引用本文:龙小强,李捷,陈彦如.基于深度学习的城市轨道交通短时客流量预测[J].控制与决策,2019,34(8):1589-1600
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基于深度学习的城市轨道交通短时客流量预测
龙小强1, 李捷2, 陈彦如3
(1. 广州市交通运输研究所与广州市公共交通研究中心,广州510627;2. 北京东方科技集团股份有限公司CIO组织数字化应用中心,北京100016;3. 西南交通大学经济管理学院,成都610031)
摘要:
我国城市轨道交通已进入快速发展期,准确预测城轨交通短时客流量,对于城轨运营安全、运营效率及运营成本具有重要意义.城轨交通短时客流量由于具有强随机性、周期性、相关性及非线性的特征,浅层模型的预测精度并不理想.对此,基于深度信念网络(DBN)和支持向量回归机(SVM),提出城轨交通短时客流深层预测模型(DBN-P/GSVM),同时基于遗传算法(GA)和粒子群算法(PSO)实现SVM的参数寻优.最后,对成都地铁火车北站客流量预测进行实例分析.结果表明,DBN-P/GSVM深度预测模型在均方误差、均方根误差、绝对误差均值及绝对百分比误差均值等方面均优于浅层模型——GA-SVM模型、PSO-SVM模型和BP神经网络模型,以及深层模型长短期记忆网络(LSTM)与LSTM-Softmax.
关键词:  城轨交通短时客流量  深度信念网络  支持向量机  遗传算法  粒子群算法  长短期记忆网络
DOI:10.13195/j.kzyjc.2018.1393
分类号:TP273
基金项目:国家自然科学基金项目(51578465, 71771190).
Metro short-term traffic flow prediction with deep learning
LONG Xiao-qiang1,LI Jie2,CHEN Yan-ru3
(1. Guangzhou Transport Research Institute & Guangzhou Public Transport Research Center,Guangzhou510627,China;2. CIO-Digital Application Center,BOE Technology Group Co.,Ltd,Beijing100016,China;3. School of Economics and Management,Southwest Jiaotong University,Chengdu610031,China)
Abstract:
At present, China's urban rail transit is developing rapidly. Short-term traffic flow prediction plays an important role on the metro safety,efficiency and cost. Many existing approaches with shallow architecture failed to provide favorable results, because short-term traffic flow are highly random, cyclical, correlative and non-linear. Therefore, we propose a prediction model with deep architecture---DBN-P/GSVM based on the deep believe network(DBN) and support vector machine(SVM). The parameters of the SVM are obtained based on the genetic algorithm(GA) and the particle swarm optimization(PSO). Abundant experiments are conducted on the Chengdu Metro North Railway Station. The results show that the proposed DBN-P/GSVM model performs better than such shallow architecture models as the GA-SVM, PSO-SVM and back propagation neural network(BPNN) and such deep architecture models as the long short-term memory(LSTM) and LSTM-Softmax in terms of mean squared error, root mean square error, mean absolute error and mean absolute percentage error.
Key words:  metro short-term traffic flow  deep belief network  support vector machine  genetic algorithm  particle swarm optimization  long short-term memory

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