Theoretical vs. empirical classification and prediction of congested traffic states

By Dirk Helbing, Martin Treiber, Arne Kesting, M. Schonhof. 2009

153 citations recorded by the source corpus.

Abstract Starting from the instability diagram of a traffic flow model, we derive conditions for the occurrence of congested traffic states, their appearance, their spreading in space and time, and the related increase in travel times. We discuss the terminology of traffic phases and give empirical evidence for the existence of a phase diagram of traffic states. In contrast to previously presented phase diagrams, it is shown that “widening synchronized patterns” are possible, if the maximum flow is located inside of a metastable density regime. Moreover, for various kinds of traffic models with different instability diagrams it is discussed, how the related phase diagrams are expected to approximately look like. Apart from this, it is pointed out that combinations of on- and off-ramps create different patterns than a single, isolated on-ramp.

Subfields: Control and Systems Engineering, Economics and Econometrics, Transportation

View Paper: https://www.semanticscholar.org/paper/f27f8e71a08b8da7ef9908cd94203342a415b8b5

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Citations in the corpus

Derivation of a fundamental diagram for urban traffic flow (2008, background): “There, they were very useful to understand capacity effects [1], different traffic states [37], and the traffic dynamics, in particularly the propagation of shock fronts [38].”
Hysteresis Phenomena of a Macroscopic Fundamental Diagram in Freeway Networks (2011, background): “While most of the traffic science theories make a clear distinction between free-flow and congested traffic states, empirical analysis of spatio-temporal congestion patterns has revealed additional complexity of traffic states and non-steady state conditions (see for example Muñoz and Daganzo, 2003;”
Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity (2009, background): “The IDM shows a crash-free collective dynamics, exhibits controllable stability properties [Helbing et al., 2009], and implements an intelligent braking strategy with smooth transitions between acceleration and deceleration behaviour.”
On the spatial partitioning of urban transportation networks (2012, background): “…(N. Geroliminis). tion between free-flow and congested traffic states, empirical analysis of spatio-temporal congestion patterns has revealed additional complexity of traffic states and non-steady state conditions (see for example Munoz and Daganzo, 2003; Helbing et al., 2009).”
Saving Human Lives: What Complexity Science and Information Systems can Contribute (2014, unknown): “For example, despite the complexity of trafﬁc patterns [20], they can be described by analytic and predictive models [18].”