IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v68y2014icp185-204.html
   My bibliography  Save this article

Cycle-by-cycle intersection queue length distribution estimation using sample travel times

Author

Listed:
  • Hao, Peng
  • Ban, Xuegang (Jeff)
  • Guo, Dong
  • Ji, Qiang

Abstract

We propose Bayesian Network based methods for estimating the cycle by cycle queue length distribution of a signalized intersection. Queue length here is defined as the number of vehicles in a cycle which have experienced significant delays. The data input to the methods are sample travel times from mobile traffic sensors collected between an upstream location and a downstream location of the intersection. The proposed methods first classify traffic conditions and sample scenarios to seven cases. BN models are then developed for each case. The methods are tested using data from NGSIM, a field experiment, and microscopic traffic simulation. The results are satisfactory compared with two specific queue length estimation methods previously developed in the literature.

Suggested Citation

  • Hao, Peng & Ban, Xuegang (Jeff) & Guo, Dong & Ji, Qiang, 2014. "Cycle-by-cycle intersection queue length distribution estimation using sample travel times," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 185-204.
    • Handle: RePEc:eee:transb:v:68:y:2014:i:c:p:185-204
    DOI: 10.1016/j.trb.2014.06.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191261514001118
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.trb.2014.06.004?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Hofleitner, Aude & Herring, Ryan & Bayen, Alexandre, 2012. "Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning," Transportation Research Part B: Methodological, Elsevier, vol. 46(9), pages 1097-1122.
    2. Sun, Zhanbo & Zan, Bin & Ban, Xuegang (Jeff) & Gruteser, Marco, 2013. "Privacy protection method for fine-grained urban traffic modeling using mobile sensors," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 50-69.
    3. 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.
    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. Comert, Gurcan, 2016. "Queue length estimation from probe vehicles at isolated intersections: Estimators for primary parameters," European Journal of Operational Research, Elsevier, vol. 252(2), pages 502-521.
    2. Wang, Zhengli & Zhu, Liyun & Ran, Bin & Jiang, Hai, 2020. "Queue profile estimation at a signalized intersection by exploiting the spatiotemporal propagation of shockwaves," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 59-71.
    3. Yang, Qiaoli & Shi, Zhongke & Yu, Shaowei & Zhou, Jie, 2018. "Analytical evaluation of the use of left-turn phasing for single left-turn lane only," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 266-303.
    4. Qinaat Hussain & Charitha Dias & Ali Al-Shahrani & Intizar Hussain, 2022. "Safety Analysis of Merging Vehicles Based on the Speed Difference between on-Ramp and Following Mainstream Vehicles Using NGSIM Data," Sustainability, MDPI, vol. 14(24), pages 1-12, December.
    5. Yang, Qiaoli & Shi, Zhongke, 2018. "The evolution process of queues at signalized intersections under batch arrivals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 413-425.
    6. Xingliang Liu & Jian Wang & Tangzhi Liu & Jin Xu, 2021. "Forecasting Spatiotemporal Boundary of Emergency-Event-Based Traffic Congestion in Expressway Network Considering Highway Node Acceptance Capacity," Sustainability, MDPI, vol. 13(21), pages 1-17, November.
    7. Hao, Peng & Wang, Chao, 2018. "Evaluating Environmental Impact of Traffic Congestion in Real Time Based on Sparse Mobile Crowd-sourced Data," Institute of Transportation Studies, Working Paper Series qt7q6760rz, Institute of Transportation Studies, UC Davis.
    8. Farrell, Jay A & Wu, Guoyuan & Hu, Wang & Oswald, David & Hao, Peng, 2023. "Lane-Level Localization and Map Matching for Advanced Connected and Automated Vehicle (CAV) Applications," Institute of Transportation Studies, Working Paper Series qt1f7661b4, Institute of Transportation Studies, UC Davis.
    9. 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.
    10. 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.
    11. JIA, Shaocheng & WONG, S.C. & WONG, Wai, 2025. "Adaptive signal control at partially connected intersections: A stochastic optimization model for uncertain vehicle arrival rates," Transportation Research Part B: Methodological, Elsevier, vol. 193(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. 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.
    2. 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.
    3. Hiribarren, Gabriel & Herrera, Juan Carlos, 2014. "Real time traffic states estimation on arterials based on trajectory data," Transportation Research Part B: Methodological, Elsevier, vol. 69(C), pages 19-30.
    4. Comert, Gurcan, 2016. "Queue length estimation from probe vehicles at isolated intersections: Estimators for primary parameters," European Journal of Operational Research, Elsevier, vol. 252(2), pages 502-521.
    5. 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.
    6. Laha, A. K. & Putatunda, Sayan, 2017. "Travel Time Prediction for Taxi-GPS Data Streams," IIMA Working Papers WP 2017-03-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    7. Yang, Qiaoli & Shi, Zhongke & Yu, Shaowei & Zhou, Jie, 2018. "Analytical evaluation of the use of left-turn phasing for single left-turn lane only," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 266-303.
    8. 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.
    9. 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).
    10. Harding, Matthew & Lamarche, Carlos, 2019. "A panel quantile approach to attrition bias in Big Data: Evidence from a randomized experiment," Journal of Econometrics, Elsevier, vol. 211(1), pages 61-82.
    11. Coogan, Samuel & Flores, Christopher & Varaiya, Pravin, 2017. "Traffic predictive control from low-rank structure," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 1-22.
    12. Sjoerd van der Spoel & Chintan Amrit & Jos van Hillegersberg, 2017. "Predictive analytics for truck arrival time estimation: a field study at a European distribution centre," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5062-5078, September.
    13. Westgate, Bradford S. & Woodard, Dawn B. & Matteson, David S. & Henderson, Shane G., 2016. "Large-network travel time distribution estimation for ambulances," European Journal of Operational Research, Elsevier, vol. 252(1), pages 322-333.
    14. 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.
    15. 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.
    16. Azam Asanjarani & Yoni Nazarathy & Peter Taylor, 2021. "A survey of parameter and state estimation in queues," Queueing Systems: Theory and Applications, Springer, vol. 97(1), pages 39-80, February.
    17. Jenelius, Erik & Koutsopoulos, Haris N., 2013. "Travel time estimation for urban road networks using low frequency probe vehicle data," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 64-81.
    18. Sara Sasaninejad & Joris Van Malderen & Joris Walraevens & Sabine Wittevrongel, 2023. "Expected Waiting Times at an Intersection with a Green Extension Strategy for Freight Vehicles: An Analytical Analysis," Mathematics, MDPI, vol. 11(3), pages 1-26, February.
    19. Wang, Zhengli & Zhu, Liyun & Ran, Bin & Jiang, Hai, 2020. "Queue profile estimation at a signalized intersection by exploiting the spatiotemporal propagation of shockwaves," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 59-71.
    20. Xingliang Liu & Jian Wang & Tangzhi Liu & Jin Xu, 2021. "Forecasting Spatiotemporal Boundary of Emergency-Event-Based Traffic Congestion in Expressway Network Considering Highway Node Acceptance Capacity," Sustainability, MDPI, vol. 13(21), pages 1-17, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:eee:transb:v:68:y:2014:i:c:p:185-204. 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.