Dynamic shortest path in stochastic traffic networks based on fluid neural network and Particle Swarm Optimization

Deng, Yupu; Tong, Hengqing; Zhang, Xiedong

doi:10.1109/icnc.2010.5584513

Cited by 14 publications

(4 citation statements)

References 15 publications

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“…Then X (0) and P (0) are set as 0 vectors. And the input is used for parameter estimation through Kalman Filtering Algorithm [17]. Finally traffic flows are forecast.…”

Section: Methodsmentioning

confidence: 99%

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

Tang

Liang

Huang

et al. 2016

Computational Intelligence and Neuroscience

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The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction.

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“…Then X (0) and P (0) are set as 0 vectors. And the input is used for parameter estimation through Kalman Filtering Algorithm [17]. Finally traffic flows are forecast.…”

Section: Methodsmentioning

confidence: 99%

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

Tang

Liang

Huang

et al. 2016

Computational Intelligence and Neuroscience

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“…3.1.3 Dynamic optimal path finding. There have been many models proposed recently to optimize the routing of the whole traffic network by searching for the best path for each individual such as the models by Deng et al, 25 Hua and Pei, 26 Nannicini et al, 27 Efentakis et al, 28 This paper introduces an algorithm to dynamically optimize the routing at the system level of a transportation network. In this algorithm, each individual will be recommended to follow a path which depends on the current traffic capacity and the user preferences such that the overall throughput of the traffic network is maximized as well as the user preferences being satisfied.…”

Section: Behaviormentioning

confidence: 99%

An agent-based model for simulation of traffic network status: Applied to Hanoi city

Nguyễn

2016

SIMULATION

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In recent times there have been many agent-based simulation models proposed for transportation network simulation. Intuitively, these models are applicable for the transportation simulation of modern cities in developed countries where the transportation network is very well organized: Roads are separated into lanes; most transportation occurs using vehicles; and almost all drivers respect the circulation rule. However, these models are not suitable for developing countries where the transportation network is unorganized. The reasons for this are as follows: (1) there is no lane on the road; (2) most vehicles are motorbikes; and (3) not many drivers respect the circulation rule. This paper introduces an agent-based model for transportation network simulation in which each form of transport is modeled as an agent exhibiting full features of unorganized circulation behavior. The model is designed for a large scale and instead of displaying the circulation for all individual modes of transport, the model displays only the status of the traffic network. The simulation of circulation is therefore considered as a background process. The simulation is launched and the results are obtained before being displayed. This model is applied to the traffic network of Hanoi to analyze the hot or bottle neck points on the transportation network during rush hours in the city.

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“…It is based on the natural process of group communication to share individual knowledge when a group of birds or insects search food or migrate and so forth in a searching space, although all birds or insects do not know where the best position is. But from the nature of the social behavior, if any member can find out a desirable path to go, the rest of the members will follow quickly [6] [7].…”

Section: Accelerated Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Performance Analysis of Mimo Radar Waveform Using Accelerated Particle Swarm Optimization Algorithm

Reddy¹,

.M²

2012

SIPIJ

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The Accelerated Particle Swarm Optimization Algorithm is promoted to numerically design orthogonal Discrete Frequency Waveforms and Modified Discrete Frequency Waveforms (DFCWs) with good correlation properties for MIMO radar. We employ Accelerated Particle Swarm Optimization algorithm (ACC_PSO), Particles of a swarm communicate good positions, velocity and accelerations to each other as well as dynamically adjust their own position, velocity and acceleration derived from the best of all particles. The simulation results show that the proposed algorithm is effective for the design of DFCWs signal used in MIMO radar. KEYWORDSMultiple Input and Multiple Output (MIMO) Radar, Discrete Frequency Coded Waveform (DFCW), Accelerated Particle Swarm Optimization Algorithm (ACC_PSO).

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Dynamic shortest path in stochastic traffic networks based on fluid neural network and Particle Swarm Optimization

Cited by 14 publications

References 15 publications

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

An agent-based model for simulation of traffic network status: Applied to Hanoi city

Performance Analysis of Mimo Radar Waveform Using Accelerated Particle Swarm Optimization Algorithm

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