Jam-Avoiding Adaptive Cruise Control (ACC) and its Impact on Traffic Dynamics
Adaptive-Cruise Control (ACC) automatically accelerates or decelerates a vehicle to maintain a selected time gap, to reach a desired velocity, or to prevent a rear-end collision. To this end, the ACC sensors detect and track the vehicle ahead for measuring the actual distance and speed difference. Together with the own velocity, these input variables are exactly the same as in car-following models. The focus of this contribution is: What will be the impact of a spreading of ACC systems on the traffic dynamics? Do automated driving strategies have the potential to improve the capacity and stability of traffic flow or will they necessarily increase the heterogeneity and instability? How does the result depend on the ACC equipment level?
We discuss microscopic modeling aspects for human and automated (ACC) driving. By means of microscopic traffic simulations, we study how a variable percentage of ACC-equipped vehicles influences the stability of traffic flow, the maximum flow under free traffic conditions until traffic breaks down, and the dynamic capacity of congested traffic. Furthermore, we compare different percentages of ACC with respect to travel times in a specific congestion scenario. Remarkably, we find that already a small amount of ACC equipped cars and, hence, a marginally increased free and dynamic capacity, leads to a drastic reduction of traffic congestion.
Unable to display preview. Download preview PDF.
- 1.“European Commission (Energy & Transport), White Paper European transport policy for 2010: time to decide,”, COM (2001) 370 final.Google Scholar
- 2.M. Minderhoud, Supported Driving: Impacts on Motorway Traffic Flow (Delft University Press, Delft, 1999).Google Scholar
- 6.B. S. Kerner, The Physics of Traffic (Springer, Heidelberg, 2004).Google Scholar
- 11.B. Tilch and D. Helbing, “Evaluation of single vehicle data in dependence of the vehicle-type, lane, and site”, in Traffic and Granular Flow’ 99, D. Helbing, H. Herrmann, M. Schreckenberg, and D. Wolf, eds., (Springer, Berlin, 2000), pp. 333–338.Google Scholar
- 14.M. Treiber, A. Kesting, and D. Helbing, “Delays, inaccuracies and anticipation in microscopic traffic models”, Physica A 359, 729–746 (2006).Google Scholar
- 16.C. Daganzo, M. Cassidy, and R. Bertini, “Possible explanations of phase transitions in highway traffic”, Transportation Research B 33, 365–379 (1999).Google Scholar
- 17.M. Treiber, A. Kesting, and D. Helbing, “Variance-driven traffic dynamics and statistical aspects of single-vehicle data”, in this volume.Google Scholar
- 18.M. Schönhof, A. Kesting, M. Treiber, and D. Helbing, “Inter-Vehicle Communication on highways: Statistical properties of information propagation”, in this volume.Google Scholar