Multi-anticipative driving in microscopic traffic models
Abstract
We generalize a wide class of time-continuous microscopic traffic models to include essential aspects of traffic dynamics not captured by these models, but relevant for human drivers. Specifically, we consider (i) finite reaction times and limited attention, (ii) errors in estimating the input variables, (iii) looking several vehicles ahead (spatial anticipation), (iv) temporal anticipation, and (v) long-term adaptation to the global traffic situation. The estimation errors are modelled as stochastic Wiener processes and lead to time-correlated fluctuations of the acceleration. By means of simulations based on the intelligent-driver model we show that the destabilizing effects of reaction times, limited attention, and estimation errors can be compensated for by spatial and temporal ''multi-anticipation''. We give quantitative details for this compensation. Remarkably, the anticipation mechanisms allow accident-free smooth driving in complex traffic situations even if reaction times exceed typical time headways. The combined effects of stabilizing and destabilizing effects result in the same qualitative macroscopic dynamics as that of the underlying simple car-following model which, in many cases, justifies the use of these simple models. However, the effects of multi-anticipation increase both spatial and temporal scales of stop-and-go waves and smoothes the transition zones between free and congested traffic states, in agreement with real traffic data.Keywords
All Related Versions
This publication has 0 references indexed in Scilit: