Wiedemann Revisited

Abstract

A new data-driven stochastic car-following model based on the principles of psychospacing or action-point modeling is presented. It uses empirical or experimental trajectory data and mimics the main microscopic behavioral characteristics present in the data. In the action-point model, regions are defined in the relative speed–distance headway plane, in which the follower is likely to perform an action (increase or decrease acceleration) or not. These regions can be established empirically from vehicle trajectory data and thereby yield a joint cumulative probability distribution function of the action points. Furthermore, the conditional distribution of the actions (the size of the acceleration or deceleration given the current distance headway and relative speed or given the acceleration before the action) can be determined from these data as well. To assess the data correctly, a new filtering technique is proposed. The main hypothesis behind this idea is that the speed profile is a continuous piecewise linear function: accelerations are piecewise constant changing values at nonequidistant discrete time instants. The durations of these constant acceleration periods are not fixed but depend on the state of the follower in relation to its leader. The data analysis illustrates that driving behavior shows nonequidistant constant acceleration periods. The distributions of the action points and the conditional accelerations form the core of the presented data-driven stochastic model. The mathematical formalization that describes how these distributions can be used to simulate car-following behavior is presented. Empirical data collected on a Dutch motorway are used to illustrate the workings of the approach and the simulation results.