Fluid-dynamical and microscopic description of traffic flow: a data-driven comparison

A lot of work has been done to compare traffic flow models with reality; so far, this has been done separately for microscopic as well as for fluid-dynamical models of traffic flow. This paper compares directly the performance of both types of models to real data. The results indicate, that microscopic models on average seem to have a tiny advantage over fluid-dynamical models, however one may admit that for most applications the differences between the two are small. Furthermore, the relaxation time of the fluid-dynamical models turns out to be fairly small, of the order of two seconds, and are comparable with the results for the microscopic models. This indicates that the second order terms are weak, however, the calibration results indicate that the speed equation is in fact important and improves the calibration results of the models

How human drivers control their vehicle

The data presented here show that human drivers apply a discrete noisy control mechanism to drive their vehicle. A car-following model built on these observations, together with some physical limitations (crash-freeness, acceleration), led to non-Gaussian probability distributions in the speed difference and distance which are in good agreement with empirical data. All model parameters have a clear physical meaning and can be measured. Despite its apparent complexity, this model is simple to understand and might serve as a starting point to develop even quantitatively correct models.Comment: 5 pages, 6 figures, submitted to PR

Agent Based Traffic Signals Regulating Flow On a Basic Grid

A simulation study on traffic light optimisation with agent-based behaviour of the traffic signals