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MASTER: macroscopic traffic simulation based on a gas-kinetic, non-local traffic model

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Listed:
  • Helbing, Dirk
  • Hennecke, Ansgar
  • Shvetsov, Vladimir
  • Treiber, Martin

Abstract

We present a gas-kinetic (Boltzmann-like) traffic equation that is not only suited for low vehicle densities, but also for the high-density regime, as it takes into account the forwardly directed interactions, effects of vehicular space requirements like increased interaction rates, and effects of velocity correlations that reflect the bunching of cars, at least partially. From this gas-kinetic equation, we systematically derive the related macroscopic traffic equations. The corresponding partial differential equations for the vehicle density and average velocity are directly related to the quantities characterizing individual driver-vehicle behavior, and, as we show by calibration of the model, their optimal values have the expected order of magnitude. Therefore, the model allows to investigate the influences of varying street and weather conditions or freeway control measures. We point out that, because of the forwardly directed interactions, the macroscopic equations contain non-local instead of diffusion or viscosity terms. This resolves some of the inconsistencies found in previous models and allows for a fast and robust numerical integration, so that several thousand freeway kilometers can be simulated in real-time. It turns out that the model is in good agreement with the experimentally observed properties of freeway traffic flow. In particular, it reproduces the characteristic outflow and dissolution velocity of traffic jams, as well as the phase transition to "synchronized" congested traffic. We also reproduce the five different kinds of congested states that have been found close to on-ramps (or bottlenecks) and present a "phase diagram" of the different traffic states in dependence of the main flow and the ramp flow, showing that congested states are often induced by perturbations in the traffic flow. Finally, we introduce generalized macroscopic equations for multi-lane and multi-userclass traffic. With these, we investigate the differences between multi-lane simulations and simulations of the effective one-lane model.

Suggested Citation

  • Helbing, Dirk & Hennecke, Ansgar & Shvetsov, Vladimir & Treiber, Martin, 2001. "MASTER: macroscopic traffic simulation based on a gas-kinetic, non-local traffic model," Transportation Research Part B: Methodological, Elsevier, vol. 35(2), pages 183-211, February.
    • Handle: RePEc:eee:transb:v:35:y:2001:i:2:p:183-211
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    References listed on IDEAS

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    2. Laval, Jorge A. & Toth, Christopher S. & Zhou, Yi, 2014. "A parsimonious model for the formation of oscillations in car-following models," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 228-238.
    3. Jiang, Rui & Wu, Qing-Song, 2003. "Study on propagation speed of small disturbance from a car-following approach," Transportation Research Part B: Methodological, Elsevier, vol. 37(1), pages 85-99, January.
    4. Mohammadian, Saeed & Zheng, Zuduo & Haque, Md. Mazharul & Bhaskar, Ashish, 2021. "Performance of continuum models for realworld traffic flows: Comprehensive benchmarking," Transportation Research Part B: Methodological, Elsevier, vol. 147(C), pages 132-167.
    5. Schönhof, Martin & Helbing, Dirk, 2009. "Criticism of three-phase traffic theory," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 784-797, August.
    6. Ngoduy, D. & Hoogendoorn, S.P. & Liu, R., 2009. "Continuum modeling of cooperative traffic flow dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(13), pages 2705-2716.
    7. Oliver Kunze & Fabian Frommer, 2021. "The Matrix vs. The Fifth Element—Assessing Future Scenarios of Urban Transport from a Sustainability Perspective," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    8. Sun, Lu & Jafaripournimchahi, Ammar & Kornhauser, Alain & Hu, Wushen, 2020. "A new higher-order viscous continuum traffic flow model considering driver memory in the era of autonomous and connected vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    9. Fan, Hongqiang & Jia, Bin & Tian, Junfang & Yun, Lifen, 2014. "Characteristics of traffic flow at a non-signalized intersection in the framework of game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 172-180.
    10. Krzysztof J. Szajowski & Kinga Włodarczyk, 2020. "Drivers’ Skills and Behavior vs. Traffic at Intersections," Mathematics, MDPI, vol. 8(3), pages 1-20, March.
    11. Jorge A. Laval & Bhargava R. Chilukuri, 2014. "The Distribution of Congestion on a Class of Stochastic Kinematic Wave Models," Transportation Science, INFORMS, vol. 48(2), pages 217-224, May.
    12. Zhou, Yang & Ahn, Soyoung & Wang, Meng & Hoogendoorn, Serge, 2020. "Stabilizing mixed vehicular platoons with connected automated vehicles: An H-infinity approach," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 152-170.
    13. Martin Schönhof & Dirk Helbing, 2007. "Empirical Features of Congested Traffic States and Their Implications for Traffic Modeling," Transportation Science, INFORMS, vol. 41(2), pages 135-166, May.

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