Multijam Solutions in Traffic Models with Velocity-Dependent Driver Strategies

The optimal-velocity follow-the-leader model is augmented with an equation that allows each driver to adjust their target headway according to the velocity difference between the driver and the car in front. In this more detailed model, which is investigated on a ring, stable and unstable multipulse or multijam solutions emerge. Analytical investigations using truncated Fourier analysis are confirmed and complemented by a detailed numerical bifurcation analysis. In addition to standard rotating waves, time-modulated waves are found.

[1]  M. Golubitsky,et al.  Singularities and groups in bifurcation theory , 1985 .

[2]  J. Pettré,et al.  Properties of pedestrians walking in line: fundamental diagrams. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  J. Starke,et al.  Complex spatiotemporal behavior in a chain of one-way nonlinearly coupled elements , 2010 .

[4]  H. W. Lee,et al.  Macroscopic traffic models from microscopic car-following models. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Floris Takens,et al.  Singularities of vector fields , 1974 .

[6]  D. Helbing Traffic and related self-driven many-particle systems , 2000, cond-mat/0012229.

[7]  Kevin Zumbrun,et al.  Nonlinear Stability of Semidiscrete Shocks for Two-Sided Schemes , 2010, SIAM J. Math. Anal..

[8]  Akihiro Nakayama,et al.  Phase transition in traffic jam experiment on a circuit , 2013 .

[9]  Mads Peter Sørensen,et al.  Stochastic control of traffic patterns , 2013, Networks Heterog. Media.

[10]  D. Wolf,et al.  Traffic and Granular Flow , 1996 .

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

[12]  J. Starke,et al.  Controlling traffic jams by time modulating the safety distance. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Nakayama,et al.  Dynamical model of traffic congestion and numerical simulation. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[14]  Y. Sugiyama,et al.  Traffic jams without bottlenecks—experimental evidence for the physical mechanism of the formation of a jam , 2008 .

[15]  James M. Greenberg Congestion Redux , 2004, SIAM J. Appl. Math..

[16]  Nicola Bellomo,et al.  On the Modeling of Traffic and Crowds: A Survey of Models, Speculations, and Perspectives , 2011, SIAM Rev..

[17]  Benjamin Seibold,et al.  Constructing set-valued fundamental diagrams from Jamiton solutions in second order traffic models , 2012, Networks Heterog. Media.

[18]  Axel Klar,et al.  Derivation of Continuum Traffic Flow Models from Microscopic Follow-the-Leader Models , 2002, SIAM J. Appl. Math..

[19]  T. Nagatani The physics of traffic jams , 2002 .

[20]  Björn Sandstede,et al.  Essential instabilities of fronts: bifurcation, and bifurcation failure , 2001 .

[21]  P. Holmes,et al.  Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields , 1983, Applied Mathematical Sciences.

[22]  J. Starke,et al.  Analytical solutions of jam pattern formation on a ring for a class of optimal velocity traffic models , 2009 .

[23]  Edward K. Nam Proof of Concept Investigation for the Physical Emission Rate Estimator ( PERE ) to be Used in MOVES , 2003 .

[24]  Axel Klar,et al.  An explicitly solvable kinetic model for vehicular traffic and associated macroscopic equations , 2002 .

[25]  Martin Treiber,et al.  Reconstructing the Traffic State by Fusion of Heterogeneous Data , 2009, Comput. Aided Civ. Infrastructure Eng..

[26]  Michel Rascle,et al.  Resurrection of "Second Order" Models of Traffic Flow , 2000, SIAM J. Appl. Math..

[27]  M R Flynn,et al.  Self-sustained nonlinear waves in traffic flow. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.