevier, vol. 186(C).
    13. Klug, Florian, 2014. "Modelling and analysis of synchronised material flow with fluid dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 404-417.
    14. Yin, Ruyang & Zheng, Nan & Liu, Zhiyuan, 2022. "Estimating fundamental diagram for multi-modal signalized urban links with limited probe data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    15. Saif Eddin Jabari & Nikolaos M. Freris & Deepthi Mary Dilip, 2020. "Sparse Travel Time Estimation from Streaming Data," Transportation Science, INFORMS, vol. 54(1), pages 1-20, January.
    16. Hao, Peng & Ban, Xuegang, 2015. "Long queue estimation for signalized intersections using mobile data," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 54-73.
    17. Shang, Pan & Li, Ruimin & Guo, Jifu & Xian, Kai & Zhou, Xuesong, 2019. "Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 135-167.
    18. 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.
    19. Wong, Wai & Shen, Shengyin & Zhao, Yan & Liu, Henry X., 2019. "On the estimation of connected vehicle penetration rate based on single-source connected vehicle data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 169-191.
    20. Nicholas Molyneaux & Riccardo Scarinci & Michel Bierlaire, 0. "Design and analysis of control strategies for pedestrian flows," Transportation, Springer, vol. 0, pages 1-41.

    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:eee:transb:v:138:y:2020:i:c:p:435-456. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    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.