scholarly journals Modeling and Optimization of Collaborative Passenger Control in Urban Rail Stations under Mass Passenger Flow

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Lili Wang ◽  
Xuedong Yan ◽  
Yun Wang
Keyword(s):  
Programming Model ◽  
Rapid Development ◽  
Urban Rail Transit ◽  
Control Measures ◽  
Target Station ◽  
Passenger Flow ◽  
Urban Rail ◽  
Entire Line ◽  
Passenger Flows ◽  
The One

With the rapid development of urban rail transit, the phenomenon of outburst passenger flows flocking to stations is occurring much more frequently. Passenger flow control is one of the main methods used to ensure passengers’ safety. While most previous studies have only focused on control measures inside the target station, ignoring the collaboration between stops, this paper puts emphasis on joint passenger control methods during the occurrence of large passenger flows. To provide a theoretic description for the problem under consideration, an integer programming model is built, based on the analysis of passenger delay and the processes by which passengers alight and board. Taking average passenger delay as the objective, the proposed model aims to disperse the pressure of oversaturated stations into others, achieving the optimal state for the entire line. The model is verified using a case study and the results show that restricted access measures taken collaboratively by stations produce less delay and faster evacuation. Finally, a sensitivity analysis is conducted, from which we find that the departure interval and maximum conveying capacity of the train affect passenger delay markedly in the process of passenger control and infer that control measures should be taken at stations near to the one experiencing an emergency.

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.

Short-Time Calculation Method of Passenger Flows on Urban Rail Transit Island Platform

2014 ◽  
Vol 644-650 ◽  
pp. 2133-2136
Author(s):  
Tian Shi Li
Keyword(s):  
Probability Theory ◽  
Calculation Method ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Numerical Examples ◽  
Passenger Flow ◽  
Urban Rail ◽  
Time Calculation ◽  
Passenger Flows ◽  
Short Time

Because urban tail transit becomes the preferred way to travel for more travelers, the passenger flow of rail transit is increasing fast. Due to the increased passenger, the congestion on platform reduces the comfort and puts passengers in danger. This article analyses the model of island platform short-time arrivals based on the probability theory and historical statistics. The calculation method is studied and the Feasibility and algorithm is testified by setting numerical examples.

Influence of Emergency Passenger Flow Distribution in Urban Rail Network

2012 ◽  
Vol 450-451 ◽  
pp. 295-301 ◽  
Author(s):  
Ling Hong ◽  
Jia Gao ◽  
Rui Hua Xu
Keyword(s):  
Flow Distribution ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Abstract Model ◽  
Flow Volume ◽  
Rail Network ◽  
Passenger Flow ◽  
Urban Rail ◽  
Passenger Flows ◽  
Urban Rail Transit Network

The emergency disposal of urban rail transit needs to accurately estimate the emergency range and total affected passenger flow volume. The urban rail transit network could be simplified to an abstract model which is easy to be analyst based on the graph theory method. Considering the actual network back-turning lines and vehicle storage tracks of urban rail network, the emergency range could be estimated effectively. The affected passenger flow could be classified as different kinds based on the different paths of passenger flow. The classification of passenger flow mainly includes “delay passenger flow”, “detour passenger flow” and “loss passenger flow”. Considering the emergency range, the different affected passenger flows could be superposed over time based on the abstract model, then the affected passenger flow volume and virtual loss time could be calculated out. The results could provide basis for the emergency disposal in urban rail transit. The example analysis is verified the feasibility of this method.

scholarly journals Urban Rail Transit System Network Reliability Analysis Based on a Coupled Map Lattice Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shaojie Wu ◽  
Yan Zhu ◽  
Ning Li ◽  
Yizeng Wang ◽  
Xingju Wang ◽  
...  
Keyword(s):  
Complex Network ◽  
Network Reliability ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Weighted Network ◽  
Network Nodes ◽  
Transit Network ◽  
Passenger Flow ◽  
Urban Rail ◽  
Passenger Flows

During the last twenty years, the complex network modeling approach has been introduced to assess the reliability of rail transit networks, in which the dynamic performance involving passenger flows have attracted more attentions during operation stages recently. This paper proposes the passenger-flow-weighted network reliability evaluation indexes, to assess the impact of passenger flows on network reliability. The reliability performances of the rail transit network and passenger-flow-weighted one are analyzed from the perspective of a complex network. The actual passenger flow weight of urban transit network nodes was obtained from the Shanghai Metro public transportation card data, which were used to assess the reliability of the passenger-flow-weighted network. Furthermore, the dynamic model of the Shanghai urban rail transit network was constructed based on the coupled map lattice (CML) model. Then, the processes of cascading failure caused by network nodes under different destructive situations were simulated, to measure the changes of passenger-flow-weighted network reliability during the processes. The results indicate that when the scale of network damage attains 50%, the reliability of the passenger-flow-weighted network approaches zero. Consequently, taking countermeasures during the initial stage of network cascading may effectively prevent the disturbances from spreading in the network. The results of the paper could provide guidelines for operation management, as well as identify the unreliable stations within passenger-flow-weighted networks.

scholarly journals Research on Passenger Flow Early Warning of Urban Rail Transit Station Based on System Dynamics

2020 ◽  
Vol 308 ◽  
pp. 01003
Author(s):  
Hui Chen ◽  
Bo Wang ◽  
Wei He ◽  
Jianhu Zheng
Keyword(s):  
System Dynamics ◽  
Early Warning ◽  
Large Scale ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Passenger Flow ◽  
Urban Rail ◽  
Passenger Flows ◽  
Transit Station ◽  
The Impact

Large-scale passenger flows occur frequently during the peak hours of urban rail transit stations and on holidays. Thus, the timely and accurate early warning of impending large-scale passenger flows can positively impact the operational safety of the entire station. By further deepening the definition of passenger flow warnings in stations, a new model of urban rail transit station passenger flow based on system dynamics is constructed. The method of determining the key area of passenger flows in the early warning stage based on streamlines is proposed; the key indicators and thresholds affecting early warnings are studied. Finally, taking a typical station as an example, a station model is built using Anylogic software. The parameter sensitivity analysis is used to determine the impact of each key indicator on the passenger flow in the key area of the station early warning, and the reference threshold of each indicator is determined.

scholarly journals Equity-Oriented Train Timetabling with Collaborative Passenger Flow Control: A Spatial Rebalance of Service on an Oversaturated Urban Rail Transit Line

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Chaoqi Gong ◽  
Baohua Mao ◽  
Min Wang ◽  
Tong Zhang
Keyword(s):  
Flow Control ◽  
Programming Model ◽  
Optimal Solution ◽  
Urban Rail Transit ◽  
Mixed Integer ◽  
Rail Transit ◽  
Optimization Approach ◽  
Train Timetabling ◽  
Passenger Flow ◽  
Urban Rail

On an oversaturated urban rail transit line, passengers at downstream stations have to wait for more trains until they get aboard, resulting in service imbalance problem. To improve the service quality, this paper proposes an integrated optimization approach combining the train timetabling and collaborative passenger flow control, with the aim of minimizing indicators associated with the passenger service imbalance and train loading capacity utilization. Considering train regulation constraints and passenger loading dynamics, a mixed-integer linear programming model is formulated. Based on the linear weighting technique, an iterative heuristic algorithm combining the tabu search and Gurobi solver is designed to solve the proposed model. Finally, a simple case with different-scale instances is used to verify that the proposed algorithm can obtain near-optimal solution efficiently. Moreover, a real-world case of Beijing Subway Batong Line is implemented to compare performances of the proposed approach with those under the original timetable and noncollaborative passenger flow control.

scholarly journals Urban Rail Timetable Optimization to Improve Operational Efficiency with Flexible Routing Plans: A Nonlinear Integer Programming Model

2019 ◽  
Vol 11 (13) ◽  
pp. 3701 ◽  
Author(s):  
Qiuchi Xue ◽  
Xin Yang ◽  
Jianjun Wu ◽  
Huijun Sun ◽  
Haodong Yin ◽  
...  
Keyword(s):  
Integer Programming ◽  
Waiting Time ◽  
Optimization Model ◽  
Programming Model ◽  
Urban Rail Transit ◽  
Nonlinear Integer Programming ◽  
Load Factor ◽  
Integer Programming Model ◽  
Passenger Flow ◽  
Urban Rail

At present, most urban rail transit systems adopt an operation mode with a single long routing. The departure frequency is determined by the maximum section passenger flow. However, when the passenger flow varies greatly within different sections, this mode will lead to a low load factor in some sections, resulting in a waste of capacity. In view of this situation, this paper develops a nonlinear integer programming model to determine an optimal timetable with a balanced scheduling mode, where the wasted capacity at a constant departure frequency can be reduced with a slight increase in passenger waiting time. Then, we simplify the original model into a single-objective integer optimization model through normalization. A genetic algorithm is designed to find the optimal solution. Finally, a numerical example is presented based on real-world passenger and operation data from Beijing Metro Line 4. The results show that the double-routing optimization model can reduce wasted capacity by 9.5%, with a 4.5% increase in passenger waiting time, which illustrates the effectiveness of this optimization model.

scholarly journals Convolutional Neural Network for Crowd Counting on Metro Platforms

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 703
Author(s):  
Jun Zhang ◽  
Jiaze Liu ◽  
Zhizhong Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Estimation Error ◽  
Image Features ◽  
Urban Rail Transit ◽  
Crowd Counting ◽  
Passenger Flow ◽  
Urban Rail ◽  
Density Map ◽  
Flow Detection

Owing to the increased use of urban rail transit, the flow of passengers on metro platforms tends to increase sharply during peak periods. Monitoring passenger flow in such areas is important for security-related reasons. In this paper, in order to solve the problem of metro platform passenger flow detection, we propose a CNN (convolutional neural network)-based network called the MP (metro platform)-CNN to accurately count people on metro platforms. The proposed method is composed of three major components: a group of convolutional neural networks is used on the front end to extract image features, a multiscale feature extraction module is used to enhance multiscale features, and transposed convolution is used for upsampling to generate a high-quality density map. Currently, existing crowd-counting datasets do not adequately cover all of the challenging situations considered in this study. Therefore, we collected images from surveillance videos of a metro platform to form a dataset containing 627 images, with 9243 annotated heads. The results of the extensive experiments showed that our method performed well on the self-built dataset and the estimation error was minimum. Moreover, the proposed method could compete with other methods on four standard crowd-counting datasets.

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.

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