Hysteresis phenomena of the intelligent driver model for traffic flow

A New Driver Model Based on Driver Response

In this paper, a new microscopic traffic model based on forward and rearward driver response is proposed. Driver response is characterized using the distance and time headways. Existing models such as the Intelligent Driver (ID) model characterize traffic flow based on a constant acceleration exponent. This exponent reflects uniform driver behaviour during different conditions which is unrealistic. Driver response is slow with a large distance headway and quick with a short time headway. Conversely, it is quick with a small distance headway and slow with a long time headway. Thus, a new microscopic traffic model is proposed which incorporates driver response. Results are given that show the proposed model provides better traffic stability than the ID model as this stability is based on traffic physics. Further, for effective utilization of road infrastructure, shorter time and longer distance headways are preferred. The performance of the ID and proposed models was evaluated over an 800 m circular road with a string of 15 vehicles for 120 s. These models are numerically discretized using the Euler scheme. The results obtained show that traffic queue dissemination with the proposed model is more realistic than with the ID model and the changes in density with the proposed model are smaller. This is because traffic dissemination with the proposed model is based on traffic parameters rather than a constant exponent.

Saddle-node bifurcation to jammed state for quasi-one-dimensional counter-chemotactic flow

The transition of a counter-chemotactic particle flow from a free-flow state to a jammed state in a quasi-one-dimensional path is investigated. One of the characteristic features of such a flow is that the constituent particles spontaneously form a cluster that blocks the path, called a path-blocking cluster (PBC), and causes a jammed state when the particle density is greater than a threshold value. Near the threshold value, the PBC occasionally collapses on itself to recover the free flow. In other words, the time evolution of the size of the PBC governs the flux of a counter-chemotactic flow. In this Rapid Communication, on the basis of numerical results of a stochastic cellular automata (SCA) model, we introduce a Langevin equation model for the size evolution of the PBC that reproduces the qualitative characteristics of the SCA model. The results suggest that the emergence of the jammed state in a quasi-one-dimensional counterflow is caused by a saddle-node bifurcation.