Random Coefficient Modeling Research on Short-Term Forecast of Passenger Flow Into an Urban Rail Transit Station

Feng, Xia‐Ting; Zhang, Hemeizi; Gan, Tiantian; Sun, Qipeng; Ma, Fei; Sun, Xun

doi:10.3846/16484142.2016.1128484

Cited by 13 publications

(4 citation statements)

References 24 publications

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“…Jing and Yin [4] proposed a method for tracking and predicting sudden changes in the passenger flow caused by emergencies. Feng et al [18] developed a random coefficient model to predict the short-term passenger flow of urban rail transit that can cope with sudden increases in inflow. Wang et al [19,20] developed a framework to detect and predict burst passenger flow, which can send warning signals ahead of time of the burst passenger inflow.…”

Section: Studies On Passenger Flow Fluctuationsmentioning

confidence: 99%

“…Although some studies have recognized that abnormal passenger flow fluctuations and mutational passenger flow exceeding the threshold have a significant impact on the safe operation of urban rail transit, few studies have evaluated the degree of the impact of mutational passenger flow [17,18,20]. Although there are many indicators to describe the fluctuation of passenger flow, they all describe the space-time distribution feature of passenger flow and lack a description of the mutation degree of passenger flow.…”

Section: Indicators Of Catastrophe Passenger Flowmentioning

confidence: 99%

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Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit

Chen,

Ma,

et al. 2023

Sustainability

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When urban rail transit is affected by interference, the fluctuation pattern of passenger flow undergoes mutation, which is not conducive to its operational safety and sustainable development. The more intense the mutation in the passenger flow, the greater the impact on the network and operations. Therefore, it is necessary to measure and evaluate the mutation degree of the urban rail transit passenger flow. In this study, we clarify the definition of the mutation degree of urban rail transit passenger flow and construct an evaluation index system for the mutation degree of passenger flow from two dimensions: horizontal mutation amplitude and vertical mutation amplitude. Based on the catastrophe theory, an evaluation model of the mutation degree was constructed. Using this evaluation method, abbreviated as CDCT, the level division of the mutation degree at different time intervals under different interference scenarios can be obtained, achieving a dynamic evaluation of the mutation degree of passenger flow. Finally, taking the passenger flow data of the Suzhou rail transit as an example, the mutational fluctuation of passenger flow affected by interference is analyzed, and the evaluation results of the mutation degree of passenger flow are obtained. The analysis results show that the CDCT evaluation method can better reflect the dynamic changes in the mutation degree throughout the process under the influence of the mutational passenger flow.

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Section: Studies On Passenger Flow Fluctuationsmentioning

confidence: 99%

Section: Indicators Of Catastrophe Passenger Flowmentioning

confidence: 99%

Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit

Chen,

Ma,

et al. 2023

Sustainability

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“…With the comprehensive development of urban rail transit, the passenger flow of rail transit continues to increase. Especially during the rush hours on working days, the passenger density is too high, which easily causes safety hazards such as crowding and trampling [1] . The heat map of passenger flow distribution in subway stations can intuitively display the passenger flow distribution and congestion level in subway stations.…”

Section: Introductionmentioning

confidence: 99%

Research on heat map generation of passenger flow distribution in subway station based on binocular vision

Jiang,

Xiao,

Wang

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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Monitoring passenger flow distribution in real-time in subway stations plays a key role in large passenger flow control and intelligent passenger flow management. A binocular vision based method for generating heat map of passenger flow distribution in subway stations is proposed in this work, based on the extraction and analysis of the monitoring video images in subway stations. Firstly, the binocular camera is used to collect the depth information of the passenger flow in the subway station. Secondly, a Yolov5-based pedestrian detection algorithm is applied to obtain the passenger flow information and coordinate positions from the image data. Then after coordinate conversion, the actual position information of passengers is obtained. Finally, based on passenger location coordinates, the heat map of passenger flow density can be generated to monitor passenger flow in real-time. The experimental results show that the average accuracy of passenger detection is 95%, which is 2.1% higher than before the improvement. ing the passenger's coordinates in the camera coordinate system to the actual coordinates, the resulting error at 5m, 10m, and 15m is 0.4m, 0.9m, and 1.3m, respectively. and the presentation of the heatmap is consistent with the actual situation. The research results are applicable to real-time passenger flow monitoring and statistics in subway systems, enabling operators to have a real-time understanding of passenger flow distribution and timely guidance, and have certain application prospects.

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“…Hierarchical Bayesian methods were applied iteratively to short-term traffic flow predictions, and the estimates in each iteration of calibration were improved by sequential Bayesian updates. It has been demonstrated in previous studies that the estimator exhibits efficient convergence to rational results with a satisfactory level of accuracy [21]. Xie Xiaoru et al employed a combined prediction method, namely the gray model-linear regression, to forecast the passenger volume of Wuchang Railway Station over a one-year period.…”

Section: Introductionmentioning

confidence: 99%

Study on Dynamic Load of Air-Conditioning System in Subway Station Based on Hourly Passenger Flow

Wang,

Li,

Deng

et al. 2023

Buildings

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The research focuses on the air-conditioning system in a public area of a subway station. To address this, an optimization model based on the grid time segmentation method was constructed, specifically a GM (1,1) model. We explored the influence of the hourly passenger flow fluctuation on the load of the subway air-conditioning system, obtained the dynamic change law of the air conditioning system load in the subway station, and then dynamically adjusted the air conditioning system according to the dynamic change law to reduce the operation energy consumption of the system. Through the analysis of the simulation results, the model predicted that compared with the actual passenger flow data, the average maximum relative error was 14.97%. On this basis, the change law of the dynamic load of the subway air-conditioning system which caused by the change in passenger flow from time to time could be calculated and analyzed. Compared with the calculated load of the air conditioning system, the working day load was decreased by 1469.77 kW, or 22.00%. The findings indicate that in response to the dynamic load of fluctuations, timely adjustment of the air supply parameter of the air-conditioning system offers a significant reference point for optimizing energy efficiency in subway stations.

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Random Coefficient Modeling Research on Short-Term Forecast of Passenger Flow Into an Urban Rail Transit Station

Cited by 13 publications

References 24 publications

Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit

Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit

Research on heat map generation of passenger flow distribution in subway station based on binocular vision

Study on Dynamic Load of Air-Conditioning System in Subway Station Based on Hourly Passenger Flow

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