短时交通流预测模型

樊娜; 赵祥模; 戴明; 安毅生

doi:10.19818/j.cnki.1671-1637.2012.04.015

短时交通流预测模型

doi: 10.19818/j.cnki.1671-1637.2012.04.015

樊娜^1,,
赵祥模¹,
戴明^{1, 2},
安毅生¹

1.
长安大学信息工程学院，陕西西安 710064
2.
中国交通通信信息中心，北京 100011

基金项目:

国家自然科学基金项目 50978030

长江学者和创新团队发展计划项目 IRT0951

陕西省自然科学基金项目 2009-jm8002-1

中央高校基本科研业务费专项资金项目 CHD2011JC027

中央高校基本科研业务费专项资金项目 CHD2011JC056

详细信息

作者简介:
樊娜(1978-), 女, 陕西渭南人, 长安大学讲师, 工学博士, 从事交通信息控制研究

中图分类号: U491.14
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- 被引次数: 0
出版历程
- 收稿日期: 2012-02-07
- 刊出日期: 2012-08-25

Short-term traffic flow prediction model

1.
School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
2.
China Transportation Telecommunication and Information Center, Beijing 100011, China

More Information

Author Bio:
FAN Na (1978-), female, lecturer, PhD, +86-29-82334763, fnsea@163.com

摘要

摘要: 针对短时交通流变化周期性与随机性的特点, 提出了新的混合预测模型, 包含非参数回归模型与BP神经网络模型2种单项模型。非参数回归模型利用相关历史交通流数据, 通过数据库匹配操作, 确定预测结果, 以充分体现交通流的周期稳定性。采用3层BP神经网络模型反映交通流的动态与非线性特点。采用模糊控制算法确定各单项模型的权重, 并按不同权重有效组合成新的混合模型。采用西安市某路段30 d的交通流量数据验证混合模型的预测效果。试验结果表明: 该混合模型的平均相对误差为1.26%, 最大相对误差为3.53%, 其预测精度明显高于单项模型单独预测时的精度, 能较准确地反映交通流真实情况。
- 短时交通流预测 /
- 混合模型 /
- 非参数回归 /
- BP神经网络 /
- 模糊控制
Abstract: A new hybrid prediction model including two single models of nonparametric regression model and BP neural network model was proposed according to the periodicity and randomness properties of short-term traffic flow.Relevant historical traffic flow data were used in nonparametric regression model to make the prediction result abtained from the databases matching proceeding fully illustrate the cyclical stability of traffic flow.Three-tier BP neural network model was used to reflect the dynamic and nonlinear characters of traffic flow.Fuzzy control algorithm was adopted to get the weight coefficient of each model.New mixed model was constituted by the two single models according to different weight coefficients.The prediction effect of hybrid prediction model was verified by the traffic flow data in 30 d from a certain section in Xi'an.Experimental result indicates that the average relative error of mixed model is 1.26%, and its maximum relative error is 3.53%, so the prediction accuracy of mixed model is obviously higher than two single models, and can accurately reflect the real situation of traffic flow.
- short-term traffic flow perdiction /
- hybrid model /
- nonparametric regression /
- BP neural network /
- fuzzy control

HTML全文

图 1 非参数回归模型预测流程

Figure 1. Prediction procedure of nonparametric regression model

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图 2 预测路段与上游路段交通流量的关系

Figure 2. Relationship of traffic flows between prediction section and upstream sections

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图 3 试验路段

Figure 3. Test sections

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图 4 三种模型预测结果对比

Figure 4. Comparison of prediction result in 3 models

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图 5 预测结果与实测数据对比

Figure 5. Comparison between prediction result and real data

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表 1 模糊变量的定义

Table 1. Definitions of fuzzy variables

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表 2 部分模糊规则定义

Table 2. Definitions of partial fuzzy rules

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表 3 中间层神经元个数

Table 3. Neuron numbers of middle layer

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表 4 输入量的预测结果

Table 4. Prediction result of inputs

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表 5 评价指标对比

Table 5. Comparison of evaluation indexes

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表 6 不同数据库规模下3种方法的预测结果

Table 6. Prediction results of three methods under different database sizes

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