短时交通流预测的改进<i>K</i>近邻算法

谢海红; 戴许昊; 齐远

短时交通流预测的改进K近邻算法

谢海红^{1, 2,},
戴许昊¹,
齐远³

1.
北京交通大学城市交通复杂系统理论与技术教育部重点实验室, 北京 100044
2.
北京城市交通协同创新中心, 北京 100044
3.
湖南省交通规划勘察设计院, 湖南长沙 410008

基金项目:

国家973计划项目 2012CB725403

详细信息

作者简介:
谢海红(1963-), 女, 山东烟台人, 北京交通大学副教授, 从事城市交通规划与管理研究

中图分类号: U491.112
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出版历程
- 收稿日期: 2014-01-13
- 刊出日期: 2014-06-25

Improved K-nearest neighbor algorithm for short-term traffic flow forecasting

1.
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China
2.
Center of Cooperative Innovation for Beijing Metropolitan Transportation, Beijing 100044, China
3.
Hunan Provincial Communications Planning, Survey and Design Institute, Changsha 410008, Hunan, China

More Information

Author Bio:
XIE Hai-hong (1963-), female, associate professor, +86-10-51687138, xiehaihong16@163.com

摘要

摘要: 分析了原有的短时交通流预测的K近邻算法, 用模式距离搜索方法代替原有的欧氏距离搜索方法, 引入多元统计回归模型, 建立了一种改进的短时交通流预测的K近邻算法, 并以北京市某路段进行实例验证。试验结果表明: 当K取23时, 利用改进的K近邻算法, 预测结果的均方误差、平均相对误差、平均绝对误差分别为31.43%、4.17%、0.27%;利用原有的K近邻算法, 预测结果的均方误差、平均相对误差、平均绝对误差分别为33.33%、4.40%、0.28%;利用历史平均模型, 预测结果的均方误差、平均相对误差、平均绝对误差分别为46.20%、11.40%、0.48%。可见, 改进的K近邻算法的预测精度明显高于其他2种方法, 在提高搜索效率的同时准确地刻画了交通流的真实情况。
- 交通规划 /
- 短时交通流预测 /
- K近邻算法 /
- 模式距离 /
- 多元统计回归
Abstract: The original K-nearest neighbor algorithm for short-term traffic flow forecasting was analyzed.Pattern distance search method was used to replace the original Euclidean distance search method, the multiple statistics regression model was introduced, an improved K-nearest neighbor algorithm for short-term traffic flow forecasting was put forward, and an example verification was carried out by using the traffic flow data from a certain section in Beijing.Test result indicates when Kis 23, the error of mean square, mean absolute error and average relative error of forecasting results are 31.43%, 4.17% and 0.27% respectively by using the improved K-nearest neighbor algorithm.By using the original K-nearest neighbor algorithm, the error of mean square, mean absolute error and average relative error of forecasting results are 33.33%, 4.40% and 0.28% respectively.By using the historical average model, the error of mean square, mean absolute error and average relative error of forecasting results are 46.20%, 11.40% and 0.48% respectively.The forecasting accuracy of the improved K-nearest neighbor algorithm is obviously higher than the other two algorithms.The improved K-nearest neighbor algorithm notonly increases searching efficiency, but also accurately reflects the real situation of traffic flow.
- traffic planning /
- short-term traffic flow forecasting /
- K-nearest neighbor algorithm /
- pattern distance /
- multiple statistical regression

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图 1 算法流程

Figure 1. Algorithm flow

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图 2 多元统计回归算法流程

Figure 2. Flow of multiple statistical regression algorithm

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图 3 实测现场

Figure 3. Measurement field

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图 4 交通量

Figure 4. Traffic volumes

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图 5 ξ与K关系

Figure 5. Relationship between ξ and K

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图 6 T_m曲线

Figure 6. T_m curve

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图 7 欧氏距离搜索结果

Figure 7. Search result by using Euclidean distance

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图 8 模式距离搜索结果

Figure 8. Search result by using pattern distance

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图 9 均方误差

Figure 9. Errors of mean square

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图 10 平均绝对误差

Figure 10. Mean absolute errors

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图 11 平均相对误差

Figure 11. Average relative errors

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表 1 欧氏距离与模式距离搜索结果

Table 1. Search results with Euclidean distance and pattern distance

下载: 导出CSV

表 2 因子排序结果

Table 2. Factor order result

下载: 导出CSV

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