Optimization of Network Signal Timing
نویسندگان
چکیده
A typical urban traffic network is a complicated large-scale stochastic system which consists of many interconnected signalized traffic intersections. Thls paper develops a decentralized real-time adaptive control strategy for the traffic networks based on Markov decision theory. Computer simulation results of this new approach on a five intersection traffic network indicate significant improvement over the traditional fully actuated control algorithm.
منابع مشابه
Traffic Signal Prediction Using Elman Neural Network and Particle Swarm Optimization
Prediction of traffic is very crucial for its management. Because of human involvement in the generation of this phenomenon, traffic signal is normally accompanied by noise and high levels of non-stationarity. Therefore, traffic signal prediction as one of the important subjects of study has attracted researchers’ interests. In this study, a combinatorial approach is proposed for traffic signal...
متن کاملA Nonlinear Autoregressive Model with Exogenous Variables Neural Network for Stock Market Timing: The Candlestick Technical Analysis
In this paper, the nonlinear autoregressive model with exogenous variables as a new neural network is used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick. In this model, the “nonlinear autoregressive model with exogenous variables” is an analyzer. For a more reliable comparison, here (like the literature) two approaches of Raw-based and Signal-ba...
متن کاملOptimal Rotor Fault Detection in Induction Motor Using Particle-Swarm Optimization Optimized Neural Network
This study examined and presents an effective method for detection of failure of conductor bars in the winding of rotor of induction motor in low load conditions using neural networks of radial-base functions. The proposed method used Hilbert method to obtain the stator current signal push. The frequency and signal amplitude of the push stator were used as the input of the neural network and th...
متن کاملArtificial Neural Network Based Multi-Objective Evolutionary Optimization of a Heavy-Duty Diesel Engine
In this study the performance and emissions characteristics of a heavy-duty, direct injection, Compression ignition (CI) engine which is specialized in agriculture, have been investigated experimentally. For this aim, the influence of injection timing, load, engine speed on power, brake specific fuel consumption (BSFC), peak pressure (PP), nitrogen oxides (NOx), carbon dioxide (CO2), Carbon mon...
متن کاملRole of STDP in regulation of neural timing networks in human: a simulation study
Many physiological events require an accurate timing signal, usually generated by neural networks called central pattern generators (CPGs). On the other hand, properties of neurons and neural networks (e.g. time constants of neurons and weights of network connections) alter with time, resulting in gradual changes in timing of such networks. Recently, a synaptic weight adjustment mechanism has b...
متن کاملRole of STDP in regulation of neural timing networks in human: a simulation study
Many physiological events require an accurate timing signal, usually generated by neural networks called central pattern generators (CPGs). On the other hand, properties of neurons and neural networks (e.g. time constants of neurons and weights of network connections) alter with time, resulting in gradual changes in timing of such networks. Recently, a synaptic weight adjustment mechanism has b...
متن کامل