276 research outputs found
Performance Evaluation of Road Traffic Control Using a Fuzzy Cellular Model
In this paper a method is proposed for performance evaluation of road traffic
control systems. The method is designed to be implemented in an on-line
simulation environment, which enables optimisation of adaptive traffic control
strategies. Performance measures are computed using a fuzzy cellular traffic
model, formulated as a hybrid system combining cellular automata and fuzzy
calculus. Experimental results show that the introduced method allows the
performance to be evaluated using imprecise traffic measurements. Moreover, the
fuzzy definitions of performance measures are convenient for uncertainty
determination in traffic control decisions.Comment: The final publication is available at http://www.springerlink.co
Coherent Moving States in Highway Traffic (Originally: Moving Like a Solid Block)
Recent advances in multiagent simulations have made possible the study of
realistic traffic patterns and allow to test theories based on driver
behaviour. Such simulations also display various empirical features of traffic
flows, and are used to design traffic controls that maximise the throughput of
vehicles in heavily transited highways. In addition to its intrinsic economic
value, vehicular traffic is of interest because it may throw light on some
social phenomena where diverse individuals competitively try to maximise their
own utilities under certain constraints.
In this paper, we present simulation results that point to the existence of
cooperative, coherent states arising from competitive interactions that lead to
a new phenomenon in heterogeneous highway traffic. As the density of vehicles
increases, their interactions cause a transition into a highly correlated state
in which all vehicles practically move with the same speed, analogous to the
motion of a solid block. This state is associated with a reduced lane changing
rate and a safe, high and stable flow. It disappears as the vehicle density
exceeds a critical value. The effect is observed in recent evaluations of Dutch
traffic data.Comment: Submitted on April 21, 1998. For related work see
http://www.theo2.physik.uni-stuttgart.de/helbing.html and
http://www.parc.xerox.com/dynamics
Modeling and Predicting Future Trajectories of Moving Objects in a Constrained Network
http://ieeexplore.ieee.org/Advances in wireless sensor networks and positioning technologies enable traffic management (e.g. routing traffic) that uses real-time data monitored by GPS-enabled cars. Location management has become an enabling technology in such application. The location modeling and trajectory prediction of moving objects are the fundamental components of location management in mobile locationaware applications. In this paper, we model the road network and moving objects in a graph of cellular automata (GCA), which makes full use of the constraints of the network and the stochastic behavior of the traffic. A simulation-based method based on graphs of cellular automata is proposed to predict future trajectories. Our technique strongly differs from the linear prediction method, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changes. The experiments, carried on two different datasets, show that the simulation-based prediction method provides higher accuracy than the linear prediction method
Comparative model accuracy of a data-fitted generalized Aw-Rascle-Zhang model
The Aw-Rascle-Zhang (ARZ) model can be interpreted as a generalization of the
Lighthill-Whitham-Richards (LWR) model, possessing a family of fundamental
diagram curves, each of which represents a class of drivers with a different
empty road velocity. A weakness of this approach is that different drivers
possess vastly different densities at which traffic flow stagnates. This
drawback can be overcome by modifying the pressure relation in the ARZ model,
leading to the generalized Aw-Rascle-Zhang (GARZ) model. We present an approach
to determine the parameter functions of the GARZ model from fundamental diagram
measurement data. The predictive accuracy of the resulting data-fitted GARZ
model is compared to other traffic models by means of a three-detector test
setup, employing two types of data: vehicle trajectory data, and sensor data.
This work also considers the extension of the ARZ and the GARZ models to models
with a relaxation term, and conducts an investigation of the optimal relaxation
time.Comment: 30 pages, 10 figures, 3 table
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