Research on Dynamic Traffic Flow Forecasting Based on Improved Particle Swarm Optimization Algorithm and Neural Network Theory

被引：1
|
作者：
Qi, Bo [1 ]
Ma, Changxi [1 ]
Sun, Li [1 ]
机构：
[1] Lanzhou Jiaotong Univ, Sch Traff & Transportat, Lanzhou 730070, Gansu, Peoples R China
来源：
SUSTAINABLE ENVIRONMENT AND TRANSPORTATION, PTS 1-4 | 2012年 / 178-181卷
关键词：
Dynamic Traffic Flow; Particle Optimization Algorithm; Nerve Network; Forecasting Model;
D O I：
10.4028/www.scientific.net/AMM.178-181.2686
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
Focused on urban traffic flow issue, the dynamic traffic flow forecasting model is established based on the improved particle optimization algorithm and nerve network theory. Taking the macro dynamic traffic flow as the model, the paper analyzes primarily the features of traffic flow by means of stage-distinguishing method, researches the improved particle optimization algorithm and nerve network theory further and establishes the dynamic traffic flow forecasting model. Finally, it utilizes this model to forecast the traffic flow on North Binghe Road in Lanzhou City. All the results demonstrate that this forecasting model is of higher prestige and proper availability.
引用
收藏
页码：2686 / 2689
页数：4
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