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

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  • 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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    1. Daganzo, Carlos F. & Lin, Wei-Hua & Del Castillo, Jose M., 1997. "A simple physical principle for the simulation of freeways with special lanes and priority vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 31(2), pages 103-125, April.
    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. Michalopoulos, Panos G. & Beskos, Dimitrios E. & Yamauchi, Yasuji, 1984. "Multilane traffic flow dynamics: Some macroscopic considerations," Transportation Research Part B: Methodological, Elsevier, vol. 18(4-5), pages 377-395.
    5. 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.
    6. Helbing, Dirk, 1996. "Derivation and empirical validation of a refined traffic flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 233(1), pages 253-282.
    7. Helbing, Dirk, 1997. "Modeling multi-lane traffic flow with queuing effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 242(1), pages 175-194.
    8. Holland, Edward N. & Woods, Andrew W., 1997. "A continuum model for the dispersion of traffic on two-lane roads," Transportation Research Part B: Methodological, Elsevier, vol. 31(6), pages 473-485, November.
    9. Daganzo, C. F. & Cassidy, M. J. & Bertini, R. L., 1999. "Possible explanations of phase transitions in highway traffic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(5), pages 365-379, June.
    10. Maria Lampis, 1978. "On the Kinetic Theory of Traffic Flow in the Case of a Nonnegligible Number of Queueing Vehicles," Transportation Science, INFORMS, vol. 12(1), pages 16-28, February.
    11. Hilliges, Martin & Weidlich, Wolfgang, 1995. "A phenomenological model for dynamic traffic flow in networks," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 407-431, December.
    12. Paul I. Richards, 1956. "Shock Waves on the Highway," Operations Research, INFORMS, vol. 4(1), pages 42-51, February.
    13. Islam, M. N. & Consul, P. C., 1991. "The consul distribution as a bunching model in traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 25(5), pages 365-372, October.
    14. Daganzo, Carlos F., 1995. "Requiem for second-order fluid approximations of traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 29(4), pages 277-286, August.
    15. Daganzo, Carlos F., 1994. "The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory," Transportation Research Part B: Methodological, Elsevier, vol. 28(4), pages 269-287, August.
    16. 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.
    17. Dirk Helbing & Bernardo A. Huberman, 1998. "Coherent moving states in highway traffic," Nature, Nature, vol. 396(6713), pages 738-740, December.
    18. Daganzo, Carlos F., 1997. "A continuum theory of traffic dynamics for freeways with special lanes," Transportation Research Part B: Methodological, Elsevier, vol. 31(2), pages 83-102, April.
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    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.
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    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.
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