Optimization Scheme of Large Passenger Flow in Huoying Station, Line 13 of Beijing Subway System

Zhou, Jin; Wang, Haochen; Sun, Di; Xu, Siqiang; Lv, Meng; Yu, Feifei

doi:10.32604/cmc.2020.09865

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…Relying on many advantages such as high reliability, large capacity, safety and comfort, urban rail transit has taken up a considerable share in long-distance commuting, contributing greatly to optimizing urban travel structure, alleviating congestion and building a low-carbon, environmentally friendly green travel mode [1][2][3] . However, as the level of urbanization and residents' travel demand continue to rise, the intensity of passenger flow carried by trains continues to rise, making the daily operation and management face increasing difficulties [4][5][6] , such as the mismatch of train capacity, the evacuation of passenger flow under unexpected circumstances, and the safety hazards arising from passenger congestion and detention. The cross-section passenger flow of trains is the amount of passengers carried by a train at a given moment, is collected in real time by the on-board monitoring cameras and is one of the important indicators of the passenger density of the network, reflecting a series of information such as train congestion, passenger comfort and dispatching level.…”

Section: Introductionmentioning

confidence: 99%

Short-time prediction model for cross-section passenger flow of trains in urban transit using GCN-AM-BiLSTM

Zuo

Zhao

Liu

et al. 2023

Preprint

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With the proliferation of passenger flow under the condition of network condition, the imbalanced temporal and spatial distribution of passenger flow occurs frequently, which brings enormous challenges to the operation of urban rail systems. Effectively predicting the short-time passenger flow of trains is an important prerequisite to optimize the transportation strategies, respond to the fluctuation of passenger flow and meet the real-time demand. Consequently, the GCN-AM-BiLSTM prediction model is proposed to extract the complex temporal and spatial characteristics of passenger flow. Firstly, the urban rail transit temporal diagram and spatial adjacency matrix are constructed to capture the global spatial characteristics using GCN. Secondly, the attention mechanism is introduced into the BiLSTM to construct the AM-BiLSTM module to extract and assign the importance of temporal characteristics from both the forward and backward dimensions. Finally, the characteristics are integrated based on the fusion network. The performance verification and analysis based on Chengdu Metro in China show that compared with several baseline models, our model achieves the best values in terms of MAE, RMSE and MAPE. The prediction efficiency can fully meet the timeliness requirements of the field, which has good application prospects.

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Section: Introductionmentioning

confidence: 99%

Short-time prediction model for cross-section passenger flow of trains in urban transit using GCN-AM-BiLSTM

Zuo

Zhao

Liu

et al. 2023

Preprint

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“…Yin et al [11] proposed a simulation-based model to optimize the release time, place and content of passenger flow guidance information in rail transit network, which can significantly save the travel time of passengers. Taking Line 13 of Beijing subway as an instance, Zhou et al [12] rapidly distributed the passenger flow in Huoying Station by adjusting the operation time of the escalator in the direction of Xizhimen Station under the Application of Anylogic software to alleviate congestion. Considering effects of congestion propagation among facilities at busy stations, Liu et al [13] created a discrete event simulation optimization model based on queuing theory and used a multi-channel queuing system to minimize operating costs and passenger time delays…”

Section: Introductionmentioning

confidence: 99%

Simulation and optimization of inbound passenger flow line in large-scale railway station

2023

3rd International Conference on Internet of Things and Smart City (IoTSC 2023）

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Confronted with fast-paced technological progress and surging travel needs, some large-scale railway passenger stations now have fallen short of passengers' demands for comfortable trips due to their complicated passenger inbound process, circuitous inbound routes, and serious passenger traffic congestion. Taking Zhengzhou East Railway Station, a large-scale passenger transport hub in China, as the case study, this paper first elaborates on the construction of the simulation model based on Anylogic for reproducing inbound passenger flow lines thereof. Next, problems existing in the current passenger flow organization are analyzed using the average inbound time of passengers from each entrance and the average queue length for each kind of passenger service equipment as evaluation indexes. And then the optimized joint measures for different operational issues including four universal flow line adjustment methods are proposed, namely, reducing flow line density, increasing flow velocity, eliminating flow line bottleneck and relieving flow line crossing. Flow line simulation experiments herein have verified the rationality and effectiveness of the foregoing optimization measures for flow line organization of large-scale railway passenger stations.

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“…With the development of science and technology, many advanced technologies are being applied to smart cities, such as blockchain technology, Internet of ings technology, machine learning technology, and big data technology [2,3]. Machine learning technologies, including the neural network model, have been widely used in many fields, such as public transportation, taxi demand prediction [4], bicycle station planning [5], and subway passenger flow optimization [6,7]. In the medical field, a neural network is applied to fetal ultrasonic standard plane recognition [8].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling and Optimal Control of Underground Ventilation and Air Conditioning in Station

Zheng

Li²,

An³

et al. 2022

Mathematical Problems in Engineering

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The underground ventilation and air-conditioning system of the station accounts for about 40% of the total energy consumption of the subway. One of the important reasons is the cooling load of the station. The related control process has typical nonlinear, time-varying, and large inertia characteristics, and the traditional PID control algorithm is difficult to achieve the ideal control effect. Therefore, it is of great significance to study the application of the intelligent algorithm in subway ventilation and air-conditioning control systems to improve the control effect. Based on the analysis of the coupling characteristics of temperature and humidity control in subway air-conditioning systems, this paper designs the feedforward decoupling link according to the identification model and analyzes the control curves with and without the decoupling link when using conventional PID control, which shows the effectiveness of the method proposed in this paper.

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Optimization Scheme of Large Passenger Flow in Huoying Station, Line 13 of Beijing Subway System

Cited by 6 publications

References 3 publications

Short-time prediction model for cross-section passenger flow of trains in urban transit using GCN-AM-BiLSTM

Short-time prediction model for cross-section passenger flow of trains in urban transit using GCN-AM-BiLSTM

Simulation and optimization of inbound passenger flow line in large-scale railway station

Mathematical Modeling and Optimal Control of Underground Ventilation and Air Conditioning in Station

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