The Intelligent Driver Model with Stochasticity -- New Insights Into Traffic Flow Oscillations
Martin Treiber, Arne Kesting

TL;DR
This paper extends the Intelligent Driver Model by incorporating stochasticity and action points, revealing how noise and human perception thresholds influence traffic oscillations and wave formation.
Contribution
It introduces a minimal model combining three oscillation mechanisms and analytically and empirically demonstrates their relative importance in different traffic regimes.
Findings
External noise causes anticipatory speed correlations in stable traffic.
Flow instabilities dominate freeway oscillations, while noise and action points are key in city and bicycle traffic.
Noise can induce traffic waves even in single-file bicycle traffic in the subcritical regime.
Abstract
Traffic flow oscillations, including traffic waves, are a common yet incompletely understood feature of congested traffic. Possible mechanisms include traffic flow instabilities, indifference regions or finite human perception thresholds (action points), and external acceleration noise. However, the relative importance of these factors in a given situation remains unclear. We bring light into this question by adding external noise and action points to the Intelligent Driver Model and other car-following models thereby obtaining a minimal model containing all three oscillation mechanisms. We show analytically that even in the subcritical regime of linearly stable flow (order parameter ), external white noise leads to spatiotemporal speed correlations "anticipating" the waves of the linearly unstable regime. Sufficiently far away from the threshold, the amplitude scales with…
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