IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v640y2024ics0378437124002000.html
   My bibliography  Save this article

Functional form selection and calibration of macroscopic fundamental diagrams

Author

Listed:
  • Ma, Wenfei
  • Huang, Yunping
  • Jin, Xiao
  • Zhong, Renxin

Abstract

Macroscopic fundamental diagram (MFD) is widely applied in network-level traffic control and management with most applications necessitating a well-calibrated MFD. With various data sources, more and more empirical MFDs are documented, while the MFD functional form is predetermined by traffic engineers based on their prior experiences. To our best, no generally accepted functional form has been identified. An automatic functional form selection method is yet to be devised. To meet this, a two-step MFD calibration framework is proposed to enable both the functional form selection and the estimation of parameters in this paper. A math program problem is first developed to identify a proper functional form from a set of candidate functions via random sampling of the measurement data. A mean-field variational Bayesian (MFVB) algorithm is then proposed to estimate the parameters of the selected MFD functions using the full measurement dataset. Both calibrations with and without the MFD dynamics are evaluated. The comparison between these calibration results highlights that the calibration considering the MFD dynamics can better characterize network traffic dynamics subject to dynamic travel demand and traffic control measures. Leveraging functional form selection and the computational advantages of the MFVB method, the two-step framework can significantly reduce the computational burden. Results using simulated data and empirical data validate the effectiveness and efficiency of the two-step framework. Furthermore, different functional forms are identified for different cities, highlighting the importance of functional form selection in the MFD calibration.

Suggested Citation

  • Ma, Wenfei & Huang, Yunping & Jin, Xiao & Zhong, Renxin, 2024. "Functional form selection and calibration of macroscopic fundamental diagrams," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    • Handle: RePEc:eee:phsmap:v:640:y:2024:i:c:s0378437124002000
    DOI: 10.1016/j.physa.2024.129691
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124002000
    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.2024.129691?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. Yao, Wenbin & Chen, Nuo & Su, Hongyang & Hu, Youwei & Jin, Sheng & Rong, Donglei, 2023. "A novel self-adaption macroscopic fundamental diagram considering network heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    2. Laval, Jorge A. & Castrillón, Felipe, 2015. "Stochastic approximations for the macroscopic fundamental diagram of urban networks," Transportation Research Part B: Methodological, Elsevier, vol. 81(P3), pages 904-916.
    3. Ramezani, Mohsen & Haddad, Jack & Geroliminis, Nikolas, 2015. "Dynamics of heterogeneity in urban networks: aggregated traffic modeling and hierarchical control," Transportation Research Part B: Methodological, Elsevier, vol. 74(C), pages 1-19.
    4. Geroliminis, Nikolas & Sun, Jie, 2011. "Properties of a well-defined macroscopic fundamental diagram for urban traffic," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 605-617, March.
    5. Su, Z.C. & Chow, Andy H.F. & Fang, C.L. & Liang, E.M. & Zhong, R.X., 2023. "Hierarchical control for stochastic network traffic with reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 196-216.
    6. Guo, Yajuan & Yang, Licai & Hao, Shenxue & Gu, Xinxin, 2021. "Perimeter traffic control for single urban congested region with macroscopic fundamental diagram and boundary conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    7. Geroliminis, Nikolas & Daganzo, Carlos F., 2008. "Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 759-770, November.
    8. Alisoltani, Negin & Leclercq, Ludovic & Zargayouna, Mahdi, 2021. "Can dynamic ride-sharing reduce traffic congestion?," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 212-246.
    9. Zhong, R.X. & Huang, Y.P. & Chen, C. & Lam, W.H.K. & Xu, D.B. & Sumalee, A., 2018. "Boundary conditions and behavior of the macroscopic fundamental diagram based network traffic dynamics: A control systems perspective," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 327-355.
    10. Qu, Xiaobo & Zhang, Jin & Wang, Shuaian, 2017. "On the stochastic fundamental diagram for freeway traffic: Model development, analytical properties, validation, and extensive applications," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 256-271.
    11. Kouvelas, Anastasios & Saeedmanesh, Mohammadreza & Geroliminis, Nikolas, 2017. "Enhancing model-based feedback perimeter control with data-driven online adaptive optimization," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 26-45.
    12. Wu, Chao-Yun & Li, Ming & Jiang, Rui & Hao, Qing-Yi & Hu, Mao-Bin, 2018. "Perimeter control for urban traffic system based on macroscopic fundamental diagram," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 231-242.
    13. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    14. Qu, Xiaobo & Wang, Shuaian & Zhang, Jin, 2015. "On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 91-102.
    15. Gayah, Vikash V. & Daganzo, Carlos F., 2011. "Clockwise hysteresis loops in the Macroscopic Fundamental Diagram: An effect of network instability," Transportation Research Part B: Methodological, Elsevier, vol. 45(4), pages 643-655, May.
    16. Ambühl, Lukas & Loder, Allister & Bliemer, Michiel C.J. & Menendez, Monica & Axhausen, Kay W., 2020. "A functional form with a physical meaning for the macroscopic fundamental diagram," Transportation Research Part B: Methodological, Elsevier, vol. 137(C), pages 119-132.
    17. Yildirimoglu, Mehmet & Geroliminis, Nikolas, 2014. "Approximating dynamic equilibrium conditions with macroscopic fundamental diagrams," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 186-200.
    18. Geroliminis, Nikolas & Boyacı, Burak, 2012. "The effect of variability of urban systems characteristics in the network capacity," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1607-1623.
    19. Daganzo, Carlos F., 2007. "Urban gridlock: Macroscopic modeling and mitigation approaches," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 49-62, January.
    20. Zhong, R.X. & Chen, C. & Huang, Y.P. & Sumalee, A. & Lam, W.H.K. & Xu, D.B., 2018. "Robust perimeter control for two urban regions with macroscopic fundamental diagrams: A control-Lyapunov function approach," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 687-707.
    21. Daganzo, Carlos F. & Geroliminis, Nikolas, 2008. "An analytical approximation for the macroscopic fundamental diagram of urban traffic," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 771-781, November.
    22. Mariotte, Guilhem & Leclercq, Ludovic & Batista, S.F.A. & Krug, Jean & Paipuri, Mahendra, 2020. "Calibration and validation of multi-reservoir MFD models: A case study in Lyon," Transportation Research Part B: Methodological, Elsevier, vol. 136(C), pages 62-86.
    23. Zhang, Jin & Qu, Xiaobo & Wang, Shuaian, 2018. "Reproducible generation of experimental data sample for calibrating traffic flow fundamental diagram," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 41-52.
    24. Huang, Y.P. & Xiong, J.H. & Sumalee, A. & Zheng, N. & Lam, W.H.K. & He, Z.B. & Zhong, R.X., 2020. "A dynamic user equilibrium model for multi-region macroscopic fundamental diagram systems with time-varying delays," Transportation Research Part B: Methodological, Elsevier, vol. 131(C), pages 1-25.
    25. Haddad, Jack & Geroliminis, Nikolas, 2012. "On the stability of traffic perimeter control in two-region urban cities," Transportation Research Part B: Methodological, Elsevier, vol. 46(9), pages 1159-1176.
    26. Geroliminis, Nikolas & Sun, Jie, 2011. "Hysteresis phenomena of a Macroscopic Fundamental Diagram in freeway networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(9), pages 966-979, November.
    Full references (including those not matched with items on IDEAS)

    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. Haddad, Jack & Zheng, Zhengfei, 2020. "Adaptive perimeter control for multi-region accumulation-based models with state delays," Transportation Research Part B: Methodological, Elsevier, vol. 137(C), pages 133-153.
    2. Ambühl, Lukas & Loder, Allister & Bliemer, Michiel C.J. & Menendez, Monica & Axhausen, Kay W., 2020. "A functional form with a physical meaning for the macroscopic fundamental diagram," Transportation Research Part B: Methodological, Elsevier, vol. 137(C), pages 119-132.
    3. Su, Z.C. & Chow, Andy H.F. & Fang, C.L. & Liang, E.M. & Zhong, R.X., 2023. "Hierarchical control for stochastic network traffic with reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 196-216.
    4. Wada, Kentaro & Satsukawa, Koki & Smith, Mike & Akamatsu, Takashi, 2019. "Network throughput under dynamic user equilibrium: Queue spillback, paradox and traffic control," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 391-413.
    5. Ding, Heng & Di, Yunran & Feng, Zhongxiang & Zhang, Weihua & Zheng, Xiaoyan & Yang, Tao, 2022. "A perimeter control method for a congested urban road network with dynamic and variable ranges," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 160-187.
    6. Saeedmanesh, Mohammadreza & Geroliminis, Nikolas, 2017. "Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 193-211.
    7. Guo, Qiangqiang & Ban, Xuegang (Jeff), 2020. "Macroscopic fundamental diagram based perimeter control considering dynamic user equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 136(C), pages 87-109.
    8. Gao, Shengling & Li, Daqing & Zheng, Nan & Hu, Ruiqi & She, Zhikun, 2022. "Resilient perimeter control for hyper-congested two-region networks with MFD dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 156(C), pages 50-75.
    9. Ramezani, Mohsen & Haddad, Jack & Geroliminis, Nikolas, 2015. "Dynamics of heterogeneity in urban networks: aggregated traffic modeling and hierarchical control," Transportation Research Part B: Methodological, Elsevier, vol. 74(C), pages 1-19.
    10. Huang, Y.P. & Xiong, J.H. & Sumalee, A. & Zheng, N. & Lam, W.H.K. & He, Z.B. & Zhong, R.X., 2020. "A dynamic user equilibrium model for multi-region macroscopic fundamental diagram systems with time-varying delays," Transportation Research Part B: Methodological, Elsevier, vol. 131(C), pages 1-25.
    11. Kouvelas, Anastasios & Saeedmanesh, Mohammadreza & Geroliminis, Nikolas, 2017. "Enhancing model-based feedback perimeter control with data-driven online adaptive optimization," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 26-45.
    12. Zhong, R.X. & Chen, C. & Huang, Y.P. & Sumalee, A. & Lam, W.H.K. & Xu, D.B., 2018. "Robust perimeter control for two urban regions with macroscopic fundamental diagrams: A control-Lyapunov function approach," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 687-707.
    13. Amirgholy, Mahyar & Gao, H. Oliver, 2017. "Modeling the dynamics of congestion in large urban networks using the macroscopic fundamental diagram: User equilibrium, system optimum, and pricing strategies," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 215-237.
    14. Ampountolas, Konstantinos & Zheng, Nan & Geroliminis, Nikolas, 2017. "Macroscopic modelling and robust control of bi-modal multi-region urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 616-637.
    15. Du, Jie & Wong, S.C. & Shu, Chi-Wang & Zhang, Mengping, 2015. "Reformulating the Hoogendoorn–Bovy predictive dynamic user-optimal model in continuum space with anisotropic condition," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 189-217.
    16. Zhong, R.X. & Huang, Y.P. & Chen, C. & Lam, W.H.K. & Xu, D.B. & Sumalee, A., 2018. "Boundary conditions and behavior of the macroscopic fundamental diagram based network traffic dynamics: A control systems perspective," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 327-355.
    17. Alonso, Borja & Ibeas, Ángel & Musolino, Giuseppe & Rindone, Corrado & Vitetta, Antonino, 2019. "Effects of traffic control regulation on Network Macroscopic Fundamental Diagram: A statistical analysis of real data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 136-151.
    18. Anupriya, & Bansal, Prateek & Graham, Daniel J., 2023. "Congestion in cities: Can road capacity expansions provide a solution?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    19. Mariotte, Guilhem & Leclercq, Ludovic & Laval, Jorge A., 2017. "Macroscopic urban dynamics: Analytical and numerical comparisons of existing models," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 245-267.
    20. Ding, Heng & Qian, Yu & Zheng, Xiaoyan & Bai, Haijian & Wang, Shiguang & Zhou, Jingwen, 2022. "Dynamic parking charge–perimeter control coupled method for a congested road network based on the aggregation degree characteristics of parking generation distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).

    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:640:y:2024:i:c:s0378437124002000. 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.