Action-points in human driving and in SUMO

Abstract

When following a vehicle, drivers change their acceleration at so called action-points (AP), and keep it constant in between them. By investigating a large data-set of car-following data, the state- and time-distributions of the APs is analyzed. In the state-space spanned by speed-difference and distance to the lead vehicle, this distribution of APs is mostly proportional to the distribution of all data-points, with small deviations from this. Therefore, the APs are not concentrated around certain thresholds as is claimed by psycho-physical car-following models.Instead, small distances indicate a slightly higher probability of finding an AP than is the case for large distances. A SUMO simulation with SUMO's implementation of the Wiedemann model confirms this view: the AP's of the Wiedemann model follow a completely different distribution than the empirical ones.