38,522 research outputs found
Modelling supported driving as an optimal control cycle: Framework and model characteristics
Driver assistance systems support drivers in operating vehicles in a safe,
comfortable and efficient way, and thus may induce changes in traffic flow
characteristics. This paper puts forward a receding horizon control framework
to model driver assistance and cooperative systems. The accelerations of
automated vehicles are controlled to optimise a cost function, assuming other
vehicles driving at stationary conditions over a prediction horizon. The
flexibility of the framework is demonstrated with controller design of Adaptive
Cruise Control (ACC) and Cooperative ACC (C-ACC) systems. The proposed ACC and
C-ACC model characteristics are investigated analytically, with focus on
equilibrium solutions and stability properties. The proposed ACC model produces
plausible human car-following behaviour and is unconditionally locally stable.
By careful tuning of parameters, the ACC model generates similar stability
characteristics as human driver models. The proposed C-ACC model results in
convective downstream and absolute string instability, but not convective
upstream string instability observed in human-driven traffic and in the ACC
model. The control framework and analytical results provide insights into the
influences of ACC and C-ACC systems on traffic flow operations.Comment: Submitted to Transportation Research Part C: Emerging Technologie
Delays, Inaccuracies and Anticipation in Microscopic Traffic Models
We generalize a wide class of time-continuous microscopic traffic models to
include essential aspects of driver behaviour not captured by these models.
Specifically, we consider (i) finite reaction times, (ii) estimation errors,
(iii) looking several vehicles ahead (spatial anticipation), and (iv) temporal
anticipation. The estimation errors are modelled as stochastic Wiener processes
and lead to time-correlated fluctuations of the acceleration.
We show that the destabilizing effects of reaction times and estimation
errors can essentially be compensated for by spatial and temporal anticipation,
that is, the combination of stabilizing and destabilizing effects results in
the same qualitative macroscopic dynamics as that of the respectively
underlying simple car-following model. In many cases, this justifies the use of
simplified, physics-oriented models with a few parameters only. Although the
qualitative dynamics is unchanged, multi-anticipation increase both spatial and
temporal scales of stop-and-go waves and other complex patterns of congested
traffic in agreement with real traffic data. Remarkably, the anticipation
allows accident-free smooth driving in complex traffic situations even if
reaction times exceed typical time headways.Comment: Major revision of the model and the simulations. Particularly, the
number of model parameters has been reduce
Stability analysis on a dynamical model of route choice in a connected vehicle environment
Research on connected vehicle environment has been growing rapidly to investigate the effects of real-time exchange of kinetic information between vehicles and road condition information from the infrastructure through radio communication technologies. A fully connected vehicle environment can substantially reduce the latency in response caused by human perception-reaction time with the prospect of improving both safety and comfort. This study presents a dynamical model of route choice under a connected vehicle environment. We analyze the stability of headways by perturbing various factors in the microscopic traffic flow model and traffic flow dynamics in the car-following model and dynamical model of route choice. The advantage of this approach is that it complements the macroscopic traffic assignment model of route choice with microscopic elements that represent the important features of connected vehicles. The gaps between cars can be decreased and stabilized even in the presence of perturbations caused by incidents. The reduction in gaps will be helpful to optimize the traffic flow dynamics more easily with safe and stable conditions. The results show that the dynamics under the connected vehicle environment have equilibria. The approach presented in this study will be helpful to identify the important properties of a connected vehicle environment and to evaluate its benefits
Three-phase traffic theory and two-phase models with a fundamental diagram in the light of empirical stylized facts
Despite the availability of large empirical data sets and the long history of
traffic modeling, the theory of traffic congestion on freeways is still highly
controversial. In this contribution, we compare Kerner's three-phase traffic
theory with the phase diagram approach for traffic models with a fundamental
diagram. We discuss the inconsistent use of the term "traffic phase" and show
that patterns demanded by three-phase traffic theory can be reproduced with
simple two-phase models, if the model parameters are suitably specified and
factors characteristic for real traffic flows are considered, such as effects
of noise or heterogeneity or the actual freeway design (e.g. combinations of
off- and on-ramps). Conversely, we demonstrate that models created to reproduce
three-phase traffic theory create similar spatiotemporal traffic states and
associated phase diagrams, no matter whether the parameters imply a fundamental
diagram in equilibrium or non-unique flow- density relationships. In
conclusion, there are different ways of reproducing the empirical stylized
facts of spatiotemporal congestion patterns summarized in this contribution,
and it appears possible to overcome the controversy by a more precise
definition of the scientific terms and a more careful comparison of models and
data, considering effects of the measurement process and the right level of
detail in the traffic model used.Comment: 18 pages in the published article, 13 figures, 2 table
- …