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Urban Network-Wide Traffic Variables and Their Relations

Author

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  • Siamak Ardekani

    (Virginia Polytechnic Institute and State University, Blacksburg, Virginia)

  • Robert Herman

    (The University of Texas at Austin, Austin, Texas)

Abstract

Time-lapse aerial photography over the Central Business Districts (CBD) of Austin and Dallas, Texas, has been employed to determine the averages of concentration, speed and fraction of vehicles stopped and to examine the relations among such network-wide averages including the flow which was measured on the ground simultaneously. The results have indicated that the average flow in a street network may indeed be expressed as the product of the space mean speed and concentration. Simultaneous ground experiments have also been conducted in the Austin CBD to investigate the reasonableness of the assumptions of the “two-fluid model,” a curvilinear relation between the trip time and stop time per unit distance, which may be used in characterizing the quality of traffic service in urban street networks. As a result of these simultaneous ground experiments and aerial observations, the assumptions of the model have been verified. Moreover, relations between the fraction of vehicles stopped and concentration as well as between speed and concentration have allowed the two-fluid model to be used to compare the quality of traffic service in various street networks under the same level of concentration. The two-fluid model may then be used to predict, for a given change in vehicular concentration in a street network, the resulting changes in the averages of speed, fraction of vehicles stopped, flow, etc. This is particularly useful as a performance model in urban planning where for a given concentration it is desirable to predict the resulting traffic conditions.

Suggested Citation

  • Siamak Ardekani & Robert Herman, 1987. "Urban Network-Wide Traffic Variables and Their Relations," Transportation Science, INFORMS, vol. 21(1), pages 1-16, February.
    • Handle: RePEc:inm:ortrsc:v:21:y:1987:i:1:p:1-16
    DOI: 10.1287/trsc.21.1.1
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    Cited by:

    1. Daganzo, Carlos F., 2011. "On the macroscopic stability of freeway traffic," Transportation Research Part B: Methodological, Elsevier, vol. 45(5), pages 782-788, June.
    2. Kenneth A. Small & Xuehao Chu, 2003. "Hypercongestion," Journal of Transport Economics and Policy, University of Bath, vol. 37(3), pages 319-352, September.
    3. Laval, Jorge A. & Aghamohammadi, Rafegh, 2022. "Network-wide Emissions Estimation Using the Macroscopic Fundamental Diagram," Institute of Transportation Studies, Working Paper Series qt8670m9jh, Institute of Transportation Studies, UC Davis.
    4. Richard Connors & David Watling, 2015. "Assessing the Demand Vulnerability of Equilibrium Traffic Networks via Network Aggregation," Networks and Spatial Economics, Springer, vol. 15(2), pages 367-395, June.
    5. Gayah, Vikash V. & Gao, Xueyu (Shirley) & Nagle, Andrew S., 2014. "On the impacts of locally adaptive signal control on urban network stability and the Macroscopic Fundamental Diagram," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 255-268.
    6. Small, Kenneth A., 2015. "The bottleneck model: An assessment and interpretation," Economics of Transportation, Elsevier, vol. 4(1), pages 110-117.
    7. 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.
    8. Cheng, Qixiu & Lin, Yuqian & Zhou, Xuesong (Simon) & Liu, Zhiyuan, 2024. "Analytical formulation for explaining the variations in traffic states: A fundamental diagram modeling perspective with stochastic parameters," European Journal of Operational Research, Elsevier, vol. 312(1), pages 182-197.
    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. Moore, James E. II & Jayakrishnan, R. & McNally, M. G. & MacCarley, C. Arthur, 1999. "Evaluation of the Anaheim Advanced Traffic Control System Field Operational Test: Introduction and Task A: Evaluation of SCOOT Performance," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7p3386qz, Institute of Transportation Studies, UC Berkeley.
    11. Gayah, Vikash V. & Daganzo, Carlos F., 2010. "Clockwise Hysteresis Loops in the MacroscopicFundamental Diagram," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2x98k1x2, Institute of Transportation Studies, UC Berkeley.
    12. Gonzales, Eric J., 2015. "Coordinated pricing for cars and transit in cities with hypercongestion," Economics of Transportation, Elsevier, vol. 4(1), pages 64-81.
    13. 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.
    14. Bou Sleiman, Lea, 2023. "Displacing Congestion: Evidence from Paris," CEPREMAP Working Papers (Docweb) 2302, CEPREMAP.
    15. Jin, Wen-Long & Gan, Qi-Jian & Gayah, Vikash V., 2013. "A kinematic wave approach to traffic statics and dynamics in a double-ring network," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 114-131.
    16. Arnott, Richard, 2013. "A bathtub model of downtown traffic congestion," Journal of Urban Economics, Elsevier, vol. 76(C), pages 110-121.
    17. 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.
    18. Kenneth Small, 2015. "The Bottleneck Model: An Assessment and Interpretation," Working Papers 141506, University of California-Irvine, Department of Economics.
    19. Gao, Xueyu (Shirley) & Gayah, Vikash V., 2018. "An analytical framework to model uncertainty in urban network dynamics using Macroscopic Fundamental Diagrams," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 660-675.
    20. Ranjan, Abhishek & Fosgerau, Mogens & Jenelius, Erik, 2016. "Emergence of a urban traffic macroscopic fundamental diagram," MPRA Paper 74350, University Library of Munich, Germany, revised 07 Oct 2016.
    21. Williams, James C. & Mahmassani, Hani S. & Herman, Robert, 1995. "Sampling strategies for two-fluid model parameter estimation in urban networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 29(3), pages 229-244, May.
    22. 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.
    23. 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.
    24. Amin Mazloumian & Nikolas Geroliminis & Dirk Helbing, "undated". "The Spatial Variability of Vehicle Densities as Determinant of Urban Network Capacity," Working Papers CCSS-09-009, ETH Zurich, Chair of Systems Design.
    25. 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.

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