Delays, Inaccuracies and Anticipation in Microscopic Traffic Models

TL;DR

This paper extends microscopic traffic models to include realistic driver behaviors like reaction times, estimation errors, and anticipation, showing how these factors influence traffic stability and pattern formation.

Contribution

It introduces a generalized framework incorporating anticipation and stochastic errors, demonstrating their effects on traffic dynamics and stability in microscopic models.

Findings

Anticipation stabilizes traffic despite reaction delays.

Multi-anticipation increases wave and pattern scales.

Anticipation enables smooth, accident-free driving in complex scenarios.

Abstract

We generalize a wide class of time-continuous microscopic traffic models to include essential aspects of driver behaviour not captured by these models. Specifically, we consider (i) finite reaction times, (ii) estimation errors, (iii) looking several vehicles ahead (spatial anticipation), and (iv) temporal anticipation. The estimation errors are modelled as stochastic Wiener processes and lead to time-correlated fluctuations of the acceleration. We show that the destabilizing effects of reaction times and estimation errors can essentially be compensated for by spatial and temporal anticipation, that is, the combination of stabilizing and destabilizing effects results in the same qualitative macroscopic dynamics as that of the respectively underlying simple car-following model. In many cases, this justifies the use of simplified, physics-oriented models with a few parameters only. Although the qualitative dynamics is unchanged, multi-anticipation increase both spatial and temporal scales of stop-and-go waves and other complex patterns of congested traffic in agreement with real traffic data. Remarkably, the anticipation allows accident-free smooth driving in complex traffic situations even if reaction times exceed typical time headways.