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 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.

scholarly journals How Do Different Treatments of Catchment Area Affect the Station Level Demand Modeling of Urban Rail Transit?

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Hongtai Yang ◽  
Xuan Li ◽  
Chaojing Li ◽  
Jinghai Huo ◽  
Yugang Liu
Keyword(s):  
Linear Regression ◽  
Catchment Area ◽  
Goodness Of Fit ◽  
Urban Rail Transit ◽  
Distance Decay ◽  
Rail Transit ◽  
Demand Modeling ◽  
Urban Rail ◽  
Catchment Areas ◽  
Log Linear

Direct demand modeling is a useful tool to estimate the demand of urban rail transit stations and to determine factors that significantly influence such demand. The construction of a direct demand model involves determination of the catchment area. Although there have been many methods to determine the catchment area, the choice of those methods is very arbitrary. Different methods will lead to different results and their effects on the results are still not clear. This paper intends to investigate this issue by focusing on three aspects related to the catchment area: size of the catchment area, processing methods of the overlapping areas, and whether to apply the distance decay function on the catchment area. Five catchment areas are defined by drawing buffers around each station with radius distance ranging from 300 to 1500 meters with the interval of 300 meters. Three methods to process the overlapping areas are tested, which are the naïve method, Thiessen polygon, and equal division. The effect of distance decay is considered by applying lower weight to the outer catchment area. Data from five cities in the United States are analyzed. Built environment characteristics within the catchment area are extracted as explanatory variables. Annual average weekday ridership of each station is used as the response variable. To further analyze the effect of regression models on the results, three commonly used models, including the linear regression, log-linear regression, and negative binomial regression models, are applied to examine which type of catchment area yields the highest goodness-of-fit. We find that the ideal buffer sizes vary among cities, and different buffer sizes do not have a great impact on the model’s goodness-of-fit and prediction accuracy. When the catchment areas are heavily overlapping, dividing the overlapping area by the number of times of overlapping can improve model results. The application of distance decay function could barely improve the model results. The goodness-of-fit of the three models is comparable, though the log-linear regression model has the highest prediction accuracy. This study could provide useful references for researchers and planners on how to select catchment areas when constructing direct demand models for urban rail transit stations.

scholarly journals The Effects of Weather on Passenger Flow of Urban Rail Transit

2020 ◽  
Vol 6 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Xiaoyuan Wang ◽  
Yongqing Guo ◽  
Chenglin Bai ◽  
Shanliang Liu ◽  
Shijie Liu ◽  
...  
Keyword(s):  
Urban Rail Transit ◽  
Rail Transit ◽  
Linear Regression Method ◽  
Effective Strategies ◽  
Passenger Flow ◽  
Transit Ridership ◽  
Urban Rail ◽  
Research Findings ◽  
Increasing Temperature ◽  
The Impact

Predicting passenger flow on urban rail transit is important for the planning, design and decision-making of rail transit. Weather is an important factor that affects the passenger flow of rail transit by changing the travel mode choice of urban residents. This study aims to explore the influence of weather on urban rail transit ridership, taking four cities in China as examples, Beijing, Shanghai, Guangzhou and Chengdu. To determine the weather effect on daily ridership rate, the three models were proposed with different combinations of the factors of temperature and weather type, using linear regression method.   The large quantities of data were applied to validate the developed models.  The results show that in Guangzhou, the daily ridership rate of rail transit increases with increasing temperature. In Chengdu, the ridership rate increases in rainy days compared to sunny days. While, in Beijing and Shanghai, the ridership rate increases in light rainfall and heavy rainfall (except moderate rainfall) compared to sunny days. The research findings are important to understand the impact of weather on passenger flow of urban rail transit. The findings can provide effective strategies to rail transit operators to deal with the fluctuation in daily passenger flow.

Sign in / Sign up

Export Citation Format

Share Document