摘要
为提高地铁客流预测的准确性,以西安地铁1号线为例,分析了地铁客流的耦合时空特征,提取了影响地铁客流变化的5个主要因素,包括节日、非节日、时间段、站点和天气,构建了反向传播(BP)神经网络,预测了地铁客流;利用引入自适应变异与均衡惯性权重的粒子群优化(PSO)算法,优化了BP神经网络,形成了考虑复杂因素影响的地铁客流预测系统;选取了换乘站、非换乘站的首站与中间站,引入天气、节日、非节日因素,对比了不同时间段下的BP神经网络模型,优化了PSO-BP神经网络模型的预测误差。研究结果表明:考虑天气、节日、非节日因素,换乘站点分时段优化PSO-BP神经网络模型预测的平均绝对误差、均方根误差和平均绝对百分比误差,较不分时段的优化PSO-BP神经网络模型分别平均下降了40.13%、31.46%和23.89%,较分时段的BP神经网络模型分别平均下降了17.50%、17.86%和17.32%;非换乘站点分时段优化PSO-BP神经网络模型预测的平均绝对误差、均方根误差和平均绝对百分比误差,较不分时段的优化PSO-BP神经网络模型分别平均下降了16.50%、20.99%和32.59%,较分时段的BP神经网络模型分别平均下降了11.48%、12.10%和17.73%;各站点分时段优化PSO-BP神经网络模型预测的平均绝对误差、均方根误差、平均绝对百分比误差,较不分时段的优化PSO-BP神经网络模型分别平均下降了24.37%、24.48%和29.69%,较分时段的BP神经网络模型分别平均下降了13.49%、14.02%和17.59%,因此,利用考虑多影响因素的优化PSO-BP神经网络模型能提高地铁客流预测的准确性。
To improve the accuracy of subway passenger flow prediction,by considering the Xi’an Metro Line 1 as an example,five main factors affecting subway passenger flow variations,such as festival,non-festival,time period,station,and weather,were extracted to analyze the coupled spatial-temporal characteristics of subway passenger flow.A back propagation(BP)neural network was constructed to predict the subway passenger flow.The proposed BP neural network was further optimized by using a particle swarm optimization(PSO)algorithm that introduced adaptive mutation and balanced inertia weights to form a subway passenger flow prediction system that could consider complex influence factors.Transfer stations and non-transfer stations including a first and an intermediate station were selected,the weather,festival,and non-festival factors were considered,and the BP neural network models for different time periods were compared.Then,the prediction errors of the PSO-BP neural network model were optimized.Research results show that by considering the weather,festival and non-festival factors,the mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)of the optimized PSO-BP neural network model predictions at transfer stations within the optimized time periods decrease by 40.13%,31.46%and 23.89%,respectively,compared with the optimized PSO-BP neural network models prediction errors without the time periods,decrease by 17.50%,17.86%and 17.32%compared with the BP neural network models prediction errors within the optimized time periods.The MAE,RMSE,and MAPE of the optimized PSO-BP neural network model predictions in the non-transfer stations within the optimized time periods decrease by 16.50%,20.99%and 32.59%,respectively,compared with the optimized PSO-BP neural network model prediction errors without time periods,and decrease by 11.48%,12.10%and 17.73%,respectively,compared with the BP neural network model prediction errors within the optimized time periods.The MAE,RMSE,and MAPE of the optimized PSO-BP neural network model predictions at each station within the optimized time periods decrease by 24.37%,24.48%and 29.69%,respectively,compared with the optimized PSO-BP neural network model prediction errors without time periods,and decrease by 13.49%,14.02%and 17.59%,respectively,compared with the BP neural network model prediction errors within the given time periods.Therefore,using the optimized PSO-BP neural network model and considering the influencing factors can improve the accuracy of subway passenger flow prediction.8 tabs,12 figs,30 refs.
作者
惠阳
王永岗
彭辉
侯淑倩
余强(指导)
HUI Yang;WANG Yong-gang;PENG Hui;HOU Shu-qian(Key Laboratory of Transport Industry of Management,Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area,Chang’an University,Xi'an 710064,Shaanxi,China;Transportation Soft Science Research Center,Chang’an University,Xi’an 710064,Shaanxi,China;Xi’an Rail Transit Group Company Limited,Xi’an 710018,Shaanxi,China)
出处
《交通运输工程学报》
EI
CSCD
北大核心
2021年第4期210-222,共13页
Journal of Traffic and Transportation Engineering
基金
国家自然科学基金项目(52072044)
陕西省自然科学基金项目(2021JQ-295)。
关键词
城市轨道交通
客流预测
耦合时空特征
反向传播神经网络
粒子群优化算法
自适应变异
惯性权重
urban rail transit
passenger flow prediction
coupled spatial-temporal characteristic
back propagation neural network
particle swarm optimization algorithm
adaptive mutation
inertia weight
