61 research outputs found
A new cellular automata model for city traffic
We present a new cellular automata model of vehicular traffic in cities by
combining ideas borrowed from the Biham-Middleton-Levine (BML) model of city
traffic and the Nagel-Schreckenberg (NaSch) model of highway traffic. The model
exhibits a dynamical phase transition to a completely jammed phase at a
critical density which depends on the time periods of the synchronized signals.Comment: 6 pages, 5 figures, uses Springer Macros 'lncse', to appear in
"Traffic and Granular Flow '99: Social, Traffic, and Granular Dynamics"
edited by D. Helbing, H. J. Herrmann, M. Schreckenberg, and D. E. Wolf
(Springer, Berlin
Fuzzy cellular model for on-line traffic simulation
This paper introduces a fuzzy cellular model of road traffic that was
intended for on-line applications in traffic control. The presented model uses
fuzzy sets theory to deal with uncertainty of both input data and simulation
results. Vehicles are modelled individually, thus various classes of them can
be taken into consideration. In the proposed approach, all parameters of
vehicles are described by means of fuzzy numbers. The model was implemented in
a simulation of vehicles queue discharge process. Changes of the queue length
were analysed in this experiment and compared to the results of NaSch cellular
automata model.Comment: The original publication is available at http://www.springerlink.co
An empirical test for cellular automaton models of traffic flow
Based on a detailed microscopic test scenario motivated by recent empirical
studies of single-vehicle data, several cellular automaton models for traffic
flow are compared. We find three levels of agreement with the empirical data:
1) models that do not reproduce even qualitatively the most important empirical
observations,
2) models that are on a macroscopic level in reasonable agreement with the
empirics, and 3) models that reproduce the empirical data on a microscopic
level as well.
Our results are not only relevant for applications, but also shed new light
on the relevant interactions in traffic flow.Comment: 28 pages, 36 figures, accepted for publication in PR
Optimised Traffic Flow at a Single Intersection: Traffic Responsive signalisation
We propose a stochastic model for the intersection of two urban streets. The
vehicular traffic at the intersection is controlled by a set of traffic lights
which can be operated subject to fix-time as well as traffic adaptive schemes.
Vehicular dynamics is simulated within the framework of the probabilistic
cellular automata and the delay experienced by the traffic at each individual
street is evaluated for specified time intervals. Minimising the total delay of
both streets gives rise to the optimum signalisation of traffic lights. We
propose some traffic responsive signalisation algorithms which are based on the
concept of cut-off queue length and cut-off density.Comment: 10 pages, 11 eps figs, to appear in J. Phys.
Calibrating Car-Following Models using Trajectory Data: Methodological Study
The car-following behavior of individual drivers in real city traffic is
studied on the basis of (publicly available) trajectory datasets recorded by a
vehicle equipped with an radar sensor. By means of a nonlinear optimization
procedure based on a genetic algorithm, we calibrate the Intelligent Driver
Model and the Velocity Difference Model by minimizing the deviations between
the observed driving dynamics and the simulated trajectory when following the
same leading vehicle. The reliability and robustness of the nonlinear fits are
assessed by applying different optimization criteria, i.e., different measures
for the deviations between two trajectories. The obtained errors are in the
range between~11% and~29% which is consistent with typical error ranges
obtained in previous studies. In addition, we found that the calibrated
parameter values of the Velocity Difference Model strongly depend on the
optimization criterion, while the Intelligent Driver Model is more robust in
this respect. By applying an explicit delay to the model input, we investigated
the influence of a reaction time. Remarkably, we found a negligible influence
of the reaction time indicating that drivers compensate for their reaction time
by anticipation. Furthermore, the parameter sets calibrated to a certain
trajectory are applied to the other trajectories allowing for model validation.
The results indicate that ``intra-driver variability'' rather than
``inter-driver variability'' accounts for a large part of the calibration
errors. The results are used to suggest some criteria towards a benchmarking of
car-following models
Phase Synchronization in Railway Timetables
Timetable construction belongs to the most important optimization problems in
public transport. Finding optimal or near-optimal timetables under the
subsidiary conditions of minimizing travel times and other criteria is a
targeted contribution to the functioning of public transport. In addition to
efficiency (given, e.g., by minimal average travel times), a significant
feature of a timetable is its robustness against delay propagation. Here we
study the balance of efficiency and robustness in long-distance railway
timetables (in particular the current long-distance railway timetable in
Germany) from the perspective of synchronization, exploiting the fact that a
major part of the trains run nearly periodically. We find that synchronization
is highest at intermediate-sized stations. We argue that this synchronization
perspective opens a new avenue towards an understanding of railway timetables
by representing them as spatio-temporal phase patterns. Robustness and
efficiency can then be viewed as properties of this phase pattern
From cellular attractor selection to adaptive signal control for traffic networks
The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains
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