Integrating Spectral Clustering and Hybrid CNN-LSTM-PSO Model for Short-Term Passenger Flow Prediction in Urban Rail Transit

IF 2.5 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Tingjing Wang, Kun Peng, Daiquan Xiao, Xuecai Xu, Yiran Guo
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引用次数: 0

Abstract

With the rapid development of model cities, urban rail transit (URT) systems have emerged as a crucial component in urban public transport, and passenger flow prediction serves as the cornerstone for planning the travel, avoiding the congestion, and improving the travel efficiency. In order to predict short-term passenger flow of the URT system, a hybrid convolutional neural network (CNN)-long short-term memory (LSTM)-particle swarm optimisation (PSO) model is proposed to accommodate both the spatial and temporal features of passenger flow. First, spectral clustering is employed to extract four different types of stations, in which the Calinski–Harabasz (CH) index is considered. Second, the hybrid CNN-LSTM-PSO model is constructed to predict the short-term passenger flow for different types of stations, in which CNN can extract the abstract feature with a multi-layer convolutional structure, LSTM can deal with time series data, and the PSO algorithm is employed to optimise some parameters. Third, the data from Hangzhou urban rail transit in 2019 are employed for prediction. The results show that the proposed hybrid model reveals the best performance in accuracy by comparing the equivalent CNN-LSTM, LSTM and autoregressive integrated moving average (ARIMA) models. At last, some empirical suggestions are provided to benefit both the passengers and operation and management departments of the URT system.

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基于谱聚类和CNN-LSTM-PSO混合模型的城市轨道交通短期客流预测
随着示范城市的快速发展,城市轨道交通系统已成为城市公共交通的重要组成部分,而客流预测是规划出行、避免拥堵、提高出行效率的基石。为了预测城市轨道交通系统的短期客流,提出了一种混合卷积神经网络(CNN)长短时记忆(LSTM)-粒子群优化(PSO)模型,以适应客流的时空特征。首先,考虑Calinski-Harabasz (CH)指数,采用光谱聚类方法提取4种不同类型的站点;其次,构建CNN-LSTM-PSO混合模型,对不同类型车站的短期客流进行预测,其中CNN利用多层卷积结构提取抽象特征,LSTM处理时间序列数据,并利用PSO算法对部分参数进行优化。第三,采用2019年杭州城市轨道交通数据进行预测。对比等效的CNN-LSTM、LSTM和自回归综合移动平均(ARIMA)模型,结果表明所提出的混合模型在精度上表现最好。最后,提出了有利于乘客和轨道交通系统运营管理部门的经验建议。
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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
期刊介绍: IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
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