scholarly journals Intuitionistic Fuzzy Factorial Analysis Model for Supplier Selection of Urban Rail Transit Companies within a Random Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hui Sun ◽  
Hanlin Li ◽  
Yuning Wang ◽  
Yufei Yang
Keyword(s):  
Random Environment ◽  
Supplier Selection ◽  
Indicator System ◽  
Urban Rail Transit ◽  
Factorial Analysis ◽  
Rail Transit ◽  
Analysis Model ◽  
Intuitionistic Fuzzy ◽  
Urban Rail ◽  
Selection Of

Facing serious environmental and traffic problems, urban rail transit companies, with the features of large capacity and high efficiency, have become an important choice for many large cities that are prioritizing public transportation and encouraging green travel options. As the construction speed of rail transit projects accelerates, the demand for materials and devices required for construction and operation is also increasing for urban rail transit companies. Therefore, the scientific selection of suppliers to meet construction and operation demands has become a problem that must be addressed. This paper presents an intuitionistic fuzzy factorial analysis model in a random environment, where correlative phenomena among each of the indicators and a random decision-making environment are considered. The evaluation indicator system of rail suppliers is established by considering the influencing factors. The extracted common factors indicate the nature of the studied object in a most direct way. The suppliers are evaluated from the perspective of the number of intuitionistic fuzzy factors and are ranked by their scores. Finally, the Tianjin urban rail transit company is used as a case study to illustrate the validity and feasibility of the method. The results can help urban rail transit companies improve their existing supplier selection method.

Risk Assessment of Crowd Crushing and Tramping Accidents in Urban Rail Transit Station Based on DEA

2015 ◽  
Vol 730 ◽  
pp. 327-330 ◽  
Author(s):  
Zhi Da Jiao ◽  
Chun Hui Gan
Keyword(s):  
Risk Assessment ◽  
Indicator System ◽  
Assessment Method ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Data Envelopment ◽  
Urban Rail ◽  
Risk Assessment Method ◽  
Transit Station ◽  
Matlab Programming

Based on the operation and management of station, the research was mainly about the risk assessment method of the crowd crushing and tramping accidents in urban rail transit stations. The indicator system about the crowd crushing and tramping accidents in urban rail transit station was established. The C2R model of data envelopment analysis (DEA) was used in the risk assessment, and the model was solved with MATLAB programming. The result of the analysis is generally consistent with the actual situation.

scholarly journals Effects of Coverage Area Treatment, Spatial Analysis Unit, and Regression Model on the Results of Station-Level Demand Modeling of Urban Rail Transit

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hongtai Yang ◽  
Chaojing Li ◽  
Xuan Li ◽  
Jinghai Huo ◽  
Yi Wen ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Model Selection ◽  
Linear Regression ◽  
Regression Models ◽  
Negative Binomial ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Ridership ◽  
Urban Rail ◽  
Selection Of

Direct ridership models can predict station-level urban rail transit ridership. Previous research indicates that the direct modeling of urban rail transit ridership uses different coverage overlapping area processing methods (such as naive method or Thiessen polygons), area analysis units (such as census block group and census tract), and various regression models (such as linear regression and negative binomial regression). However, the selection of these methods and models seems arbitrary. The objective of this research is to suggest methods of station-level urban rail transit ridership model selection and evaluate the impact of this selection on ridership model results and prediction accuracy. Urban rail transit ridership data in 2010 were collected from five cities: New York, San Francisco, Chicago, Philadelphia, and Boston. Using the built environment characteristics as the independent variables and station-level ridership as the dependent variable, an analysis was conducted to examine the differences in the model performance in ridership prediction. Our results show that a large overlap of circular coverage areas will greatly affect the accuracy of models. The equal division method increases model accuracy significantly. Most models show that the generalized additive models have lower mean absolute percentage errors (MAPE) and higher adjusted R 2 values. By comparison, the Akaike information criterion (AIC) values of the negative binomial models are lower. The influence of different basic spatial analysis unit on the model results is marginal. Therefore, the selection of basic area unit can use existing data. In terms of model selection, advanced models seem to perform better than the linear regression models.

scholarly journals Spatial–Temporal Features of Coordination Relationship between Regional Urbanization and Rail Transit—A Case Study of Beijing

2021 ◽  
Vol 19 (1) ◽  
pp. 212
Author(s):  
Xuanxuan Xia ◽  
Hongchang Li ◽  
Xujuan Kuang ◽  
Jack Strauss
Keyword(s):  
Urban Area ◽  
Spatial Correlation ◽  
Urban Areas ◽  
Central City ◽  
Indicator System ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Urban Rail ◽  
Degree Of Coupling ◽  
Coordination Degree

Urban rail transit is an important transportation infrastructure that mitigates the congestion of the central city and realizes compact city space development. However, the literature on the spatiotemporal coupling of urbanization and rail transit from the urban scale and its influencing factors is still uncommon. Taking Beijing as an example, based on the theory of coupling coordination, we have constructed a comprehensive indicator system for regional urbanization (hereafter RU) (including population, economy, and spatial urbanization) and rail transit (hereafter RT). On this basis, we use the entropy method, coupling coordination degree model, and spatial autocorrelation analysis method to explore the spatiotemporal characteristics of the overall and pairwise coupling coordination between population, economy, spatial urbanization, and rail transit. Finally, we analyze the spatial correlation and standard deviational ellipse analysis of the coupling coordination degree between RU and RT. The results indicate the following: (1) In addition to population urbanization, the other urbanization indicators and the RT level all show a downward–rising–falling trend from 2006 to 2017, among which the level of economic urbanization is the highest. The degree of coupling coordination between RU and RT is unbalanced development and shows a trend of first rising and then falling. (2) The degree of coupling coordination between RU and RT presents an imbalanced distribution in various regions, and the coupling coordination degree in the central urban areas is significantly higher than that in the outer suburbs. (3) From 2006 to 2017, the spatial correlation of the coupling coordination degree between the various systems has a similar changing trend. Moreover, the distribution of the spatial agglomeration points of the coupling coordination degree between RU and the RT is similar, showing a decreasing trend from the central urban area to the surrounding urban area. Therefore, relevant departments can rationally plan the construction of urban rail transit according to the coordination relationship between RU and RT and the spatial aggregation degree to realize the benign and sustainable development between urban especially suburbanization and rail transit.

scholarly journals Assessing Customer Satisfaction of Urban Rail Transit Network in Tianjin Based on Intuitionistic Fuzzy Group Decision Model

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yuning Wang ◽  
Zhe Zhang ◽  
Hui Sun
Keyword(s):  
Customer Satisfaction ◽  
Decision Model ◽  
Group Decision ◽  
Urban Rail Transit ◽  
Fuzzy Entropy ◽  
Rail Transit ◽  
Satisfaction Level ◽  
Intuitionistic Fuzzy ◽  
Urban Rail ◽  
Intuitionistic Fuzzy Entropy

Urban rail transit has played an important role in big and crowded cities. Providing services with high levels of customer satisfaction is essential in order to increase the sharing rate of urban rail transit and to reduce traffic congestion by shifting people away from private car use. Therefore, it is of great significance to improve the customer satisfaction of urban rail transit. This paper presents an intuitionistic fuzzy group decision model to evaluate the customer satisfaction of urban rail transit. An evaluation indicator system including seven categories of indicators is established to measure passengers’ satisfaction. The overall customer satisfaction level of the urban rail transit lines is obtained by the intuitionistic fuzzy entropy and intuitionistic fuzzy weighted average (IFWA) operator. The intuitionistic fuzzy entropy is used to solve attribute weights and IFWA operator is used to solve the information aggregation. Drawing on Tianjin urban rail transit lines as a case study, the detailed analyses were conducted to evaluate the overall customer satisfaction level of five urban rail transit lines and as such suggesting remedy strategies. The results can help urban rail transit operation company to improve the service quality of urban rail transit.

Sensitivity Analysis about Traffic Mode Selection of Lintong Metro Line Based on Logit Model

2014 ◽  
Vol 587-589 ◽  
pp. 1977-1980
Author(s):  
Tian Li ◽  
Kuan Min Chen ◽  
Li Jie Yu ◽  
Rui Ling Li
Keyword(s):  
Logit Model ◽  
Mode Selection ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Network ◽  
Urban Rail ◽  
Intercity Railway ◽  
Urban Rail Transit Network ◽  
Traffic Corridors ◽  
Selection Of

Lintong Metro Line is one intercity railway transit line of the “Xi’an urban rail transit network (revision)”. This line forms an important link between Xi’an and Lintong.The main modes of this link are cars, buses, subway and intercity railway. To determine different modes of traffic corridors sharing ratio, this paper determines the logit model coefficients by analyzes the characteristics of different traffic modes, according to the person trip survey of Xi’an. Then finalizing the sharing ratio range of different traffic modes as well as the shared variability when the intercity railway is not repaired.

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