Abstract
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.
Highlights
Urban rail transit is a popular form of urban public transportation because of its large capacity, environmental friendliness, and fast speed. e emergence of the urban rail transit has alleviated the problems of congestion and exhaust pollution caused by private vehicles in the city
A growing number of mega cities have adopted various measures to develop rail transit systems. In cities such as Seoul and Shanghai, the government promotes the use of urban rail transits through transit-oriented development (TOD) [2, 3]. e station-level ridership is a main factor for determining the operation and planning of the urban rail transit system
With the same model parameters and analysis units, the equal division method in processing the overlapping area appears to be the most effective method overall with average adjusted R2 0.6, mean absolute percentage errors (MAPE) 112.078, and Akaike information criterion (AIC) 8020.195, which is better than the naive method (0.454, 164.758, and 8094.405) and the iessen polygon method (0.560, 126.657, and 8046.428), respectively. e method using iessen polygons performs better than the naive method as well. is is likely because when urban stations are densely located, the use of naive method recalculates a large amount of data, while the other two methods can better dilute the situation and reflect it around the station more closely
Summary
Urban rail transit is a popular form of urban public transportation because of its large capacity, environmental friendliness, and fast speed. e emergence of the urban rail transit has alleviated the problems of congestion and exhaust pollution caused by private vehicles in the city. Is study aims to determine which regression method is the most reliable in dealing with station coverage area overlapping issues, to explore if there exist significant differences between modeling CBGs versus CTs as spatial analysis units, and to provide insights into which model performs the best when modeling direct ridership at the station level. E section reviews the existing literature in contributing factors of transit ridership, measures to address the overlapping issues of station coverage area, and applications of various spatial analysis units and models. A conclusion of our study findings, merits, and limitations is presented
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
