Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (3): 705-713.doi: 10.13229/j.cnki.jdxbgxb20171075

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Multi⁃source traffic data fusion algorithm based onmind evolutionary algorithm optimization

Lei CHEN(),Jiang⁃feng WANG(),Yuan⁃li GU,Xue⁃dong YAN   

  1. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2017-11-07 Online:2019-05-01 Published:2019-07-12
  • Contact: Jiang?feng WANG E-mail:16114206@bjtu.edu.cn;wangjiangfeng@bjtu.edu.cn

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Abstract:

Considering the limitation of insufficient accuracy and poor stability of multi?sensor traffic data fusion algorithm, a mind evolutionary data fusion algorithm based on BP neural network is presented. Based on the introduction of the basic principle, the calculation steps of the proposed fusion algorithm for optimizing the initial weight and threshold of network are designed by using the “convergence” and “alienation” operations. This makes it possible to find out the best fusion model. The empirical analysis using the interval velocity indicates that the proposed fusion algorithm shows better convergence and has better accuracy and stability than the BP neural network algorithm, which are improved by 9.38% and 31.44% respectively. Time sensitivity analysis shows that the proposed fusion algorithm also has good transportability.

Key words: engineering of communications and transportation system, urban traffic, data fusion, mind evolutionary algorithm, BP neural network, validation analysis

CLC Number: 

  • U491.1

Fig.1

Structure of MEA?BP fusion algorithm"

Fig.2

Flow chart of MEA?BP fusion algorithm"

Table 1

Primary data obtained from detectors"

检测器类型主要数据
视频检测器视频检测区间ID、区间位置、交通量、平均旅行时间、区间平均车速、天气
微波检测器检测器ID、车道ID、流量、平均瞬时车速、时间占有率、车型分类
浮动车浮动车区间ID、区间位置、浮动车区间平均车速、浮动车平均旅行时间、浮动车交通量

Fig.3

Research scope"

Table 2

Detectors’ spatial information in study area"

ID路段长度/m
视频检测器LD00701西直门桥至阜成门桥1900
微波检测器HI7030a阜城门桥北467 m至阜城门桥298
HI7064a马尾沟路口至阜城门桥北467 m291
HI7053a官圆桥至马尾沟路口417
HI2105a西直门桥至官圆桥904
浮动车1914?339
1913?32
1595?145
1919?689
1586?310
1481?445

Fig.4

Result of data noise reduction"

Fig.5

Principle of data space matching"

Table 3

Network parameter settings"

要素具体内容
输入vm,vf
输出vp
个体总数量100
优胜子种群个数5
临时子种群个数5
子种群个体数量10
子种群内部迭代次数上限20
MEA全局迭代次数上限10

Table 4

Network structure settings"

要 素具体内容
输入神经元2个
输出神经元1个
隐层个数5个共一层

传递函数

输入层和隐层之间采用Sigmoid
隐层和输出层之间采用Purelin
训练函数Trainlm
收敛判别迭代达到1000次或误差小于10?4

Fig.6

Performance comparison of two algorithms"

Fig.7

Convergence comparison of two algorithms"

Table 5

Comparison of index values of the two methods"

算 法MRELSE
BP神经网络算法0.18890.2287
MEA?BP融合算法0.11280.1568
微波检测器数据0.62120.2670
浮动车数据0.28750.3245

Fig.8

Performance comparison of two algorithms"

Table 6

Results of time sensitivity analysis"

日期算法MRELSE
4月22日BP0.13600.1972
MEA?BP0.09270.1280
4月23日BP0.10580.2023
MEA?BP0.10430.1995
4月24日BP0.19610.4576
MEA?BP0.17700.2740
4月25日BP0.15370.3893
MEA?BP0.14340.3055
4月26日BP0.08710.1678
MEA?BP0.07010.1312
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