基于循环神经网络的城市轨道交通短时客流预测

doi:10.13229/j.cnki.jdxbgxb20210720

摘要/Abstract

摘要：

为更好地预测城市轨道交通的短时客流情况，提出了基于循环神经网络模型的预测方法。首先，针对轨道交通进出站客流数据，利用Pearson相关系数确定短时客流影响因素；然后，改进K-means聚类算法划分高、中、低客流量三类轨道站点，分析客流时空分布规律及高峰时间段；最后，采用分别基于长短时记忆神经网络（LSTM）与门控循环单元（GRU）的短时客流预测方法，预测不同类型站点在不同时段的客流。实验结果表明：5 min为预测的最佳时间粒度，在此时间粒度下GRU模型整体性能优于LSTM模型。

关键词: 交通运输工程, 城市轨道交通, 短时客流预测, 循环神经网络, 长短时记忆神经网络, 门控循环单元

Abstract:

In order to better predict the short-term passenger flow of urban rail transit， a prediction method based on the recurrent neural network model is proposed. Firstly， based on the actual passenger flow data of each rail transit station， the Pearson correlation coefficient is used to determine the influencing factors of short-term passenger flow of rail transit， such as the weather conditions， historical passenger flow， whether it is a peak time period， whether it is a working day， etc. Secondly， the K-means clustering algorithm is used to classify rail transit stations into three types： high， medium， and low passenger flow stations. Then the distribution of passenger flow for each station type in time and space is analyzed， to determine the peak period of passenger flow for each station type. Finally， two urban rail transit short-term passenger flow prediction methods based on long-short term memory neural network（LSTM） and gated recurrent unit（GRU） respectively are proposed to predict the passenger flow of each type of station in different time period. The experimental results show that 5 min is the best time granularity for short-term passenger flow prediction of the two models. In this time granularity， the overall performance of the GRU model is better than the LSTM model.

Key words: engineering of communication and transportation, urban rail traffic, short-term passenger flow forecast, recurrent neural network, long-short term memory neural network, gated recurrent unit

中图分类号:

U121

张惠臻,高正凯,李建强,王晨曦,潘玉彪,王成,王靖. 基于循环神经网络的城市轨道交通短时客流预测[J]. 吉林大学学报(工学版), 2023, 53(2): 430-438.

Hui-zhen ZHANG,Zheng-kai GAO,Jian-qiang LI,Chen-xi WANG,Yu-biao PAN,Cheng WANG,Jing WANG. Short⁃term passenger flow forecasting of urban rail transit based on recurrent neural network[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 430-438.

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相关影响因素	Pearson相关系数	显著性（双尾）
［-45，-30）min客流量	0.880^*	0.000
［-30，-15）min客流量	0.930^*	0.000
［-15，0）min客流量	0.960^*	0.000
温度	0.304^*	0.000
湿度	-0.176^*	0.000
能见度	0.053^*	0.004
降水量	-0.093^*	0.000
云量	0.420^*	0.000

变量	说明
x₁	温度/°C
x₂	湿度/%
x₃	能见度/km
x₄	降水量/mm
x₆	前一时段客流量/人次
x₇	是否工作日（1表示非工作日、0表示工作日）
x₈	是否高峰时段（1表示高峰时段、 0表示平峰时段、-1表示停运时段）

时间粒度 /min	LSTM/GRU模型训练时间/s
	时间步长1		时间步长3		时间步长6
	单轮时间	总时间	单轮时间	总时间	单轮时间	总时间
5	0.8/0.7	629/ 583	2.6/2.3	2047/ 1842	6.2/5.7	4934/ 4603
10	0.4/0.3	323/ 283	1.0/0.9	834/ 758	2.7/2.6	2173/ 2085
15	0.3/0.2	259/ 228	0.5/0.5	435/ 393	1.6/1.6	1293/ 1278

站点	模型	时间粒度 /min	时间步长
			1		3		6
			RMSE	MAE	RMSE	MAE	RMSE	MAE
镇海路	LSTM	5	38.2	24.8	35.2	23.6	34.7	23.0
		10	56.3	38.6	62.4	43.9	64.4	44.8
		15	79.6	56.5	74.2	51.9	81.9	58.3
	GRU	5	38.5	25.8	34.2	23.2	33.8	22.6
		10	56.2	40.3	54.4	38.5	51.2	36.1
		15	78.3	55.3	75.3	53.5	76.5	55.1
乌石浦	LSTM	5	24.4	16.4	23.5	16.0	23.4	15.9
		10	41.6	28.3	41.0	28.3	39.5	27.5
		15	58.8	40.3	57.3	40.1	28.1	41.9
	GRU	5	24.4	16.5	22.9	15.6	22.3	15.1
		10	40.7	28.2	39.9	27.0	38.5	26.3
		15	60.7	42.4	54.7	36.8	55.2	37.9
吕厝	LSTM	5	19.1	13.2	17.5	12.3	19.7	13.2
		10	26.9	19.0	29.3	20.0	31.1	21.1
		15	40.0	27.5	38.9	27.9	38.1	26.3
	GRU	5	19.9	14.2	17.4	12.2	17.1	11.7
		10	28.0	19.7	28.2	19.8	31.0	22.0
		15	43.6	31.9	39.3	28.3	39.3	29.0
集美学村	LSTM	5	18.6	12.5	15.7	10.9	16.5	11.8
		10	23.8	16.1	23.6	15.9	29.4	19.7
		15	34.1	22.5	34.0	23	36.2	24.3
	GRU	5	18.4	12.4	16.4	11.6	15.6	10.5
		10	23.4	15.7	24.0	15.9	22.1	14.7
		15	34.0	22.9	34.3	23.8	32.7	21.7
官任	LSTM	5	11.9	7.0	11.3	7.3	12.7	8.8
		10	19.6	12.6	19.9	13.4	18.1	12.1
		15	28.8	18.5	28.1	18.6	24.5	15.2
	GRU	5	12.2	8.1	11.2	7.2	11.7	7.9
		10	20.0	13.2	19.1	12.4	18.0	12.1
		15	30.1	20.7	27.2	18.2	24.4	15.5
集美大道	LSTM	5	7.5	5	6.6	4.4	6.6	4.5
		10	11.1	7.1	10.6	6.8	10.7	7
		15	15.8	10.4	15.4	10.3	14.1	9.1
	GRU	5	7.5	5.0	6.6	4.3	6.8	4.6
		10	11.6	7.8	10.6	6.9	10.5	7.2
		15	15.6	10.4	16.0	10.9	14.6	9.5

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