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Continuum modeling of multiclass traffic flow

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  • Hoogendoorn, Serge P.
  • Bovy, Piet H. L.

Abstract

In contemporary macroscopic traffic flow modeling, a distinction between user-classes is rarely made. However, it is envisaged that both the accuracy and the explanatory ability of macroscopic traffic flow models can be improved significantly by distinguishing classes and their specific driving characteristics. In this article, we derive such a multiple user-class traffic flow model. Starting point for the derivation of the macroscopic flow model is the user-class specific phase-space density, which can be considered as a generalization of the traditional density. The gas-kinetic equations describing the dynamics of the multiclass Phase-Space Density (MUC-PSD) are governed by various, interacting processes, such as acceleration towards a class-specific desired velocity, deceleration caused by vehicle interactions and the influence of lane changing. The gas-kinetic equations serve as the foundation of the proposed macroscopic traffic flow models, describing the dynamics of the class-dependent spatial density, velocity and velocity variance. The modeling approach yields explicit relations for both the velocity and the velocity variance. These equilibrium relations show competing processes: on the one hand, drivers accelerate towards their class-dependent desired velocity, while on the other hand, they need to decelerate due to interactions with vehicles from their own class and asymmetric interactions with vehicles from other classes. Using the operationalized model, macroscopic simulations provide insight into the model behavior for different scenarios.

Suggested Citation

  • Hoogendoorn, Serge P. & Bovy, Piet H. L., 2000. "Continuum modeling of multiclass traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 34(2), pages 123-146, February.
  • Handle: RePEc:eee:transb:v:34:y:2000:i:2:p:123-146
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    References listed on IDEAS

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    2. Wang, Shuliang & Chen, Chen & Zhang, Jianhua & Gu, Xifeng & Huang, Xiaodi, 2022. "Vulnerability assessment of urban road traffic systems based on traffic flow," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    3. Saberi, Meead & Aghabayk, Kayvan & Sobhani, Amir, 2015. "Spatial fluctuations of pedestrian velocities in bidirectional streams: Exploring the effects of self-organization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 120-128.
    4. Wong, G. C. K. & Wong, S. C., 2002. "A multi-class traffic flow model - an extension of LWR model with heterogeneous drivers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(9), pages 827-841, November.
    5. Hu, Zejing & Smirnova, M.N. & Zhang, Yongliang & Smirnov, N.N. & Zhu, Zuojin, 2021. "Estimation of travel time through a composite ring road by a viscoelastic traffic flow model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 501-521.
    6. Daniel (Jian) Sun & Lily Elefteriadou, 2014. "A Driver Behavior-Based Lane-Changing Model for Urban Arterial Streets," Transportation Science, INFORMS, vol. 48(2), pages 184-205, May.
    7. Mu, Rui & Yamamoto, Toshiyuki, 2019. "Analysis of traffic flow with micro-cars with respect to safety and environmental impact," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 217-241.
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    9. (Sean) Qian, Zhen & Li, Jia & Li, Xiaopeng & Zhang, Michael & Wang, Haizhong, 2017. "Modeling heterogeneous traffic flow: A pragmatic approach," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 183-204.
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