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浙江大学学报(工学版)  2021, Vol. 55 Issue (6): 1072-1082    DOI: 10.3785/j.issn.1008-973X.2021.06.007
交通工程、土木工程     
基于相关向量机和模糊综合评价的路况预测模型
林浩1,2(),李雷孝1,2,*(),王慧1,2,马志强1,2,万剑雄1,2
1. 内蒙古工业大学 数据科学与应用学院,内蒙古 呼和浩特 010080
2. 内蒙古自治区基于大数据的软件服务工程技术研究中心,内蒙古 呼和浩特 010080
Model based on relevance vector machine and fuzzy comprehensive evaluation for road condition prediction
Hao LIN1,2(),Lei-xiao LI1,2,*(),Hui WANG1,2,Zhi-qiang MA1,2,Jian-xiong WAN1,2
1. College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China
2. Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service, Hohhot 010080, China
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摘要:

为了缓解交通拥堵、道路服务水平低、市民出行效率低等问题,提出基于相关向量机和模糊综合评价的路况预测模型. 利用遗传算法和粒子群算法作为参数寻优算法,优化组合核相关向量机. 基于Spark并行化参数寻优算法,提高模型的训练效率. 提出基于Spark并行化的遗传算法和粒子群算法,优化组合核相关向量机(SPGAPSO-CKRVM). 使用SPGAPSO-CKRVM对车流量和车速进行预测,利用预测结果计算3个交通路况评价参数:平均车速、路段饱和度和交通流密度. 将3个参数输入到模糊综合评价模型中,通过熵值法确定高峰时段和平常时段的各指标权重系数,将路况划分为6个等级. 使用加拿大Whitemud Drive公路的真实数据进行验证,证明了该模型与传统方法相比具有更高的预测精度和扩展性,路况预测准确率达到90.28%.

关键词: 路况预测相关向量机(RVM)组合核函数模糊综合评价Spark    
Abstract:

A model based on relevance vector machine and fuzzy comprehensive evaluation was proposed in order to solve the problems of traffic congestion, low road service level, and low efficiency of citizens’ travel. Genetic algorithm and particle swarm optimization algorithm were used as the parameter optimization algorithm in order to optimize relevance vector machine with combined kernel functions. Then the parameter optimization algorithm was parallelized by Spark to improve the training efficiency of model. Genetic algorithm and particle swarm optimization based on Spark parallelization were proposed, and combined kernel relevance vector machine (SPGAPSO-CKRVM) was optimized. Traffic flow and traffic speed were predicted by SPGAPSO-CKRVM, and the prediction results were used to calculate three traffic condition evaluation parameters: average speed, road occupancy and traffic density. These three parameters were input into fuzzy comprehensive evaluation model. Weight coefficients of these evaluation parameters in peak hours and normal hours were determined by entropy method. Road condition was divided into six levels. The proposed model was verified with the real data of Whitemud Drive in Canada. The experimental results show that the proposed model has better prediction accuracy and scalability than traditional methods. The accuracy of road condition prediction can reach 90.28%.

Key words: road condition prediction    relevance vector machine (RVM)    combined kernel function    fuzzy comprehensive evaluation    Spark
收稿日期: 2020-07-02 出版日期: 2021-07-30
CLC:  TP 181  
基金资助: 内蒙古自治区科技重大专项资助项目(2019ZD015);内蒙古自治区关键技术攻关计划资助项目(2019GG273)
通讯作者: 李雷孝     E-mail: suzukaze_aoba@foxmail.com;llxhappy@126.com
作者简介: 林浩(1995—),男,硕士生,从事大数据、云计算、智能交通的研究. orcid.org/0000-0001-9304-0279. E-mail: suzukaze_aoba@foxmail.com
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引用本文:

林浩,李雷孝,王慧,马志强,万剑雄. 基于相关向量机和模糊综合评价的路况预测模型[J]. 浙江大学学报(工学版), 2021, 55(6): 1072-1082.

Hao LIN,Lei-xiao LI,Hui WANG,Zhi-qiang MA,Jian-xiong WAN. Model based on relevance vector machine and fuzzy comprehensive evaluation for road condition prediction. Journal of ZheJiang University (Engineering Science), 2021, 55(6): 1072-1082.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.06.007        https://www.zjujournals.com/eng/CN/Y2021/V55/I6/1072

图 1  提出的路况预测模型的流程图
图 2  本文使用的隶属函数
图 3  参数寻优算法的结构
图 4  参数寻优算法各部分耗时
图 5  SPGAPSO-CKRVM的算法流程图
数据集编号 道路 方向 监测点编号
数据集1 Whitemud Drive 向东 1027
数据集2 Whitemud Drive 向西 1036
数据集3 Whitemud Drive匝道 向西 1042
表 1  Whitemud Drive公路实验数据信息
参数 参数值
种群大小 $m$ 10
最大迭代次数 $T$ 20
最小适应度 ${\rm{fitnes}}{{\rm{s}}_{{\rm{min}}}}$ 0.000 1
交叉概率 0.6
变异概率 0.2
粒子群学习因子 1.5
粒子速度 [?0.2,0.2]
粒子位置 [?8,8]
表 2  SPGAPSO-CKRVM算法的参数设置
编号 核函数 准确率
数据集1 数据集2 数据集3
1 $\exp\; \left( { - \dfrac{ {\left\| {{{a}}_1 - {{{a}}_2} } \right\|} }{ { {\rm{2} }{\sigma ^{\rm{2} } } } } } \right)$ 0.8084 0.7964 0.7131
2 $\exp\; \left( { - \dfrac{ { { {\left\| {{{a}}_1 - {{{a}}_2} } \right\|}^2} } }{ {2{\sigma ^2} } } } \right)$ 0.8520 0.8355 0.7452
3 ${{a}}_1^{\rm{T}}{{a}}_2$ 0.8529 0.8310 0.7472
4 $\gamma {\left( {{{a} }_1^{\rm{T} }{{a} }_2 + 1} \right)^d} + c$ 0.8514 0.8367 0.7544
5 $\exp\; \left( { - \dfrac{ {\left\| {{{a}}_1 - {{{a}}_2} } \right\|} }{ {2{\sigma ^2} } } } \right)\lambda + \left( { {\rm{1 - } }\lambda } \right)\left[ {\gamma { {\left( {{{a}}_1^{\rm{T}}{{a}}_2 + 1} \right)}^d} + c} \right]$ 0.8529 0.8368 0.7554
6 $\exp \;\left( { - \dfrac{ { { {\left\| {{{a}}_1 - {{{a}}_2} } \right\|}^2} } }{ {2{\sigma ^2} } } } \right)\lambda + \left( { {\rm{1 - } }\lambda } \right)\left[ {\gamma { {\left( {{{a}}_1^{\rm{T}}{{a}}_2 + 1} \right)}^d} + c} \right]$ 0.8503 0.8387 0.7555
表 3  不同核函数的性能对比结果
算法模型 数据集1 数据集2 数据集3
MSE RMSE MAPE MSE RMSE MAPE MSE RMSE MAPE
PSO-SVM 564.06 23.75 0.1624 251.54 15.86 0.1724 48.86 6.99 0.2701
LSTM 493.13 22.21 0.1435 234.37 15.31 0.1689 82.88 9.10 0.2702
GRU 495.68 22.26 0.1432 233.978 15.29 0.1676 83.10 9.12 0.2733
CNN-LSTM 483.18 21.92 0.1438 235.89 15.36 0.1663 82.43 9.08 0.2659
CNN-GRU[26] 487.46 22.07 0.1429 229.62 15.15 0.1648 82.44 9.08 0.2640
Bi-LSTM[27] 481.34 21.94 0.1545 223.13 14.94 0.1811 81.58 9.03 0.2703
GA-CKRVM[28] 433.53 20.82 0.1412 161.56 12.71 0.1616 62.24 7.76 0.2347
CNN-Bi-LSTM 477.35 21.84 0.1396 227.11 15.07 0.1622 81.79 9.04 0.2578
SPGAPSO-CKRVM 392.43 19.81 0.1383 161.1 12.69 0.1589 41.09 6.41 0.2232
表 4  不同算法模型预测车流量的对比
图 6  利用SPGAPSO-CKRVM预测3个路段的车流量结果
图 7  增大测试集后利用SPGAPSO-CKRVM预测3个路段的车流量结果
算法模型 数据集1 数据集2 数据集3
MSE RMSE MAPE MSE RMSE MAPE MSE RMSE MAPE
PSO-SVM 6.0762 2.4650 0.0738 7.2539 2.6933 0.0768 7.1888 2.6812 0.0754
LSTM 5.1940 2.2790 0.0596 5.6122 2.3690 0.0625 5.5984 2.3661 0.0629
GRU 5.1532 2.2701 0.0583 5.6074 2.3681 0.0628 5.6164 2.3699 0.0632
CNN-LSTM 5.1726 2.2740 0.0582 5.3024 2.3027 0.0613 5.5418 2.3541 0.0622
CNN-GRU 5.0687 2.2514 0.0576 5.2964 2.3014 0.0612 5.5469 2.3552 0.0621
Bi-LSTM 5.1557 2.2706 0.0579 5.4228 2.3287 0.0627 5.5691 2.3599 0.0625
GA-CKRVM 4.8118 2.1936 0.0581 5.0895 2.2560 0.0603 5.5145 2.3483 0.0597
CNN-Bi-LSTM 5.0794 2.2538 0.0572 5.1734 2.2745 0.0599 5.5286 2.3513 0.0603
SPGAPSO-CKRVM 4.6656 2.1601 0.0570 4.9809 2.2318 0.0586 5.4214 2.3284 0.0589
表 5  不同算法模型预测车速的对比
图 8  不同情况下的训练时间
图 9  不同情况下的加速比计算结果
图 10  路况预测结果
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