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

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.

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Does Urban Rail Transit Discourage People from Owning and Using Cars? Evidence from Beijing, China

Journal of Advanced Transportation ◽

10.1155/2018/1835241 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Shasha Liu ◽

Enjian Yao ◽

Toshiyuki Yamamoto

Keyword(s):

Travel Distance ◽

Urban Rail Transit ◽

Mediating Effect ◽

Car Ownership ◽

Lower Probability ◽

Rail Transit ◽

Car Use ◽

Urban Rail ◽

Catchment Areas ◽

Beijing China

With the rapid urbanization and motorization, many cities are developing urban rail transit (URT) to reduce car dependence. This paper explores the URT effect on car ownership and use based on the home-based work tour data in Beijing, China. Considering the mediating effects of car ownership and travel distance simultaneously, we develop a structural equation model to examine the complex relationship among URT, car ownership, travel distance, and car use. The results indicate that URT plays an important role in reducing car dependence. Living within URT catchment areas by itself is not significantly associated with car ownership and use, but if the workplace is near a URT station, people are less likely to own and use cars. People who both live and work near URT station areas have lower probability of owning and using cars. Moreover, car ownership and travel distance mediate the relationship between URT and car use, and the mediating effect of car ownership is greater than travel distance. Our study verifies that URT does discourage people from owning and using cars, which may have important implications for developing cities to make response to the ongoing motorization.

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

Journal of Advanced Transportation ◽

10.1155/2021/7345807 ◽

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.

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Improved Huff Model for Estimating Urban Rail Transit Station Catchment Areas considering Station Choices

Journal of Advanced Transportation ◽

10.1155/2021/6374724 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Zhenjun Zhu ◽

Yudong He ◽

Xiucheng Guo ◽

Yibang Zhang ◽

Junlan Chen

Keyword(s):

Urban Rail Transit ◽

Rail Transit ◽

Transit Oriented Development ◽

Urban Rail ◽

Catchment Areas ◽

Feeder Bus ◽

Transit Station ◽

Bus Services ◽

Additive Form

Estimating urban rail transit station catchment areas is of great significance to deepening our understanding of Transit-Oriented Development in Chinese megacities. This study investigated station choices of residents and considered that residents may not only pay attention to the proximity to stations when the URT system develops into a relatively mature network. An improved Huff model was proposed to calculate the probability of residents’ station choice, which considered the station attractiveness. The station attractiveness is measured by three variables: walk score, public transport accessibility level, and service and facility index. The additive form based on multicriteria decision is adopted to incorporate experts’ opinions on the importance of three variables. In this study, extended catchment areas that can be accessed by cycling and feeder bus services are adopted to replace the conventional pedestrian-oriented catchment areas. A case study of Xi’an, China, was used to validate the applicability of the proposed methodology. The results revealed that the methodology effectively solved the problem. The findings could be used as a reference and provide technical support to policymakers and city planners with regard to the transport facilities configuration for URT station catchment areas, which contributes to facilitating transit-oriented development.

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