Controlling traffic jams by time modulating the safety distance

Gaididei, Yuri; Gorria, Carlos; Berkemer, Rainer; Kawamoto, Atsushi; Shiga, Tohru; Christiansen, Peter Leth; Sørensen, Mads Peter; Starke, Jens

doi:10.1103/physreve.88.042803

Cited by 11 publications

(8 citation statements)

References 36 publications

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“…We use the method introduced by [38] to measure local density. [43,44] from metastudy (Weidmann,[29]). The corridor was 40m long and 4m wide with periodic boundary conditions.…”

Section: Simulationsmentioning

confidence: 99%

Gradient navigation model for pedestrian dynamics

Dietrich

Köster

2014

Phys. Rev. E

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We present a new microscopic ODE-based model for pedestrian dynamics: the Gradient Navigation Model. The model uses a superposition of gradients of distance functions to directly change the direction of the velocity vector. The velocity is then integrated to obtain the location. The approach differs fundamentally from force based models needing only three equations to derive the ODE system, as opposed to four in, e.g., the Social Force Model. Also, as a result, pedestrians are no longer subject to inertia. Several other advantages ensue: Model induced oscillations are avoided completely since no actual forces are present. The derivatives in the equations of motion are smooth and therefore allow the use of fast and accurate high order numerical integrators. At the same time, existence and uniqueness of the solution to the ODE system follow almost directly from the smoothness properties. In addition, we introduce a method to calibrate parameters by theoretical arguments based on empirically validated assumptions rather than by numerical tests.These parameters, combined with the accurate integration, yield simulation results with no collisions of pedestrians. Several empirically observed system phenomena emerge without the need to recalibrate the parameter set for each scenario: obstacle avoidance, lane formation, stop-and-go waves and congestion at bottlenecks. The density evolution in the latter is shown to be quantitatively close to controlled experiments. Likewise, we observe a dependence of the crowd velocity on the local density that compares well with benchmark fundamental diagrams.

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“…We use the method introduced by [38] to measure local density. [43,44] from metastudy (Weidmann,[29]). The corridor was 40m long and 4m wide with periodic boundary conditions.…”

Section: Simulationsmentioning

confidence: 99%

Gradient navigation model for pedestrian dynamics

Dietrich

Köster

2014

Phys. Rev. E

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“…Traffic source control can be implemented in a variety of ways so to modulate the flux capacity of the road, e.g. using route recommendation panels, variable speed limit regulation [26], employing integrated vehicular and roadside sensors [23] or autonomous vehicles [27]. Control strategies adopted in the process of continuous sedimentation taking place in a clarifier-thickener unit, or settler (used, for example, in waste water treatment), usually consist in modulating the inflow and outflow of the settler containing solid particles dispersed in a liquid [20,21].…”

Section: Some Applicationsmentioning

confidence: 99%

On the global controllability of scalar conservation laws with boundary and source controls

Ancona¹,

Nguyen²

2020

Preprint

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We provide global and semi-global controllability results for hyperbolic conservation laws on a bounded domain, with a general (not necessarily convex) flux and a time-dependent source term acting as a control. The results are achieved for, possibly critical, both continuously differentiable states and BV states. The proofs are based on a combination of the return method and on the analysis of the Riccati equation for the space derivative of the solution.

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“…One such model is the optimal-velocity (OV) model introduced in [7], in which each driver adjusts their acceleration to bring their velocity to an optimal velocity that depends on the headway, that is the distance from the vehicle in front. We focus on the formulation of this model as in [19] in which the position p n (t) of the nth vehicle evolves according to…”

Section: Microscopic Model: Optimal Velocitymentioning

confidence: 99%

“…The parameter 0 is a characteristic length scale which describes the pace at which the driver attempts to achieve the optimal velocity. We note that there have been studies of a variety of extensions of (4), such as allowing drivers to react also to the preceding car ( [16,17]), and allowing the optimal headways to vary deterministically or stochastically in time ( [18,19]).…”

Section: Microscopic Model: Optimal Velocitymentioning

confidence: 99%

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Assimilating Eulerian and Lagrangian data in traffic-flow models

Xia

Cochrane

DeGuire

et al. 2017

Physica D: Nonlinear Phenomena

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Data assimilation of traffic flow remains a challenging problem. One difficulty is that data come from different sources ranging from stationary sensor and camera data to GPS and cell phone data from moving cars. Sensors and cameras give information about traffic density, while GPS data provide information about the positions and velocities of individual cars. Previous methods for assimilating Lagrangian data collected from individual cars relied on specific properties of the underlying computational model or its reformulation in Lagrangian coordinates: these approaches make it hard to assimilate both Eulerian density and Lagrangian positional data simultaneously. In this paper, we propose an alternative approach that allows us to assimilate both Eulerian and Lagrangian data. We show that the proposed algorithm is accurate and works well in different traffic scenarios and regardless of whether ensemble Kalman or particle filters are used. We also show that the algorithm is capable of estimating parameters and assimilating real traffic data and data obtained from microscopic models.

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Controlling traffic jams by time modulating the safety distance

Cited by 11 publications

References 36 publications

Gradient navigation model for pedestrian dynamics

Gradient navigation model for pedestrian dynamics

On the global controllability of scalar conservation laws with boundary and source controls

Assimilating Eulerian and Lagrangian data in traffic-flow models

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