Network transmission model: A dynamic traffic model at network level (poster)

New IT techniques allow communication and coordination between traffic measures. To best use this, one needs to coordinate over longer distances. Optimization of the measures is not possible using traditional microscopic or macroscopic simulation models. The Network Fundamental Diagram (NFD) describes the relation between flow and density on a network level. This paper introduces a traffic model which uses this relationship, representing traffic and traffic dynamics at a high spatial scale. The model shown to work on an example network. The model can be used to predict the effect of routing information or perimeter control.

[1]  Markos Papageorgiou,et al.  Exploiting the fundamental diagram of urban networks for feedback-based gating , 2012 .

[2]  Serge P. Hoogendoorn,et al.  Empirics of a Generalized Macroscopic Fundamental Diagram for Urban Freeways , 2013 .

[3]  Steven Logghe,et al.  Multicommodity Link Transmission Model for Dynamic Network Loading , 2006 .

[4]  Serge P. Hoogendoorn,et al.  Empirical Analysis of Merging Behavior at Freeway On-Ramp , 2010 .

[5]  J. Lebacque THE GODUNOV SCHEME AND WHAT IT MEANS FOR FIRST ORDER TRAFFIC FLOW MODELS , 1996 .

[6]  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.

[7]  Ludovic Leclercq,et al.  Hybrid approaches to the solutions of the "Lighthill-Whitham-Richards" model , 2007 .

[8]  Hani S. Mahmassani,et al.  Network performance under system optimal and user equilibrium dynamic assignments: Implications for , 1993 .

[9]  N. Geroliminis,et al.  Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings - eScholarship , 2007 .

[10]  Dirk Cattrysse,et al.  A generic class of first order node models for dynamic macroscopic simulation of traffic flows , 2011 .

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

[12]  Serge P. Hoogendoorn,et al.  Data requirements for traffic control on a macroscopic level , 2011 .

[13]  Nikolaos Geroliminis,et al.  Estimating MFDs in Simple Networks with Route Choice. , 2013 .

[14]  G. F. Newell A simplified theory of kinematic waves in highway traffic, part II: Queueing at freeway bottlenecks , 1993 .

[15]  J. W. C. van Lint,et al.  Relationship between Application Scale and Maximum Time Latency in Intelligent Transport Solutions , 2012 .

[16]  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.

[17]  C. Daganzo,et al.  Macroscopic relations of urban traffic variables: Bifurcations, multivaluedness and instability , 2011 .

[18]  C. Daganzo THE CELL TRANSMISSION MODEL.. , 1994 .

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

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

[21]  Serge P. Hoogendoorn,et al.  The impact of traffic dynamics on macroscopic fundamental diagram , 2013 .

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