A Study of Aggregated Speed in Road Networks Using Cellular Automata

Several recent works have focused on studying the relationship between the aggregated flow and density in arterial road networks. Analogous studies involving aggregated speed appear not to have been yet undertaken, however. Here we study and compare such relations for arterial road networks controlled by different types of adaptive traffic signal systems, under various boundary conditions. To study such systems we simulate stochastic cellular automaton models. Our simulation results suggest that network speed could be used as a surrogate for density, due to a strong anticorrelation between these two network observables. Since speed estimates can be more easily obtained than density estimates, e.g. from probe vehicle data, this suggests that Macroscopic Fundamental Diagrams relating aggregated flow with speed might be a practically useful alternative to those relating flow to density.

[1]  Nikolas Geroliminis,et al.  Optimal Perimeter Control for Two Urban Regions With Macroscopic Fundamental Diagrams: A Model Predictive Approach , 2013, IEEE Transactions on Intelligent Transportation Systems.

[2]  Alain L. Kornhauser,et al.  Estimating Nationwide Link Speed Distribution Using Probe Position Data , 2008, J. Intell. Transp. Syst..

[3]  S. P. Hoogendoorn,et al.  Routing Strategies Based on Macroscopic Fundamental Diagram , 2012 .

[4]  Nikolaos Geroliminis,et al.  Hysteresis Phenomena of a Macroscopic Fundamental Diagram in Freeway Networks , 2011 .

[5]  Haris N. Koutsopoulos,et al.  Travel time estimation for urban road networks using low frequency probe vehicle data , 2013, Transportation Research Part B: Methodological.

[6]  Vikash V. Gayah,et al.  Clockwise Hysteresis Loops in the Macroscopic Fundamental Diagram , 2010 .

[7]  Lele Zhang,et al.  A comparative study of Macroscopic Fundamental Diagrams of arterial road networks governed by adaptive traffic signal systems , 2011, 1112.3761.

[8]  Henry X. Liu,et al.  A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials , 2009 .

[9]  Jan de Gier,et al.  Traffic flow on realistic road networks with adaptive traffic lights , 2010, 1011.6211.

[10]  Liping Fu,et al.  Reducing bias in probe-based arterial link travel time estimates , 2002 .

[11]  D. Helbing.  Derivation of a fundamental diagram for urban traffic flow , 2008, 0807.1843.

[12]  Christine Buisson,et al.  Exploring the Impact of Homogeneity of Traffic Measurements on the Existence of Macroscopic Fundamental Diagrams , 2009 .

[13]  Nikolaos Geroliminis,et al.  Properties of a well-defined Macroscopic Fundamental Diagram for urban traffic , 2011 .

[14]  Nikolaos Geroliminis,et al.  On the stability of traffic perimeter control in two-region urban cities , 2012 .

[15]  Liping Fu,et al.  Assessing Expected Accuracy of Probe Vehicle Travel Time Reports , 1999 .

[16]  Carlos F. Daganzo,et al.  Urban Gridlock: Macroscopic Modeling and Mitigation Approaches , 2007 .

[17]  Dirk Helbing,et al.  The spatial variability of vehicle densities as determinant of urban network capacity , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[18]  Michael Schreckenberg,et al.  A cellular automaton model for freeway traffic , 1992 .

[19]  N. Geroliminis,et al.  An analytical approximation for the macropscopic fundamental diagram of urban traffic , 2008 .