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The Spatial Variability of Vehicle Densities as Determinant of Urban Network Capacity

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Listed:
  • Amin Mazloumian

    ()

  • Nikolas Geroliminis
  • Dirk Helbing

Abstract

Due to the complexity of the traffic flow dynamics in urban road networks, most quantitative descriptions of city traffic so far are based on computer simulations. This contribution pursues a macroscopic (fluid-dynamic) simulation approach, which facilitates a simple simulation of congestion spreading in cities. First, we show that a quantization of the macroscopic turning flows into units of single vehicles is necessary to obtain realistic fluctuations in the traffic variables, and how this can be implemented in a fluid-dynamic model. Then, we propose a new method to simulate destination flows without the requirement of individual route assignments. Combining both methods allows us to study a variety of different simulation scenarios. These reveal fundamental relationships between the average flow, the average density, and the variability of the vehicle densities. Considering the inhomogeneity of traffic as an independent variable can eliminate the scattering of congested flow measurements. The variability also turns out to be a key variable of urban traffic performance. Our results can be explained through the number of full links of the road network, and approximated by a simple analytical formula.

Suggested Citation

  • 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.
  • Handle: RePEc:stz:wpaper:ccss-09-009
    as

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    References listed on IDEAS

    as
    1. D. Helbing, 2009. "Derivation of a fundamental diagram for urban traffic flow," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 70(2), pages 229-241, July.
    2. D. Helbing & M. Treiber & A. Kesting & M. Schönhof, 2009. "Theoretical vs. empirical classification and prediction of congested traffic states," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 69(4), pages 583-598, June.
    3. David Charypar & Kai Nagel, 2005. "Generating complete all-day activity plans with genetic algorithms," Transportation, Springer, vol. 32(4), pages 369-397, July.
    4. 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.
    5. D. Helbing & A. Mazloumian, 2009. "Operation regimes and slower-is-faster effect in the controlof traffic intersections," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 70(2), pages 257-274, July.
    6. Daganzo, Carlos F., 2007. "Urban gridlock: Macroscopic modeling and mitigation approaches," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 49-62, January.
    7. D. Helbing & M. Moussaid, 2009. "Analytical calculation of critical perturbation amplitudes and critical densities by non-linear stability analysis of a simple traffic flow model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 69(4), pages 571-581, June.
    8. Geroliminis, Nikolas & Daganzo, Carlos F., 2008. "Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 759-770, November.
    9. 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.
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