2 research outputs found
Fuzzy cellular model of signal controlled traffic stream
Microscopic traffic models have recently gained considerable importance as a
mean of optimising traffic control strategies. Computationally efficient and
sufficiently accurate microscopic traffic models have been developed based on
the cellular automata theory. However, the real-time application of the
available cellular automata models in traffic control systems is a difficult
task due to their discrete and stochastic nature. This paper introduces a novel
method of traffic streams modelling, which combines cellular automata and fuzzy
calculus. The introduced fuzzy cellular traffic model eliminates main drawbacks
of the cellular automata approach i.e. necessity of multiple Monte Carlo
simulations and calibration issues. Experimental results show that the
evolution of a simulated traffic stream in the proposed fuzzy cellular model is
consistent with that observed for stochastic cellular automata. The comparison
of both methods confirms that the computational cost of traffic simulation is
considerably lower for the proposed model. The model is suitable for real-time
applications in traffic control systems.Comment: 18 pages, 9 figure
Uncertainty-dependent data collection in vehicular sensor networks
Vehicular sensor networks (VSNs) are built on top of vehicular ad-hoc
networks (VANETs) by equipping vehicles with sensing devices. These new
technologies create a huge opportunity to extend the sensing capabilities of
the existing road traffic control systems and improve their performance.
Efficient utilisation of wireless communication channel is one of the basic
issues in the vehicular networks development. This paper presents and evaluates
data collection algorithms that use uncertainty estimates to reduce data
transmission in a VSN-based road traffic control system.Comment: 10 pages, 6 figure