| 基于时空GPSO-SVM的短时交通流预测 |
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| 引用本文： | 梅朵, 郑黎黎, 冷强奎, 鄂旭. 基于时空GPSO-SVM的短时交通流预测[J]. 交通信息与安全, 2017, 35(2): 68-74,120. doi: 10.3963/j.issn.1674-4861.2017.02.010 |
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| 作者姓名： | 梅朵 郑黎黎 冷强奎 鄂旭 |
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| 作者单位： | 渤海大学信息科学与技术学院 辽宁 锦州 121013;吉林大学交通学院 长春 130022;渤海大学信息科学与技术学院 辽宁 锦州 121013;渤海大学信息科学与技术学院 辽宁 锦州 121013 |
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| 基金项目： | 国家青年科学基金项目辽宁省自然科学基金项目 |
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| 摘 要： | 为了提高城市道路短时交通流预测的精度,提出了一种基于时空遗传粒子群支持向量机的短时交通流预测模型.通过主成分分析法对路网原始交通流量进行时空相关性分析,用较少的主成分代替原始交通流量并作为预测因子,在粒子群算法中引入遗传算法的交叉和变异因子,避免粒子群算法陷入局部最优.利用改进后的粒子群算法优化支持向量机参数,得到最优的支持向量机模型,并实现城市道路的短时交通流预测.以长春市路网的实测数据为基础进行了实例验证,结果表明,优化支持向量机参数时,遗传粒子群算法不会陷入局部最优,优化效果更好;与粒子群支持向量机模型和遗传粒子群支持向量机模型相比,所提出预测模型的相对误差波动较稳定,平均预测精度分别提高了4.96%和3.41%.
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| 关 键 词： | 城市交通 短时交通流预测 路网时空相关性 主成分分析 遗传算法 粒子群算法 支持向量机 |
A Prediction Model for Short-term Traffic Flow Based on Space-time GPSO-SVM |
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| MEI Duo, ZHENG Lili, LENG Qiangkui, E Xu. A Prediction Model for Short-term Traffic Flow Based on Space-time GPSO-SVM[J]. Journal of Transport Information and Safety, 2017, 35(2): 68-74,120. doi: 10.3963/j.issn.1674-4861.2017.02.010 |
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| Authors: | MEI Duo ZHENG Lili LENG Qiangkui E Xu |
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| Abstract: | In order to improve accuracy of short-term forecasting of traffic flow on urban roads, a model is proposed by space-time genetic-particle swarm optimization (GPSO) and support vector machine (SVM).The spatial-temporal correlation of original traffic flow of a road network is analyzed based on a principal component analysis.Instead of original traffic flow, this paper takes less principal components as predictive factors.The crossover and mutation factors of a genetic algorithm are appiled into a particle swarm optimization algorithm, which can avoid local optimization.According to the improved particle swarm optimization algorithm, parameters of SVM model are optimized, then an optimal SVM model is developed, as well as forecasting of short-term traffic flow.A practical case study is taken based on the data of a road network in Changchun City.The results show that the GPSO algorithm does not fall into local optimum when the parameters of SVM model are optimized, and the effects of optimization is better;the relative error of this proposed model is stable compared with the particle swarm SVM model and GPSO-SVM model, and the average prediction accuracy is improved by 4.96% and 3.41%, respectively. |
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| Keywords: | urban traffic forecasting of short-term traffic flow spatial-temporal correlation of road network principal component analysis genetic algorithm particle swarm optimization algorithm support vector machine |
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