Modeling a Functional Form of Fundamental Diagram by Automated License Plate Readers Data: A Case Study in Tehran, Iran
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 1
Abstract
Recently, the traffic control infrastructure and public transport fare records in Tehran have produced massive data. Such data can be analyzed to monitor the city’s transportation condition and also to improve its transportation network. Understanding the fundamental features of traffic, including flow, density, and velocity, on important roadways is vital for a reliable traffic control mechanism. Obtaining this data from traffic cameras scattered across the city was the goal of this research. The average speed of cars, as recorded by automated license plate readers (ALPRs), was also obtained, and a relationship for estimating the flow density is proposed that simulates cars’ behaviors. The curve plotted by the proposed relationship has about an 25% average speed deviancy from that determined by the probe vehicles for all cameras. This error seems acceptable, considering the limitations of ALPRs and/or diversity of roads.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. Codes are available at Github (Atieh Barati Nia 2022), and because a non-disclosure agreement has been signed, the authors do not have the authority to publish the data. However, the data can be obtained within Iran by contacting TCUSO and following the necessary protocols (Tehran Center for Urban Statistics and Observatory, n.d.).
Acknowledgments
This research was supported by a grant from Tehran Municipality.
References
Ambühl, L., A. Loder, M. Menendez, and K. W. Axhausen. 2017. “Empirical macroscopic fundamental diagrams: New insights from loop detector and floating car data.” In Proc., 96th Annual Meeting of the Transportation Research Board (TRB 2017), 17. Washington, DC: Transportation Research Board.
Atieh Barati Nia, K. K. 2022. “Modeling fundamental diagram.” Accessed September 23, 2022. https://doi.org/10.5281/zenodo.7108078.
Chiabaut, N., C. Buisson, and L. Leclercq. 2009. “Fundamental diagram estimation through passing rate measurements in congestion.” IEEE Trans. Intell. Transp. Syst. 10 (2): 355–359. https://doi.org/10.1109/TITS.2009.2018963.
Dijkstra, E. W. 1959. “A note on two problems in connexion with graphs.” Numer. Math. 1 (1): 269–271. https://doi.org/10.1007/BF01386390.
Drake, J. S., J. L. Schofer, and A. D. May. 1967. “A statistical analysis of speed-density hypothesis.” Highway Res. Rec. 154: 53–87.
Edie, L. C. 1995. “Car-following and steady-state theory for noncongested traffic.” Oper. Res. 9 (1): 66–76.
Gayah, V. V., and C. F. Daganzo. 2011. “Clockwise hysteresis loops in the Macroscopic Fundamental Diagram: An effect of network instability.” Transp. Res. Part B Methodol. 45 (4): 643–655. https://doi.org/10.1016/j.trb.2010.11.006.
Geroliminis, N., and B. Boyacı. 2012. “The effect of variability of urban systems characteristics in the network capacity.” Transp. Res. Part B Methodol. 46 (10): 1607–1623. https://doi.org/10.1016/j.trb.2012.08.001.
Greenberg, H. 1959. “An analysis of traffic flow.” Oper. Res. 7 (1): 79–85. https://doi.org/10.1287/opre.7.1.79.
Greenshields, B., J. Bibbins, W. Channing, and H. Miller. 1935. “A study of traffic capacity.” In Vol. 1935 of High Way Research Board Proc. Rockville, MD: National Research Council.
Herrera, J. C., and A. M. Bayen. 2010. “Incorporation of Lagrangian measurements in freeway traffic state estimation.” Transp. Res. Part B Methodol. 44 (4): 460–481. https://doi.org/10.1016/j.trb.2009.10.005.
Intelligent Control of Odd/Even Zone. 2016. Tehran municipality. Tehran, Iran: Tehran Municipality.
Ji, Y., W. Daamen, S. Hoogendoorn, S. Hoogendoorn-Lanser, and X. Qian. 2010. “Investigating the shape of the macroscopic fundamental diagram using simulation data.” Transp. Res. Rec. 2161 (1): 40–48. https://doi.org/10.3141/2161-05.
Kerner, B. S., and P. Konhäuser. 1994. “Structure and parameters of clusters in traffic flow.” Phys. Rev. E 50 (1): 54. https://doi.org/10.1103/PhysRevE.50.54.
Keyvan-Ekbatani, M., A. Kouvelas, I. Papamichail, and M. Papageorgiou. 2012. “Exploiting the fundamental diagram of urban networks for feedback-based gating.” Transp. Res. Part B Methodol. 46 (10): 1393–1403. https://doi.org/10.1016/j.trb.2012.06.008.
Keyvan-Ekbatani, M., M. Papageorgiou, and I. Papamichail. 2013. “Urban congestion gating control based on reduced operational network fundamental diagrams.” Transp. Res. Part C Emerging Technol. 33 (Aug): 74–87. https://doi.org/10.1016/j.trc.2013.04.010.
Lin, X. 2019. “A road network traffic state identification method based on macroscopic fundamental diagram and spectral clustering and support vector machine.” Math. Probl. Eng. 2019 (1): 1–10.
Macnicholas, M. J. 2011. “A simple and pragmatic representation of traffic flow.” In Vol. 75 of Proc., Symp. on the Fundamental Diagram, 161–177. Washington, DC: Transportation Research Board.
Mao, S., G. Xiao, J. Lee, L. Wang, Z. Wang, and H. Huang. 2021. “Safety effects of work zone advisory systems under the intelligent connected vehicle environment: A microsimulation approach.” J. Intell. Connected Veh. 4 (1): 16–27. https://doi.org/10.1108/JICV-07-2020-0006.
May, A. D. 1990. Traffic flow fundamentals. Englewood Cliffs, NJ: Prentice Hall.
Mesbah, M., S. Bourbour, H. Faroqi, A. Ghaffari, and S. Ghasemi. 2022. A data-driven approach to estimate dynamic and multimodal urban origin-destination matrices. Washington, DC: Transportation Research Board.
Parsa, A. B., and M. Habibian. 2017. Understanding motorcyclists’ behavior toward TDM policies in work tours. Washington, DC: Transportation Research Board.
Qu, X., S. Wang, and J. Zhang. 2015. “On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models.” Transp. Res. Part B Methodol. 73 (Mar): 91–102. https://doi.org/10.1016/j.trb.2015.01.001.
Qu, X., J. Zhang, and S. Wang. 2017. “On the stochastic fundamental diagram for freeway traffic: Model development, analytical properties, validation, and extensive applications.” Transp. Res. Part B Methodol. 104 (Oct): 256–271. https://doi.org/10.1016/j.trb.2017.07.003.
Seo, T., Y. Kawasaki, T. Kusakabe, and Y. Asakura. 2019. “Fundamental diagram estimation by using trajectories of probe vehicles.” Transp. Res. Part B Methodol. 122 (Apr): 40–56. https://doi.org/10.1016/j.trb.2019.02.005.
Shi, X., and X. Li. 2021. “Constructing a fundamental diagram for traffic flow with automated vehicles: Methodology and demonstration.” Transp. Res. Part B Methodol. 150 (Aug): 279–292. https://doi.org/10.1016/j.trb.2021.06.011.
Statistical Yearbook of Tehran Province—2016. 2017. Tehran municipality. Tehran, Iran: Tehran Municipality.
Tajdari, F., and C. Roncoli. 2021. “Adaptive traffic control at motorway bottlenecks with time-varying Fundamental Diagram.” IFAC-PapersOnLine 54 (2): 271–277. https://doi.org/10.1016/j.ifacol.2021.06.051.
Tehran Center for Urban Statistics and Observatory. n.d. “Tehran Municipality Data Center.” Accessed January 1, 2022. https://tuo.tehran.ir/About-us.
Treiber, M., A. Kesting, and D. Helbing. 2010. “Three-phase traffic theory and two-phase models with a fundamental diagram in the light of empirical stylized facts.” Transp. Res. Part B Methodol. 44 (8–9): 983–1000. https://doi.org/10.1016/j.trb.2010.03.004.
Underwood, R. T. 1960. Speed, volume and density relationships. Washington, DC: Transportation Research Board.
Wang, H., J. Li, Q.-Y. Chen, and D. Ni. 2011. “Logistic modeling of the equilibrium speed-density relationship.” Transp. Res. Part A Policy Pract. 45 (6): 554–566.
Wang, H., D. Ni, Q.-Y. Chen, and J. Li. 2013. “Stochastic modeling of the equilibrium speed-density relationship.” J. Adv. Transp. 47 (1): 126–150.
Wang, S., X. Chen, and X. Qu. 2021. “Model on empirically calibrating stochastic traffic flow fundamental diagram.” Commun. Transp. Res. 1 (Dec): 100015.
Wang, W., and Y. Wu. 2021. “Is uncertainty always bad for the performance of transportation systems?” Commun. Transp. Res. 1 (Dec): 100021. https://doi.org/10.1016/j.commtr.2021.100021.
Zhang, J., X. Qu, and S. Wang. 2018. “Reproducible generation of experimental data sample for calibrating traffic flow fundamental diagram.” Transp. Res. Part A Policy Pract. 111 (May): 41–52.
Zhang, Z., and X. T. Yang. 2021. “Analysis of highway performance under mixed connected and regular vehicle environment.” J. Intell. Connected Veh. 4 (2): 68–79. https://doi.org/10.1108/JICV-10-2020-0011.
Zhou, J., and F. Zhu. 2020. “Modeling the fundamental diagram of mixed human-driven and connected automated vehicles.” Transp. Res. Part C Emerging Technol. 115 (Jun): 102614. https://doi.org/10.1016/j.trc.2020.102614.
Zito, R., G. D’Este, and M. Taylor. 1995. “Global positioning systems in the time domain: How useful a tool for intelligent vehicle-highway systems?” Transp. Res. Part C Emerging Technol. 3 (4): 193–209. https://doi.org/10.1016/0968-090X(95)00006-5.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 1 • January 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Feb 27, 2022
Accepted: Aug 3, 2022
Published online: Oct 20, 2022
Published in print: Jan 1, 2023
Discussion open until: Mar 20, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.