Memory effects in microscopic traffic models and wide scattering in flow-density data
Martin Treiber, Dirk Helbing

TL;DR
This study demonstrates through microscopic simulations that driver memory effects and measurement methods can explain the inverse-$\\lambda$ shape and wide scattering in flow-density data of congested traffic, aligning well with empirical observations.
Contribution
It introduces a novel memory effect in car-following models by coupling a driver adaptation variable to model traffic behavior more accurately.
Findings
Simulated flow-density data matches empirical inverse-$\lambda$ shape.
Memory effects cause wide scattering even in deterministic models.
Model reproduces stochasticity and shape of real traffic data.
Abstract
By means of microscopic simulations we show that non-instantaneous adaptation of the driving behaviour to the traffic situation together with the conventional measurement method of flow-density data can explain the observed inverse- shape and the wide scattering of flow-density data in ``synchronized'' congested traffic. We model a memory effect in the response of drivers to the traffic situation for a wide class of car-following models by introducing a new dynamical variable describing the adaptation of drivers to the surrounding traffic situation during the past few minutes (``subjective level of service'') and couple this internal state to parameters of the underlying model that are related to the driving style. % For illustration, we use the intelligent-driver model (IDM) as underlying model, characterize the level of service solely by the velocity and couple the internal…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
