46 research outputs found
Traffic flow on realistic road networks with adaptive traffic lights
We present a model of traffic flow on generic urban road networks based on
cellular automata. We apply this model to an existing road network in the
Australian city of Melbourne, using empirical data as input. For comparison, we
also apply this model to a square-grid network using hypothetical input data.
On both networks we compare the effects of non-adaptive vs adaptive traffic
lights, in which instantaneous traffic state information feeds back into the
traffic signal schedule. We observe that not only do adaptive traffic lights
result in better averages of network observables, they also lead to
significantly smaller fluctuations in these observables. We furthermore compare
two different systems of adaptive traffic signals, one which is informed by the
traffic state on both upstream and downstream links, and one which is informed
by upstream links only. We find that, in general, both the mean and the
fluctuation of the travel time are smallest when using the joint
upstream-downstream control strategy.Comment: 41 pages, pdflate
Statistical Physics of Vehicular Traffic and Some Related Systems
In the so-called "microscopic" models of vehicular traffic, attention is paid
explicitly to each individual vehicle each of which is represented by a
"particle"; the nature of the "interactions" among these particles is
determined by the way the vehicles influence each others' movement. Therefore,
vehicular traffic, modeled as a system of interacting "particles" driven far
from equilibrium, offers the possibility to study various fundamental aspects
of truly nonequilibrium systems which are of current interest in statistical
physics. Analytical as well as numerical techniques of statistical physics are
being used to study these models to understand rich variety of physical
phenomena exhibited by vehicular traffic. Some of these phenomena, observed in
vehicular traffic under different circumstances, include transitions from one
dynamical phase to another, criticality and self-organized criticality,
metastability and hysteresis, phase-segregation, etc. In this critical review,
written from the perspective of statistical physics, we explain the guiding
principles behind all the main theoretical approaches. But we present detailed
discussions on the results obtained mainly from the so-called
"particle-hopping" models, particularly emphasizing those which have been
formulated in recent years using the language of cellular automata.Comment: 170 pages, Latex, figures include
Traffic pattern prediction in cellular networks.
PhDIncreasing numbers of users together with a more use of high bit-rate services complicate radio resource management in 3G systems. In order to improve the system capacity and guarantee the QoS, a large amount of research had been carried out on radio resource management. One viable approach reported is to use semi-smart antennas to dynamically change the radiation pattern of target cells to reduce congestion.
One key factor of the semi-smart antenna techniques is the algorithm to adjust the beam pattern to cooperatively control the size and shape of each radio cell. Methods described in the literature determine the optimum radiation patterns according to the current observed congestion. By using machine learning methods, it is possible to detect the upcoming change of the traffic patterns at an early stage and then carry out beamforming optimization to alleviate the reduction in network performance.
Inspired from the research carried out in the vehicle mobility prediction field, this work learns the movement patterns of mobile users with three different learning models by analysing the movement patterns captured locally. Three different mobility models are introduced to mimic the real-life movement of mobile users and provide analysable data for learning.
The simulation results shows that the error rates of predictions on the geographic distribution of mobile users are low and it is feasible to use the proposed learning models to predict future traffic patterns. Being able to predict these patterns mean that the optimized beam patterns could be calculated according to the predicted traffic patterns and loaded to the relevant base stations in advance
Vehicle-based modelling of traffic . Theory and application to environmental impact modelling
This dissertation addresses vehicle-based approaches to traffic flow modelling. Having regard to the inherent dynamic nature of traffic, the investigations are mainly focused on the question, how this is captured by different model classes. In the first part, the dynamics of a microscopic car-following model (SKM), presented in, is studied by means of computer simulations and analytical calculations. A classification of the model's behaviour is given with respect to the stability of high-flow states and the outflow from jam. The effects of anticipatory driving on the model's dynamics is explored, yielding results valid in general for this model class. In the second part, a new approach is introduced based on queueing theory. It can be regarded as a microscopic implementation of a state-dependent queueing model, using coupled queues where the service rates additionally depend on the conditions downstream. The concept is shown to reproduce the dynamics of free flow and wide-moving jams. This is demonstrated by comparison with the SKM and real world measurements. An analytical treatment is given as well. The phenomena of boundary induced phase transitions is further addressed, giving the complete phase diagrams of both models. Finally, the application of the queueing approach within simulation-based traffic assignment is demonstrated in regard to environmental impact modelling
A comparative study of Macroscopic Fundamental Diagrams of arterial road networks governed by adaptive traffic signal systems
Using a stochastic cellular automaton model for urban traffic flow, we study
and compare Macroscopic Fundamental Diagrams (MFDs) of arterial road networks
governed by different types of adaptive traffic signal systems, under various
boundary conditions. In particular, we simulate realistic signal systems that
include signal linking and adaptive cycle times, and compare their performance
against a highly adaptive system of self-organizing traffic signals which is
designed to uniformly distribute the network density. We find that for networks
with time-independent boundary conditions, well-defined stationary MFDs are
observed, whose shape depends on the particular signal system used, and also on
the level of heterogeneity in the system. We find that the spatial
heterogeneity of both density and flow provide important indicators of network
performance. We also study networks with time-dependent boundary conditions,
containing morning and afternoon peaks. In this case, intricate hysteresis
loops are observed in the MFDs which are strongly correlated with the density
heterogeneity. Our results show that the MFD of the self-organizing traffic
signals lies above the MFD for the realistic systems, suggesting that by
adaptively homogenizing the network density, overall better performance and
higher capacity can be achieved.Comment: 35 pages, 14 figure