| 基于短时交通流量预测对路网微观交通仿真的研究 |
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| 引用本文： | 张虎.基于短时交通流量预测对路网微观交通仿真的研究[J].苏州市职业大学学报,2021(1):6-10. |
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| 作者姓名： | 张虎 |
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| 作者单位： | 苏州高等职业技术学校机电工程系 |
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| 摘 要： | 针对早高峰短时交通流量预测数据少、波动大的特点,提出用灰色模型进行预测。将灰色GM(2,1)改进为灰色GM(2,1,λ,ρ)预测模型,以提高预测精度。针对粒子群算法(PSO)的早熟现象,将Logistic混沌搜索嵌入到PSO算法,应用混沌粒子群算法(CPSO)寻找灰色GM(2,1,λ,ρ)预测模型最优的参数λ和ρ。结合两者提出了基于CPSO–GM(2,1,λ,ρ)的早高峰短时交通流预测模型。利用VISSIM对研究路网进行微观交通仿真,通过VISSIM–Excel、VBA–Matlab平台实现了短时交通流量预测和路网微观交通仿真数据的交互,对集成交通控制系统的架构进行了方案设计。仿真结果表明,结合流量预测的路网优于固定信号配时下的路网仿真。
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| 关 键 词： | 灰色模型 粒子群算法 短时交通流预测 交通控制 |
Road Network Traffic Simulation Based on Short-term Traffic Flow Prediction |
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| ZHANG Hu.Road Network Traffic Simulation Based on Short-term Traffic Flow Prediction[J].Journal of Suzhou Vocational University,2021(1):6-10. |
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| Authors: | ZHANG Hu |
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| Institution: | (Department of Mechanical and Electrical Engineering,Suzhou Higher Vocational School,Suzhou 215009,China) |
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| Abstract: | To solve the problem of less and changeable data of the early peak short-term traffic flow prediction,the paper proposes a gray prediction model.First,gray GM(2,1)is replaced by grey GM(2,1,λ,ρ)prediction model to improve the prediction accuracy.Then,the Logistic chaotic search is embedded into the PSO algorithm to prevent the precocious of the PSO.This paper discovers the optimal parametersλandρof gray GM(2,1,λ,ρ)prediction model through chaotic particle swarm optimization(CPSO).Combining the above two methods,we propose the short-term traffic flow prediction model based on CPSO–GM(2,1,λ,ρ).At last,we use VISSIM to simulate the micro traffic.The paper acquires an interactive data about short-term traffic flow forecasting and network of traffic simulation through VISSIM–Excel VBA–Matlab platform,and also makes a design about the architecture of integrated traffic control system.Simulation results show that the traffic prediction combined with traffic flow is better than that based on fixed signal timing. |
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| Keywords: | gray model particle swarm algorithm short-term traffic flow prediction traffic control |
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