Evaluation of subway passengers transfer hub congestion based on queuing model and TOPSIS method

2021 ◽  
pp. 1-22
Author(s):  
Xiaokun Wang ◽  
Dong Ni
Keyword(s):  
Queuing Theory ◽  
Evaluation Model ◽  
Flow Characteristics ◽  
Distribution Area ◽  
Flow Density ◽  
Topsis Method ◽  
Queuing Model ◽  
Service Facility ◽  
Passenger Flow ◽  
Topsis Technique

To scientifically and reasonably evaluate and pre-warn the congestion degree of subway transfer hub, and effectively know the risk of subway passengers before the congestion time coming. We analyzed the passenger flow characteristics of various service facilities in the hub. The congested area of the subway passenger flow interchange hub is divided into queuing area and distribution area. The queuing area congestion evaluation model selects M/M/C and M/G/C based on queuing theory. The queuing model and the congestion evaluation model of the distribution area select the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. Queue length and waiting time are selected as the evaluation indicators of congestion in the queuing area, and passenger flow, passenger flow density and walking speed are selected as the evaluation indicators of congestion in the distribution area. And then, K-means cluster analysis method is used to analyze the sample data, and based on the selected evaluation indicators and the evaluation model establishes the queuing model of the queuing area and the TOPSIS model of the collection and distribution area. The standard value of the congestion level of various service facilities and the congestion level value of each service facility obtained from the evaluation are used as input to comprehensively evaluate the overall congestion degree of the subway interchange hub. Finally we take the Xi’an Road subway interchange hub in Dalian as empirical research, the data needed for congestion evaluation was obtained through field observations and questionnaires, and the congestion degree of the queue area and the distribution area at different times of the workday was evaluated, and the congestion of each service facility was evaluated. The grade value is used as input, and the TOPSIS method is used to evaluate the degree of congestion in the subway interchange hub, which is consistent with the results of passenger congestion in the questionnaire, which verifies the feasibility of the evaluation model and method.

scholarly journals Multiobjective Optimization Model of Production Planning in Cloud Manufacturing Based on TOPSIS Method with Combined Weights

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Zhiru Li ◽  
Wei Xu ◽  
Huibin Shi ◽  
Qingshan Zhang ◽  
Fengyi He
Keyword(s):  
Multiobjective Optimization ◽  
Production Planning ◽  
Mass Customization ◽  
Evaluation Model ◽  
Cloud Manufacturing ◽  
Production Plan ◽  
Topsis Method ◽  
Nsga Ii ◽  
Optimal Production ◽  
Planning Scheme

Combined with the research of mass customization and cloud manufacturing mode, this paper discussed the production planning of mass customization enterprises in the context of cloud manufacturing in detail, analyzed the attribute index of manufacturing resource combination, and given a system considering the characteristics of batch production in mass customization and the decentralization of manufacturing resources in cloud manufacturing environment. Then, a multiobjective optimization model has been constructed according to the product delivery date, product cost, and product quality that customers care most about. The Pareto solution set of production plan has been obtained by using NSGA-II algorithm. This paper established a six-tier attribute index system evaluation model for the optimization of production planning scheme set of mass customization enterprises in cloud manufacturing environment. The weight coefficients of attribute indexes were calculated by combining subjective and objective weights with analytic hierarchy process (AHP) and entropy weight method. Finally, the combined weights calculated were applied to the improved TOPSIS method, and the optimal production planning scheme has been obtained by ranking. This paper validated the effectiveness and feasibility of the multiobjective model and NSGA-II algorithm by the example of company A. The Pareto effective solution has been obtained by solving the model. Then the production plan set has been sorted synthetically according to the comprehensive evaluation model, and the optimal production plan has been obtained.

scholarly journals A Decision-Making Approach Based on TOPSIS Method for Ranking Smart Cities in the Context of Urban Energy

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2691
Author(s):  
Sławomira Hajduk ◽  
Dorota Jelonek
Keyword(s):  
Decision Making ◽  
Smart Cities ◽  
Energy Performance ◽  
Linear Ordering ◽  
Topsis Method ◽  
Research Papers ◽  
The World ◽  
Urban Energy ◽  
World Council ◽  
Topsis Technique

This paper presents the use of multi-criteria decision-making (MCDM) for the evaluation of smart cities. During the development of the method, the importance of the decision-making approach in the linear ordering of cities was presented. The method of using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was proposed for the preparation of ranking. The method was verified by the application in the measurement of energy performance in smart cities. The authors conducted a literature review of research papers related to urban energy and MCDM published in the period from 2010 to 2020. The paper uses data from the World Council on City Data (WCCD). The research conducted allowed for the identification of the most popular MCDM techniques in the field of urban energy such as TOPSIS, AHP and DEA. The TOPSIS technique was used to organize and group the analyzed cities. Porto took the top position, whereas Buenos Aries was the last.

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.

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

2021 ◽  
pp. 036119812110302
Author(s):  
Chao Wang ◽  
Weijie Chen ◽  
Yueru Xu ◽  
Zhirui Ye
Keyword(s):  
Approximation Method ◽  
Service Time ◽  
Diffusion Approximation ◽  
Coefficient Of Variation ◽  
Queuing Theory ◽  
Queuing Model ◽  
Bus Stop ◽  
Proposed Model ◽  
Bus Stops ◽  
Bus Service

For bus service quality and line capacity, one critical influencing factor is bus stop capacity. This paper proposes a bus capacity estimation method incorporating diffusion approximation and queuing theory for individual bus stops. A concurrent queuing system between public transportation vehicles and passengers can be used to describe the scenario of a bus stop. For most of the queuing systems, the explicit distributions of basic characteristics (e.g., waiting time, queue length, and busy period) are difficult to obtain. Therefore, the diffusion approximation method was introduced to deal with this theoretical gap in this study. In this method, a continuous diffusion process was applied to estimate the discrete queuing process. The proposed model was validated using relevant data from seven bus stops. As a comparison, two common methods— Highway Capacity Manual (HCM) formula and M/M/S queuing model (i.e., Poisson arrivals, exponential distribution for bus service time, and S number of berths)—were used to estimate the capacity of the bus stop. The mean absolute percentage error (MAPE) of the diffusion approximation method is 7.12%, while the MAPEs of the HCM method and M/M/S queuing model are 16.53% and 10.23%, respectively. Therefore, the proposed model is more accurate and reliable than the others. In addition, the influences of traffic intensity, bus arrival rate, coefficient of variation of bus arrival headway, service time, coefficient of variation of service time, and the number of bus berths on the capacity of bus stops are explored by sensitivity analyses.

A spatial queuing model for the location decision of emergency medical vehicles for pandemic outbreaks: the case of Za'atari refugee camp

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Felix Blank
Keyword(s):  
Queuing Theory ◽  
Emergency Service ◽  
Performance Metrics ◽  
Service System ◽  
Refugee Camps ◽  
Location Decision ◽  
Stress Tests ◽  
Queuing Model ◽  
Content Type ◽  
Emergency Medical

PurposeRefugee camps can be severely struck by pandemics, like potential COVID-19 outbreaks, due to high population densities and often only base-level medical infrastructure. Fast responding medical systems can help to avoid spikes in infections and death rates as they allow the prompt isolation and treatment of patients. At the same time, the normal demand for emergency medical services has to be dealt with as well. The overall goal of this study is the design of an emergency service system that is appropriate for both types of demand.Design/methodology/approachA spatial hypercube queuing model (HQM) is developed that uses queuing-theory methods to determine locations for emergency medical vehicles (also called servers). Therefore, a general optimization approach is applied, and subsequently, virus outbreaks at various locations of the study areas are simulated to analyze and evaluate the solution proposed. The derived performance metrics offer insights into the behavior of the proposed emergency service system during pandemic outbreaks. The Za'atari refugee camp in Jordan is used as a case study.FindingsThe derived locations of the emergency medical system (EMS) can handle all non-virus-related emergency demands. If additional demand due to virus outbreaks is considered, the system becomes largely congested. The HQM shows that the actual congestion is highly dependent on the overall amount of outbreaks and the corresponding case numbers per outbreak. Multiple outbreaks are much harder to handle even if their cumulative average case number is lower than for one singular outbreak. Additional servers can mitigate the described effects and lead to enhanced resilience in the case of virus outbreaks and better values in all considered performance metrics.Research limitations/implicationsSome parameters that were assumed for simplification purposes as well as the overall model should be verified in future studies with the relevant designers of EMSs in refugee camps. Moreover, from a practitioners perspective, the application of the model requires, at least some, training and knowledge in the overall field of optimization and queuing theory.Practical implicationsThe model can be applied to different data sets, e.g. refugee camps or temporary shelters. The optimization model, as well as the subsequent simulation, can be used collectively or independently. It can support decision-makers in the general location decision as well as for the simulation of stress-tests, like virus outbreaks in the camp area.Originality/valueThe study addresses the research gap in an optimization-based design of emergency service systems for refugee camps. The queuing theory-based approach allows the calculation of precise (expected) performance metrics for both the optimization process and the subsequent analysis of the system. Applied to pandemic outbreaks, it allows for the simulation of the behavior of the system during stress-tests and adds a further tool for designing resilient emergency service systems.

scholarly journals Optimization of Design Scheme for Toll Plaza Based on M/M/C Queuing Theory and Cellular Automata Simulation Algorithm

2017 ◽  
Vol 11 (7) ◽  
pp. 1 ◽  
Author(s):  
Yi-Jian Liu ◽  
Jian Cao ◽  
Xiao-Yan Cao ◽  
Yuan-Biao Zhang
Keyword(s):  
Cellular Automata ◽  
Traffic Control ◽  
Queuing Theory ◽  
Low Cost ◽  
Evaluation Model ◽  
Original Design ◽  
Safety Coefficient ◽  
Toll Plazas ◽  
Important Field ◽  
Toll Plaza

As an important field in traffic control science, the research in design of toll plazas has increasingly attracted attention of scholars and society. A good design of toll plaza needs to meet a lot of conditions, such as high safety coefficient, high throughput and low cost level. In this study, we established an evaluation model of toll plaza based on cellular automata and M/M/C queuing theory applying to three aspects: safety coefficient, throughput and cost. Then, we took the Asbury Park Toll Plaza in New Jersey as an example to analyze its performance and further optimized the design of the toll plaza. Compared with the original design, the optimized toll plaza we designed is proved to be safer and preferable. Last but not least, we further analyzed the robustness of the designed toll plaza, proving that the designed toll plaza had a preferable performance in reality.

scholarly journals The Model for Dilution Process of Landslide Triggered Debris Flow —A Case of Guanba River in Tibet Southeastern Plateau

2018 ◽  
Vol 22 (2) ◽  
pp. 103-111 ◽  
Author(s):  
Jun Li ◽  
Ningsheng Chen
Keyword(s):  
Debris Flow ◽  
Water Flow ◽  
Debris Flows ◽  
Evaluation Model ◽  
Flow Density ◽  
Flow Section ◽  
Average Depth ◽  
Flow Discharge ◽  
Study Results ◽  
Density Evaluation

Understanding and modeling the downstream dilution process of a landslide triggered debris flow is the foundation for recognizing the boundary condition and dilution mechanism of this type of debris flow, and this serves as the theoretical basis for the categorized control of viscous debris flows, diluted debris flows, hyperconcentration flows and flash floods in a drainage basin. In this study, taking as an example a typical debris flow that occurred in the Guanba River on Tibet’s southeastern plateau on July 6th, 1998, empirical models are used to calculate the density, water flow discharge, debris flow discharge, average depth of loose materials and channel gradient at 11 cross-sections upstream to downstream in the debris flow. On this basis, the dilution characteristics and debris flow dilution process are analyzed in this study. According to the correlation between the debris flow density and the water-soil ratio and channel gradient, we have established the density evaluation model for the debris flow dilution process, which can predict the dilution process of a landslide triggered debris flow. The study results include the following four aspects: (1) The key factors in the dilution process of landslide triggered debris flows are the water flow discharge, average depth of loose materials and channel gradient. (2) The debris flow dilution characteristics in the Guanba River in 1998 include the occurrence of the debris flow dilution process after a significant increase in the water-soil ratio; an increase in the proportion of fine particles after dilution of the debris flow; and the size distribution of grain is “narrowed.” (3) In accordance with the density and dilution characteristics, the debris flow dilution process in the Guanba River can be divided into the upstream viscous debris flow section, midstream and downstream transitional debris flow section and downstream diluted debris flow section. (4) The density evaluation model for the debris flow dilution process is expressed by the Lorentz equation, and this model can reflect the debris flow dilution process such that the debris flow density will decrease gradually with an increase in the water-soil ratio and decrease in channel gradient. The density evaluation model for the debris flow dilution process has been verified by three debris flow cases, which include Gaoqiao Gully, Haizi Valley, and Aizi Valley

scholarly journals Applications of Queuing Theory in Quantitative Business Analysis

2020 ◽  
pp. 253-257
Keyword(s):  
Quantitative Analysis ◽  
Waiting Time ◽  
Queuing Theory ◽  
Single Channel ◽  
Analysis Approach ◽  
Single Server ◽  
Expected Number ◽  
Queuing Model ◽  
Infinite Population ◽  
Expected Waiting Time

Queuing Theory provides the system of applications in many sectors in life cycle. Queuing Structure and basic components determination is computed in queuing model simulation process. Distributions in Queuing Model can be extracted in quantitative analysis approach. Differences in Queuing Model Queue discipline, Single and Multiple service station with finite and infinite population is described in Quantitative analysis process. Basic expansions of probability density function, Expected waiting time in queue, Expected length of Queue, Expected size of system, probability of server being busy, and probability of system being empty conditions can be evaluated in this quantitative analysis approach. Probability of waiting ‘t’ minutes or more in queue and Expected number of customer served per busy period, Expected waiting time in System are also computed during the Analysis method. Single channel model with infinite population is used as most common case of queuing problems which involves the single channel or single server waiting line. Single Server model with finite population in test statistics provides the Relationships used in various applications like Expected time a customer spends in the system, Expected waiting time of a customer in the queue, Probability that there are n customers in the system objective case, Expected number of customers in the system

scholarly journals Fuzzy Topsis in the Assessment of OHS Management System

2019 ◽  
Vol 1 (1) ◽  
pp. 18-25
Author(s):  
Joanna Tabor
Keyword(s):  
Health And Safety ◽  
Fuzzy Topsis ◽  
Point Of View ◽  
Assessment Criteria ◽  
Management Systems ◽  
Manufacturing Companies ◽  
Topsis Method ◽  
Furniture Manufacturing ◽  
Order Preference ◽  
Topsis Technique

AbstractOccupational health and safety (OHS) management is a cycle of decision-making processes, many of which are in fact multi-criterion processes in nature. Therefore, it is important to look for and develop tools to support decision-makers in their actions aimed at improving work safety levels. The objective of this paper is to propose and verify the fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method applied to compare and assess the ways OHS management systems function in different companies. The fuzzy TOPSIS method has already been used for a number of years in assessments of alternative solutions in many different areas, but the application that uses ordered fuzzy numbers is quite original in nature. It is especially beneficial to use the fuzzy approach in OHS management systems, as it makes it possible for experts to assess different criteria using most frequently used linguistic variables. The adopted approach was verified in the study of OHS management systems in four furniture manufacturing companies. Assessment criteria were requirements of the PN-N 18001: 2004 Standard. Thanks to the ordered fuzzy TOPSIS method, the analysed OHS management systems were streamlined from the point of view of 24 assessment criteria, and the best and the worst functioning system was identified. The approach presented here may constitute a significant tool for improving OHS management systems.

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