IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i8p4430-d536983.html
   My bibliography  Save this article

Dynamic Route Flow Estimation in Road Networks Using Data from Automatic Number of Plate Recognition Sensors

Author

Listed:
  • Santos Sánchez-Cambronero

    (Department of Civil and Building Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Spain)

  • Fernando Álvarez-Bazo

    (Department of Civil and Building Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Spain)

  • Ana Rivas

    (Department of Civil and Building Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Spain)

  • Inmaculada Gallego

    (Department of Civil and Building Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Spain)

Abstract

The traffic flow on road networks is dynamic in nature. Hence, a model for dynamic traffic flow estimation should be a very useful tool for administrations to make decisions aimed at better management of traffic. In fact, these decisions may in turn improve people’s quality of life and help to implement good sustainable policies to reduce the external transportation costs (congestion, accidents, travel time, etc.). Therefore, this paper deals with the problem of estimating dynamic traffic flows in road networks by proposing a model which is continuous in the time variable and that assumes the first-in-first-out (FIFO) hypothesis. In addition, the data used as model inputs come from Automatic Number of Plate Recognition (ANPR) sensors. This powerful data permits not only to directly reconstruct the route followed by each registered vehicle but also to evaluate its travel time, which in turn is also used for the flow estimation. In addition, the fundamental variable of the model is the route flow, which is a great advantage since the rest of the flows can be obtained using the conservation laws. A synthetic network is used to illustrate the proposed method, and then it is applied to the well-known Nguyen-Dupuis and Eastern Massachusetts networks to prove its usefulness and feasibility. The results on all the tested networks are very positive and the estimated flows reproduce the simulated real flows fairly well.

Suggested Citation

  • Santos Sánchez-Cambronero & Fernando Álvarez-Bazo & Ana Rivas & Inmaculada Gallego, 2021. "Dynamic Route Flow Estimation in Road Networks Using Data from Automatic Number of Plate Recognition Sensors," Sustainability, MDPI, vol. 13(8), pages 1-30, April.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4430-:d:536983
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/8/4430/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/8/4430/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vladislav Krivda & Jan Petru & David Macha & Kristyna Plocova & David Fibich, 2020. "An Analysis of Traffic Conflicts as a Tool for Sustainable Road Transport," Sustainability, MDPI, vol. 12(17), pages 1-23, September.
    2. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part III: Multi-destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 305-313, August.
    3. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part II: Queueing at freeway bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 289-303, August.
    4. 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.
    5. Cantarella, Giulio E. & Watling, David P., 2016. "A general stochastic process for day-to-day dynamic traffic assignment: Formulation, asymptotic behaviour, and stability analysis," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 3-21.
    6. Ge, Qian & Fukuda, Daisuke, 2019. "A macroscopic dynamic network loading model for multiple-reservoir system," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 502-527.
    7. Mínguez, R. & Sánchez-Cambronero, S. & Castillo, E. & Jiménez, P., 2010. "Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 282-298, February.
    8. Yi Wang & Jian Rong & Chenjing Zhou & Xin Chang & Siyang Liu, 2020. "An Analysis of the Interactions between Adjustment Factors of Saturation Flow Rates at Signalized Intersections," Sustainability, MDPI, vol. 12(2), pages 1-18, January.
    9. Michael J. Smith, 1984. "The Stability of a Dynamic Model of Traffic Assignment---An Application of a Method of Lyapunov," Transportation Science, INFORMS, vol. 18(3), pages 245-252, August.
    10. Hyunmyung Kim & R. Jayakrishnan, 2010. "The estimation of a time-dependent OD trip table with vehicle trajectory samples," Transportation Planning and Technology, Taylor & Francis Journals, vol. 33(8), pages 747-768, October.
    11. Iryo, Takamasa, 2016. "Day-to-day dynamical model incorporating an explicit description of individuals’ information collection behaviour," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 88-103.
    12. Wang, Yi & Szeto, W.Y. & Han, Ke & Friesz, Terry L., 2018. "Dynamic traffic assignment: A review of the methodological advances for environmentally sustainable road transportation applications," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 370-394.
    13. Daganzo, Carlos, 1992. "The Cell Transmission Model. Part I: A Simple Dynamic Representation Of Highway Traffic," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0b6612tk, Institute of Transportation Studies, UC Berkeley.
    14. Smith, M. J., 1979. "The existence, uniqueness and stability of traffic equilibria," Transportation Research Part B: Methodological, Elsevier, vol. 13(4), pages 295-304, December.
    15. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part I: General theory," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 281-287, August.
    16. Ke Han & Gabriel Eve & Terry L. Friesz, 2019. "Computing Dynamic User Equilibria on Large-Scale Networks with Software Implementation," Networks and Spatial Economics, Springer, vol. 19(3), pages 869-902, September.
    17. Janson, Bruce N., 1991. "Dynamic traffic assignment for urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 25(2-3), pages 143-161.
    18. Castillo, Enrique & Menéndez, José María & Jiménez, Pilar, 2008. "Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 455-481, June.
    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. Alyse K. Winchester & Ryan A. Peterson & Ellison Carter & Mary D. Sammel, 2021. "Impact of COVID-19 Social Distancing Policies on Traffic Congestion, Mobility, and NO 2 Pollution," Sustainability, MDPI, vol. 13(13), pages 1-17, June.

    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. 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.
    2. Wang, Guanfeng & Jia, Hongfei & Feng, Tao & Tian, Jingjing & Wu, Ruiyi & Gao, Heyao & Liu, Chao, 2024. "Modelling the dual dynamic traffic flow evolution with information perception differences between human-driven vehicles and connected autonomous vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    3. Tilg, Gabriel & Ambühl, Lukas & Batista, Sergio & Menendez, Monica & Busch, Fritz, 2021. "On the application of variational theory to urban networks," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 435-456.
    4. Raadsen, Mark P.H. & Bliemer, Michiel C.J., 2019. "Continuous-time general link transmission model with simplified fanning, Part II: Event-based algorithm for networks," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 471-501.
    5. 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.
    6. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    7. Flötteröd, G. & Osorio, C., 2017. "Stochastic network link transmission model," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 180-209.
    8. 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.
    9. Daganzo, Carlos F., 2010. "On the Stability of Freeway Traffic," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4vf597r5, Institute of Transportation Studies, UC Berkeley.
    10. 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.
    11. Xiaopeng Li & Yanfeng Ouyang, 2012. "Reliable Traffic Sensor Deployment Under Probabilistic Disruptions and Generalized Surveillance Effectiveness Measures," Operations Research, INFORMS, vol. 60(5), pages 1183-1198, October.
    12. Lu, Chung-Cheng & Liu, Jiangtao & Qu, Yunchao & Peeta, Srinivas & Rouphail, Nagui M. & Zhou, Xuesong, 2016. "Eco-system optimal time-dependent flow assignment in a congested network," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 217-239.
    13. Ngoduy, D. & Hoang, N.H. & Vu, H.L. & Watling, D., 2016. "Optimal queue placement in dynamic system optimum solutions for single origin-destination traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 92(PB), pages 148-169.
    14. Costeseque, Guillaume & Lebacque, Jean-Patrick, 2014. "A variational formulation for higher order macroscopic traffic flow models: Numerical investigation," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 112-133.
    15. Wong, S. C. & Wong, G. C. K., 2002. "An analytical shock-fitting algorithm for LWR kinematic wave model embedded with linear speed-density relationship," Transportation Research Part B: Methodological, Elsevier, vol. 36(8), pages 683-706, September.
    16. Friesz, Terry L. & Han, Ke & Neto, Pedro A. & Meimand, Amir & Yao, Tao, 2013. "Dynamic user equilibrium based on a hydrodynamic model," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 102-126.
    17. Raadsen, Mark P.H. & Bliemer, Michiel C.J., 2023. "General solution scheme for the static link transmission model," Transportation Research Part B: Methodological, Elsevier, vol. 169(C), pages 108-135.
    18. Tumash, Liudmila & Canudas-de-Wit, Carlos & Delle Monache, Maria Laura, 2022. "Multi-directional continuous traffic model for large-scale urban networks," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 374-402.
    19. Jin, Wen-Long, 2007. "A dynamical system model of the traffic assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 32-48, January.
    20. Michele D. Simoni & Edoardo Marcucci & Valerio Gatta & Christian G. Claudel, 2020. "Potential last-mile impacts of crowdshipping services: a simulation-based evaluation," Transportation, Springer, vol. 47(4), pages 1933-1954, August.

    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:gam:jsusta:v:13:y:2021:i:8:p:4430-:d:536983. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.