Understanding Traffic Jams Using Lindblad Superoperators

Traffic Jams Detection Using Flock Mining

The widespread use of GPS devices on cars enables the collection of time-dependent positions of vehicles and, hence, of their movements on the road network. It is possible to analyze such huge collection of data to look for critical situation on the traffic flow. The offline analysis of traffic congestions represents a challenging task for urban mobility managers. This kind of analysis can be u...

Deterministic models for traffic jams

We study several deterministic one-dimensional traffic models. For integer positions and velocities we find the typical high and low density phases separated by a simple transition. If positions and velocities are continuous variables the model shows self-organized criticality driven by the slowest car.

Traffic jams: dynamics and control.

This introductory paper reviews the current state-of-the-art scientific methods used for modelling, analysing and controlling the dynamics of vehicular traffic. Possible mechanisms underlying traffic jam formation and propagation are presented from a dynamical viewpoint. Stable and unstable motions are described that may give the skeleton of traffic dynamics, and the effects of driver behaviour...

Derivation of nonlinear single-particle equations via many-body Lindblad superoperators: A density-matrix approach

The impacts of molecular motor traffic jams.

M uch of a modern person’s day is spent trying to get from point A to point B. So, too, in the cell, much time and energy is expended on shuttling organelles, protein complexes, and mRNA, called “cargos,” from point A to point B. We know that vehicle traffic slows down when roads get jammed with high-volume congestion. The traffic analogy begs the question: Do cellular highways get jammed? This...