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

Abstract

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

CLC Number:

U121

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.

Figures/Tables 9

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References 21

1	中国城市轨道交通协会. 城市轨道交通2020年度统计和分析报告[J]. 隧道建设(中英文), 2021, 41(4): 691.
	China Association of Metros. Statistics and analysis Report of urban rail transit in 2020[J]. Tunnel Construction(English and Chinese), 2021, 41(4): 691.
2	Schimbinschi F, Moreira-Matias L, Nguyen V X, et al. Topology-regularized universal vector autoregression for traffic forecasting in large urban areas[J]. Expert Systems with Applications, 2017, 82: 301-316.
3	Guo J H, Huang W, Williams B M. Adaptive kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification[J]. Transportation Research Part C, 2014, 43(2): 50-64.
4	张淑玉. 基于贝叶斯理论的铁路短期客流预测方法研究[D]. 北京: 北京交通大学交通运输学院, 2018.
	Zhang Shu-yu. Research on forecasting method of short term passenger flow based on Bayesian theory[D]. Beijing: School of Traffic and Transportation, Beijing Jiaotong University, 2018.
5	孟品超,李学源,贾洪飞,等.基于滑动平均法的轨道交通短时客流实时预测[J]. 吉林大学学报:工学版, 2018, 48(2): 448-453.
	Meng Pin-chao, Li Xue-yuan, Jia Hong-fei, et al. Short-time rail transit passenger flow real-time prediction based on moving average[J]. Journal of Jilin University(Engineering ang Technology Edition), 2018, 48(2): 448-453.
6	Zhang Y R, Zhang Y L, Haghani A. A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model[J]. Transportation Research Part C: Emerging Technologies, 2014, 43: 65-78.
7	刁文君. 基于灰色模型的城市轨道交通客流预测研究[D]. 天津: 天津大学管理与经济学部, 2016.
	Diao Wen-jun. Study on the forecast of urban rail transit passenger flow based on Grey Model[D]. Tianjin: College of Management and Economy, Tianjin University, 2016.
8	杨兆升,王媛,管青.基于支持向量机方法的短时交通流量预测方法[J]. 吉林大学学报:工学版, 2006, 36(6): 881-884.
	Yang Zhao-sheng, Wang Yuan, Guan Qing. Short-term traffic flow prediction method based on SVM[J]. Journal of Jilin University(Engineering ang Technology Edition), 2006, 36(6): 881-884.
9	姚智胜,邵春福,熊志华,等.基于主成分分析和支持向量机的道路网短时交通流量预测[J]. 吉林大学学报:工学版, 2008, 38(1): 48-52.
	Yao Zhi-sheng, Shao Chun-fu, Xiong Zhi-hua,et al. Short-term traffic volumes forecasting of road network based on principal component analysis and support vector machine[J]. Journal of Jilin University (Engineering ang Technology Edition), 2008, 38(1): 48-52.
10	陈健冲. 基于改进卡尔曼滤波算法的短时公交客流预测模型研究[D]. 大连: 大连海事大学信息科学技术学院, 2020.
	Chen Jian-chong. Research on short-term bus passeng based on improved Kalman flow prediction model filter algorithm[D]. Dalian: College of Information Science and Technology, Dalian Maritime University, 2020.
11	Bai Y, Sun Z Z, Zeng B, et al. A multi-pattern deep fusion model for short-term bus passenger flow forecasting[J]. Applied Soft Computing, 2017, 58: 669-680.
12	Glisovic N, Milenkovic M, Bojovic N, et al. A hybrid model for forecasting the volume of passenger flows on Serbian railways[J]. Operational Research, 2016, 16(2): 271-285.
13	田壮壮. 城市轨道交通延伸线的进出站客流预测[D]. 南昌: 华东交通大学电气与自动化工程学院, 2019.
	Tian Zhuang-zhuang. Forecasting passenger flow of inbound and outbound for urban rail transit extension line[D].Nanchang: School of Electrical and Automation Engineering, East China Jiantong University,2019.
14	王秋雯, 陈彦如, 刘媛春. 基于卷积长短时记忆神经网络的城市轨道交通短时客流预测[J/OL]. [2020-09-02]. / 10.13195/j.kzyjc.2020.0501 doi: 10.13195/j.kzyjc.2020.0501
15	赵阳阳, 夏亮, 江欣国. 基于经验模态分解与长短时记忆神经网络的短时地铁客流预测模型[J]. 交通运输工程学报, 2020, 20(4): 194-204.
	Zhao Yang-yang, Xia Liang, Jiang Xin-guo. Short-term metro passenger flow prediction based on EMD-LSTM[J]. Journal of Traffic and Transportation Engineering, 2020, 20(4): 194-204.
16	Tsai T H, Lee C K, Wei C H.Neural network based temporal feature models for short-term railway passenger demand forecasting[J]. Expert Systems with Applications, 2009, 36(2): 3728-3736.
17	Pearson K. Notes on the history of correlation[J]. Biometrika, 1920, 13(1): 25-45.
18	徐威,郑长江,马庚华,等.基于k-means聚类的城市轨道交通站点分类研究[J].贵州大学学报:自然科学版, 2018, 35(6): 106-111.
	Xu Wei, Zheng Chang-jiang, Ma Geng-hua, et al. Classification of urban rail transit stations based on k-means clustering[J]. Journal of Guizhou University (Natural Sciences), 2018, 35(6): 106-111.
19	Cho K, Merrienboer B V, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C] ∥EMNLP, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, 2014: 1724-1734.
20	李梅. 基于深度学习的短时公交客流预测研究[D]. 北京: 北京交通大学经济管理学院, 2019.
	Li Mei. Research on short-time bus passenger flows forecasting based on deep learning[D]. Beijing: School of Economics and Management, Beijing Jiaotong University, 2019.
21	王挺. 城市轨道交通短时客流预测时间粒度选择[D]. 北京: 北京交通大学土木建筑工程学院, 2018.
	Wang Ting. Selecting time granularity of short-term passenger flow forecast in urban rail transit[D]. Beijing: School of Civil Engineering, Beijing Jiaotong University, 2018.

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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

Short⁃term passenger flow forecasting of urban rail transit based on recurrent neural network

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