scholarly journals Understanding the Propagation and Control Strategies of Congestion in Urban Rail Transit Based on Epidemiological Dynamics Model

Information ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 258 ◽  
Author(s):  
Shi ◽  
Zhang ◽  
Lei
Keyword(s):  
Control Strategies ◽  
Urban Rail Transit ◽  
Model Parameters ◽  
Rail Transit ◽  
Dynamics Model ◽  
Passenger Flow ◽  
Urban Rail ◽  
Passenger Volume ◽  
And Control ◽  
Demand Control

With the construction of the urban rail transit (URT) network, the explosion of passenger volume is more rapid than the increased capacity of the newly built infrastructure, which results in serious passenger flow congestion (PLC). Understanding the propagation process of PLC is the key to formulate sustainable policies for reducing congestion and optimizing management. This study proposes a susceptible-infected-recovered (SIR) model based on the theories of epidemiological dynamics and complex network to analyze the PLC propagation. We simulate the PLC propagation under various situations, and analyze the sensitivity of PLC propagation to model parameters. Finally, the control strategies of restricting PLC propagation are introduced from two aspects, namely, supply control and demand control. The results indicate that both of the two control strategies contribute to relieving congestion pressure. The propagating scope of PLC is more sensitive when taking mild supply control, whereas, the demand control strategy shows some advantages in flexibly implementing and dealing with serious congestion. These results are of important guidance for URT agencies to understand the mechanism of PLC propagation and formulate appropriate congestion control strategies.

scholarly journals Optimum Equilibrium Passenger Flow Control Strategies with Delay Penalty Functions under Oversaturated Condition on Urban Rail Transit

2021 ◽  
Vol 2021 ◽  
pp. 1-27
Author(s):  
Yonghao Yin ◽  
Dewei Li ◽  
Kai Zhao ◽  
Ruixia Yang
Keyword(s):  
Flow Control ◽  
Simulated Annealing Algorithm ◽  
Control Strategies ◽  
Penalty Functions ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit System ◽  
Passenger Flow ◽  
Urban Rail ◽  
Linear Delay

When passengers are oversaturated in the urban rail transit system and a further increase of train frequency is impossible, passenger flow control strategy is an indispensable approach to avoid congestion and ensure safety. To make the best use of train capacity and reduce the passenger waiting time, coordinative flow control is necessary at each station on a line. In most published studies, the equilibrium of passenger distributions among different stations and periods is not considered. As a result, two issues occur making it hard to implement in practical. First, a large number of passengers are held up outside a small number of stations for very long time. Second, there is a large variation of controlled flows for successive time intervals. To alleviate this problem, a single-line equilibrium passenger flow control model is constructed, which minimizes the total passenger delay. By applying different forms of the delay penalty function (constant and linear), flow control strategies such as independent flow control and equilibrium flow control can be reproduced. An improved simulated annealing algorithm is proposed to solve the model. A numerical case is studied to analyze the sensitivity of the functions, and the best parameter relationship in different functions could be confirmed. A real-world case from Batong Line corridor in Beijing subway is used to test the applicability of the model and algorithm, and the result shows that the solution with linear delay penalty functions can not only reduce the total passenger delay but also equilibrate the number of flow control passengers on spatial and temporal.

scholarly journals Research on intelligent prevention and control of COVID-19 in China’s urban rail transit based on artificial intelligence and big data

2020 ◽  
Vol 39 (6) ◽  
pp. 9085-9090
Author(s):  
Qi Liu ◽  
Zhenzhen Huang
Keyword(s):  
Prevention And Control ◽  
Iris Recognition ◽  
The Body ◽  
Urban Rail Transit ◽  
Public Security ◽  
Rail Transit ◽  
Confined Space ◽  
Passenger Flow ◽  
Urban Rail ◽  
And Control

Since December 2019, the outbreak of novel coronavirus pneumonia has brought great challenges to global public health, which is the most serious epidemic over the past hundred years. The urban rail transit is an important part of public transport in large cities with characteristic of intensive passengers and confined space, which is easy to become viral infection intermediary. In order to prevent and control the situation of the epidemic, the police’s public security department for urban rail transit and the urban rail transit operation company have established a three-layer filter network, which is composed of safety inspection, patrol and temporary interrogation, and intelligent police service, and this network implements the deep learning technology to identify key persons, prohibited luggage, and the body temperature of passengers. For the problem of uncertainty in total passenger flow and its density, this paper proposes a method for re-establishing the passenger flow model to focus on data monitoring, and resetting the threshold value of alarm to control the passenger density. In view of the difficulty of passenger identification caused by mask during the epidemic, this paper proposes a systematic schema of timely adjusting face recognition algorithm, modifying the alarm threshold, using iris recognition system, carrying out information collision comparison, deep mining and intelligent judging, which discover the high-risk groups of epidemic prevention and control in time. China’s police’s public security department for urban rail transit aims at prevention of virus input, infection, riot, fake new, scientific prevention and control, and has made precise policy implementation to hold urban rail transit’s covid-19 intelligent prevention and control work, finally won the battle and effectively guaranteed the people’s life safety and health.

Passenger flow control strategies for urban rail transit networks

2020 ◽  
Vol 82 ◽  
pp. 168-188 ◽  
Author(s):  
Fuya Yuan ◽  
Huijun Sun ◽  
Liujiang Kang ◽  
Jianjun Wu
Keyword(s):  
Flow Control ◽  
Control Strategies ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Networks ◽  
Passenger Flow ◽  
Urban Rail

Passenger Flow Forecast of Urban Rail Transit Based on Support Vector Regression

2013 ◽  
Vol 433-435 ◽  
pp. 612-616 ◽  
Author(s):  
Bin Xia ◽  
Fan Yu Kong ◽  
Song Yuan Xie
Keyword(s):  
Support Vector Regression ◽  
Risk Function ◽  
Urban Rail Transit ◽  
Support Vector ◽  
Rail Transit ◽  
Risk Minimization ◽  
Promising Alternative ◽  
Minimization Principle ◽  
Passenger Flow ◽  
Urban Rail

This study analyses and compares several forecast methods of urban rail transit passenger flow, and indicates the necessity of forecasting short-term passenger flow. Support vector regression is a promising method for the forecast of passenger flow because it uses a risk function consisting of the empirical error and a regularized term which is based on the structural risk minimization principle. In this paper, the prediction model of urban rail transit passenger flow is constructed. Through the comparison with BP neural networks forecast methods, the experimental results show that applying this method in URT passenger flow forecasting is feasible and it provides a promising alternative to passenger flow prediction.

Operation Organization at Urban Rail Transit Station under Large Passenger Flow

2012 ◽  
Vol 253-255 ◽  
pp. 1995-2000
Author(s):  
Qiao Mei Tang ◽  
Li Ping Shen ◽  
Xian Yong Tang
Keyword(s):  
Transport Process ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Common Condition ◽  
Urban Transit ◽  
Passenger Flow ◽  
Operation Process ◽  
Urban Rail ◽  
Transit Station ◽  
Transit Operation

large passenger flow is a common condition of urban transit operation, and the station bears the pressure of large passenger flow directly. This paper analyzes the reason for the appearance of large passenger flow and the characteristics of it, discusses the principles and methods that the station can apply under large passenger flow combined with the passenger’s transport process and the operation process.

Collaborative passenger flow control of urban rail transit network considering balanced distribution of passengers

2021 ◽  
pp. 2150461
Author(s):  
Xiang Li ◽  
Yan Bai ◽  
Kaixiong Su
Keyword(s):  
Flow Control ◽  
Programming Model ◽  
Flow Characteristics ◽  
Service Level ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Control Measures ◽  
Rail Transit ◽  
Passenger Flow ◽  
Urban Rail

The increase of urban traffic demands has directly affected some large cities that are now dealing with more serious urban rail transit congestion. In order to ensure the travel efficiency of passengers and improve the service level of urban rail transit, we proposed a multi-line collaborative passenger flow control model for urban rail transit networks. The model constructed here is based on passenger flow characteristics and congestion propagation rules. Considering the passenger demand constraints, as well as section transport and station capacity constraints, a linear programming model is established with the aim of minimizing total delayed time of passengers and minimizing control intensities at each station. The network constructed by Line 2, Line 6 and Line 8 of the Beijing metro is the study case used in this research to analyze control stations, control durations and control intensities. The results show that the number of delayed passengers is significantly reduced and the average flow control ratio is relatively balanced at each station, which indicates that the model can effectively relieve congestion and provide quantitative references for urban rail transit operators to come up with new and more effective passenger flow control measures.

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