IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v508y2018icp367-375.html
   My bibliography  Save this article

Traffic speed cloud maps: A new method for analyzing macroscopic traffic flow

Author

Listed:
  • Xiao, Jianli
  • Wang, Zhonghao

Abstract

Among traffic flow analysis, there are many methods presented for microscopic and mesoscopic traffic flow in past decades. However, the methods for macroscopic traffic flow analysis are relatively few. That is because the macroscopic traffic flow analysis needs more data and computational resources. In this paper, we focus on macroscopic traffic flow analysis using massive traffic data. A new method, traffic speed cloud maps, is proposed. It can present the traffic states in a large scale, accurately. Also, it can describe the variations of the congestion area. More importantly, with traffic speed cloud maps, people can capture the forming and degrading processes of the congestion and investigate the operation rules of macroscopic traffic flow. In order to evaluate the effectiveness of traffic speed cloud maps, 14 786 073 traffic samples are taken to perform experiments. Experimental results show that traffic speed cloud maps can present the traffic state of large areas accurately, and help us to investigate the variation of macroscopic traffic flow states intuitively and efficiently.

Suggested Citation

  • Xiao, Jianli & Wang, Zhonghao, 2018. "Traffic speed cloud maps: A new method for analyzing macroscopic traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 367-375.
  • Handle: RePEc:eee:phsmap:v:508:y:2018:i:c:p:367-375
    DOI: 10.1016/j.physa.2018.05.122
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118306666
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2018.05.122?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. Gipps, P.G., 1981. "A behavioural car-following model for computer simulation," Transportation Research Part B: Methodological, Elsevier, vol. 15(2), pages 105-111, April.
    2. Paul Wasielewski, 1979. "Car-Following Headways on Freeways Interpreted by the Semi-Poisson Headway Distribution Model," Transportation Science, INFORMS, vol. 13(1), pages 36-55, February.
    3. Rajat Jain & J. Macgregor Smith, 1997. "Modeling Vehicular Traffic Flow using M/G/C/C State Dependent Queueing Models," Transportation Science, INFORMS, vol. 31(4), pages 324-336, November.
    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. Mondal, Satyajit & Gupta, Ankit, 2021. "Speed distribution for interrupted flow facility under mixed traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    2. Hu, Xu & Li, Dongshuang & Yu, Zhaoyuan & Yan, Zhenjun & Luo, Wen & Yuan, Linwang, 2022. "Quantum harmonic oscillator model for fine-grained expressway traffic volume simulation considering individual heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(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. Li, Xiaopeng & Wang, Xin & Ouyang, Yanfeng, 2012. "Prediction and field validation of traffic oscillation propagation under nonlinear car-following laws," Transportation Research Part B: Methodological, Elsevier, vol. 46(3), pages 409-423.
    2. 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.
    3. Pedro Cesar Lopes Gerum & Andrew Reed Benton & Melike Baykal-Gürsoy, 2019. "Traffic density on corridors subject to incidents: models for long-term congestion management," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 795-831, December.
    4. Rehborn, Hubert & Klenov, Sergey L. & Palmer, Jochen, 2011. "An empirical study of common traffic congestion features based on traffic data measured in the USA, the UK, and Germany," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4466-4485.
    5. Yaqi Liu & Xiaoyuan Wang, 2020. "Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions," IJERPH, MDPI, vol. 17(19), pages 1-22, September.
    6. Amir Rastpour & Armann Ingolfsson & Bora Kolfal, 2020. "Modeling Yellow and Red Alert Durations for Ambulance Systems," Production and Operations Management, Production and Operations Management Society, vol. 29(8), pages 1972-1991, August.
    7. He, Zhengbing & Zheng, Liang & Guan, Wei, 2015. "A simple nonparametric car-following model driven by field data," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 185-201.
    8. Ziakopoulos, Apostolos & Oikonomou, Maria G. & Vlahogianni, Eleni I. & Yannis, George, 2021. "Quantifying the implementation impacts of a point to point automated urban shuttle service in a large-scale network," Transport Policy, Elsevier, vol. 114(C), pages 233-244.
    9. Mehrdad Moshtagh & Jafar Fathali & James MacGregor Smith & Nezam Mahdavi-Amiri, 2019. "Finding an optimal core on a tree network with M/G/c/c state-dependent queues," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 89(1), pages 115-142, February.
    10. Van Woensel, T. & Kerbache, L. & Peremans, H. & Vandaele, N., 2008. "Vehicle routing with dynamic travel times: A queueing approach," European Journal of Operational Research, Elsevier, vol. 186(3), pages 990-1007, May.
    11. Simin Hesami & Majid Vafaeipour & Cedric De Cauwer & Evy Rombaut & Lieselot Vanhaverbeke & Thierry Coosemans, 2023. "Dynamic Pro-Active Eco-Driving Control Framework for Energy-Efficient Autonomous Electric Mobility," Energies, MDPI, vol. 16(18), pages 1-19, September.
    12. Andrea Papu Carrone & Jeppe Rich & Christian Anker Vandet & Kun An, 2021. "Autonomous vehicles in mixed motorway traffic: capacity utilisation, impact and policy implications," Transportation, Springer, vol. 48(6), pages 2907-2938, December.
    13. Serge P. Hoogendoorn & W. Daamen, 2005. "Pedestrian Behavior at Bottlenecks," Transportation Science, INFORMS, vol. 39(2), pages 147-159, May.
    14. Tordeux, Antoine & Lassarre, Sylvain & Roussignol, Michel, 2010. "An adaptive time gap car-following model," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 1115-1131, September.
    15. Blanch Micó, Mª Teresa & Lucas Alba, Antonio & Bellés Rivera, Teresa & Ferruz Gracia, Ana Mª & Melchor Galán, Óscar M. & Delgado Pastor, Luis C. & Ruíz Jiménez, Francisco & Chóliz Montañés, Mariano, 2018. "Car following: Comparing distance-oriented vs. inertia-oriented driving techniques," Transport Policy, Elsevier, vol. 67(C), pages 13-22.
    16. Pan, Wei & Xue, Yu & He, Hong-Di & Lu, Wei-Zhen, 2018. "Impacts of traffic congestion on fuel rate, dissipation and particle emission in a single lane based on Nasch Model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 154-162.
    17. Tian, Junfang & Li, Guangyu & Treiber, Martin & Jiang, Rui & Jia, Ning & Ma, Shoufeng, 2016. "Cellular automaton model simulating spatiotemporal patterns, phase transitions and concave growth pattern of oscillations in traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 560-575.
    18. Tian, Junfang & Treiber, Martin & Ma, Shoufeng & Jia, Bin & Zhang, Wenyi, 2015. "Microscopic driving theory with oscillatory congested states: Model and empirical verification," Transportation Research Part B: Methodological, Elsevier, vol. 71(C), pages 138-157.
    19. Saifuzzaman, Mohammad & Zheng, Zuduo & Haque, Md. Mazharul & Washington, Simon, 2017. "Understanding the mechanism of traffic hysteresis and traffic oscillations through the change in task difficulty level," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 523-538.
    20. Jeffrey P. Kharoufeh & Natarajan Gautam, 2004. "Deriving Link Travel-Time Distributions via Stochastic Speed Processes," Transportation Science, INFORMS, vol. 38(1), pages 97-106, February.

    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:phsmap:v:508:y:2018:i:c:p:367-375. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.