scholarly journals Traffic simulation of queueing systems in the ANYLOGIC application with an example of passenger flow of the subway station

2018 ◽  
Vol 2018 (3) ◽  
Author(s):  
V. M. Antonova ◽  
◽  
N. A. Grechishkina ◽  
N. A. Kuznetsov ◽  
N. A. Sukhorukova ◽  
...  
Keyword(s):  
Traffic Simulation ◽  
Queueing Systems ◽  
Subway Station ◽  
Passenger Flow

scholarly journals Experimental Investigation of Air Quality in a Subway Station with Fully Enclosed Platform Screen Doors

2020 ◽  
Vol 17 (14) ◽  
pp. 5213
Author(s):  
Liping Pang ◽  
Chenyuan Yang ◽  
Xiaodong Cao ◽  
Qing Tian ◽  
Bo Li
Keyword(s):  
Air Quality ◽  
Indoor Air ◽  
Air Pollutants ◽  
Subway Station ◽  
Passenger Flow ◽  
Volatile Organic ◽  
Maximum Allowable Concentrations ◽  
Indoor Air Pollutants ◽  
Beijing China ◽  
Concentration Limits

In this study, the indoor air quality (IAQ) was investigated in a subway station with fully enclosed platform screen doors in Beijing, China. Eight indoor air pollutants, including PM2.5, PM10, SO2 (sulfur dioxide), NO2 (nitrogen dioxide), NH3 (ammonia), CO (carbon monoxide), CH2O (formaldehyde) and TVOC (total volatile organic compound), were measured for six consecutive days in October 2019. The results indicated that the IAQ in the subway station was basically stable at good levels for most times during the whole measurement period. All eight indoor air pollutants were far below their corresponding maximum allowable concentrations, except for the PM2.5 concentrations, which occasionally exceeded the concentration limits. The concentrations of indoor air pollutants in the subway station were basically within the corresponding standards. The correlation analyses showed that outdoor air pollutants have important influences on indoor air pollutants. The concentrations of PM10, PM2.5, SO2, NO2 and CO in the subway station were positively correlated with their corresponding outdoor concentrations. PM10 was statistically significantly correlated with the passenger flow and train frequency, but the other air pollutants were less impacted by the passenger flow and train frequency.

scholarly journals Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3348
Author(s):  
Yicao Ma ◽  
Shifeng Liu ◽  
Gang Xue ◽  
Daqing Gong
Keyword(s):  
Smart Cities ◽  
Experimental Testing ◽  
Rapid Development ◽  
Reference Value ◽  
Urban Environments ◽  
Subway Station ◽  
Functional Region ◽  
Feature Map ◽  
Passenger Flow ◽  
Subway Stations

The rapid development of urbanization has increased traffic pressure and made the identification of urban functional regions a popular research topic. Some studies have used point of interest (POI) data and smart card data (SCD) to conduct subway station classifications; however, the unity of both the model and the dataset limits the prediction results. This paper not only uses SCD and POI data, but also adds Online to Offline (OTO) e-commerce platform data, an application that provides customers with information about different businesses, like the location, the score, the comments, and so on. In this paper, these data are combined to and used to analyze each subway station, considering the diversity of data, and obtain a passenger flow feature map of different stations, the number of different types of POIs within 800 m, and the situation of surrounding OTO stores. This paper proposes a two-stage framework, to identify the functional region of subway stations. In the passenger flow stage, the SCD feature is extracted and converted to a feature map, and a ResNet model is used to get the output of stage 1. In the built environment stage, the POI and OTO features are extracted, and a deep neural network with stacked autoencoders (SAE–DNN) model is used to get the output of stage 2. Finally, the outputs of the two stages are connected and a SoftMax function is used to make the final identification of functional region. We performed experimental testing, and our experimental results show that the framework exhibits good performance and has a certain reference value in the planning of subway stations and their surroundings, contributing to the construction of smart cities.

Research on Calculation Methods of Urban Rail Transit Station Ticket Gates Load Degree

2013 ◽  
Vol 330 ◽  
pp. 569-573
Author(s):  
Chen Chen Zhang ◽  
Xu Zhang ◽  
Yan Hui Wang ◽  
Ling Xi Zhu
Keyword(s):  
Urban Rail Transit ◽  
The Other ◽  
Subway Station ◽  
Rail Transit ◽  
Calculation Methods ◽  
Passenger Flow ◽  
Urban Rail ◽  
Transit Station

There are many ticket gates at the subway station. The managers are quite careful about the load degree of the ticket gates. This paper studies two different methods to calculate the load degree of the ticket gates. One is based on the station ticket gates group as the research object, and the other one is based on the volume of passenger flow passing in and out of the station as the research object. Then we use the data investigated at Chaoyangmen station in Beijing to test and verify both of the methods. At last, we can get the conclusion that we can get the same result by the two methods, but the method of based on the volume of passenger flow passing in and out of the station is more reasonable.

Large Sections Peak Traffic Flow Subway Station Simulation Modeling

2014 ◽  
Vol 1065-1069 ◽  
pp. 3325-3328
Author(s):  
Xin Hua Zhang ◽  
Shu Hao Xu ◽  
Li Li Wu ◽  
Yin Hua Du ◽  
Zhi Jun Duan ◽  
...  
Keyword(s):  
Simulation Modeling ◽  
Flow Analysis ◽  
Local Area ◽  
Subway Station ◽  
Peak Time ◽  
Mathematical Software ◽  
Flow Data ◽  
Traffic Dynamics ◽  
Passenger Flow ◽  
Large Sections

This paper, three subway lines converge site meet a change to the peak time for passenger flow analysis, with the analysis of passenger flow field physical statistics, image processing technology to guide passenger flow data, analysis of local area biggest traffic speed and density, traffic dynamics theory, the application of mathematical software Matlab, establish mathematical model.

Passenger management strategy and evacuation in subway station under Covid-19

2021 ◽  
Author(s):  
Xiao-Xia Yang ◽  
Hai-Long Jiang ◽  
Yuan-Lei Kang ◽  
Yi Yang ◽  
Yong-Xing Li ◽  
...  
Keyword(s):  
Social Distance ◽  
Management Strategy ◽  
Emergency Evacuation ◽  
Path Selection ◽  
Subway Station ◽  
Time Interval ◽  
Flow Management ◽  
Passenger Flow ◽  
The Social ◽  
Simulation Results

Abstract Under the background of Covid-19 sweeping the world, safe and reasonable passenger flow management strategy in subway stations is an effective means to prevent the spread of virus. Based on the social force model and the minimum cost model, the movement and path selection behavior of passengers in the subway station are modeled, and a strategy for passenger flow management to maintain a safe social distance is put forward. Take Qingdao Jinggangshan Road subway station of China as the simulation scene, the validity of the simulation model is verified by comparing the measured value and simulation value of the time required for passengers from getting off the train to the ticket gate. Simulation results indicate that controlling the time interval between incoming passengers at the entrance can effectively control the social distance between passengers and reduce the risk of epidemic infection. By comparing the evacuation process of passengers under different initial densities, it is found that the greater the initial density of passengers, the longer the passengers are at risk social distance. In the process of passenger emergency evacuation, the stairs/escalators and ticket gates are bottleneck areas with high concentration of passenger density, which should be strictly disinfected many times on the basis of strictly checking the health code of incoming passengers and controlling the arrival time interval. The simulation results of this paper verify the harmfulness of passenger emergency evacuation without protective measures, and provide theoretical support for the operation and management of subway station under the epidemic situation.

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