Utilize Improved Particle Swarm to Predict Traffic Flow

Hong Ying Liu

doi:10.4028/www.scientific.net/AMR.756-759.3744

Paper Titles

Study on Predistortion Techniques and Adaptive Modulation Used in Fading Channels
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Traceroute-Based Route Gathering and Repairing Mechanism of Client-Independent IP Geolocation System
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Application of Improved DV-Hop Localization Algorithm in Port Container Positioning
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A Blind Watermarking Algorithm Research Based on Fast DCT
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Utilize Improved Particle Swarm to Predict Traffic Flow
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A Support State Marker Method for Detecting Discontinuities in Simulation of Dynamic Systems
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An Energy-Efficient Routing Algorithm for Disruption Tolerant Networks
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Predicting Protein Subcellular Localization Using the Algorithm of Diversity Finite Coefficient Combined with Artificial Neural Network
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Distributed Heterogeneous Database System Synchronization Problems in Intelligent Building
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759Utilize Improved Particle Swarm to Predict Traffic...

Utilize Improved Particle Swarm to Predict Traffic Flow

Abstract:

Presented an improved particle swarm optimization algorithm, introduced a crossover operation for the particle location, interfered the particles speed, made inert particles escape the local optimum points, enhanced PSO algorithm's ability to break away from local extreme point. Utilized improved algorithms to train the RBF neural network models, predict short-time traffic flow of a region intelligent traffic control. Simulation and test results showed that, the improved algorithm can effetely forecast short-time traffic flow of the regional intelligent transportation control, forecasting effects is better can be effectively applied to actual traffic control.

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Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

3744-3748

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.3744

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Online since:

September 2013

Authors:

Hong Ying Liu

Keywords:

Improved Particle Swarm, RBF Neural Network, Traffic Flow Prediction

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