Analysis of Metro Station Ridership Considering Spatial Heterogeneity

Abstract

This study aims to explore the role of spatial heterogeneity in ridership analysis and examine the relationship between built environment, station attributes and urban rapid transit ridership at the station level. Although spatial heterogeneity has been widely acknowledged in spatial data analysis, it has been rarely considered in travel behavior studies. Four models (three global models-ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM) and one local model-geographically weighted regression (GWR) model) are estimated separately to explore the relationship between various independent variables and station ridership, and identify the influence of spatial heterogeneity. Using the data of built environment and station characteristics, the results of diagnostic identify evidence the existence of spatial heterogeneity in station ridership for the metro network in Nanjing, China. Results of comparing the various goodness-of-fit indicators show that, the GWR model yields the best fit of the data, performance followed by the SEM, SLM and OLS model. The results also demonstrate that population, number of lines, number of feeder buses, number of exits, road density and proportion residential area have a significant impact on station ridership. Moreover, the study pays special attention to the spatial variation in the coefficients of the independent variables and their statistical significance. It underlines the importance of taking spatial heterogeneity into account in the station ridership analysis and the decision-making in urban planning.

Examining the relationship between built environment and metro ridership at station-to-station level

“…For instance, using oneweek data for all stations in the Shenzhen metro and bus systems, Tu et al (2018) found that increased road density encourages a larger number of people to travel by bus and metro. Similar studies conducted by Zhao et al (2014) and Gan et al (2019b) also concluded that road density (or road length) is positively and significantly associated with urban rail transit station ridership. Durning and Townsend (2015) observed a positive relationship between rapid transit station ridership and intersection density based on data collected in Canada's five largest cities.…”

“…Distance to city center, a measure of regional accessibility, has also been examined in the literature. In two empirical studies pertaining to Nanjing, China, the effects of distance to city center on metro station ridership have been found insignificant (Zhao et al, 2014;Gan et al, 2019b). However, based on the daily boardings data of light rail transit for the Baltimore Metro collected, Maryland, distance to city center was found to have a negative effect on ridership.…”

“…Residential ratio, commercial ratio, and governmentinstitutional ratio were found to have significantly positive effects on ridership, while the impact of the open area ratio, park area ratio and resource-industrial ratio were insignificant. Some other studies examined the relationship between the proportion of different types of land use and urban rail transit ridership, yielding inconsistent and fragmented findings (e.g., Sohn and Shim,2010;Sung and Oh, 2011;Zhao et al, 2014;Tu et al, 2018;Gan et al, 2019b).…”

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

“…Hu et al [17] used the generalized additive mixed effects model (GAMM) to investigate the nonlinear relationship between determinants and the attractiveness of car sharing. Since spatial autocorrelation is always observed when dealing with spatial data, spatial econometric models are usually used to deal with this issue [15,33,34]. Gan et al [33] applied the spatial error model and found the factors that significantly influence station-level ridership while controlling for spatial autocorrelation.…”

Applying Multivariate Geostatistics for Transit Ridership Modeling at the Bus Stop Level

“…This limitation is overcome by the spatial regression models, which have already been used for travel demand forecasting (Gan et al 2019;Lopes, Brondino and Rodrigues da Silva 2014;Sarlas and Axhausen 2016;Wang 2001). These models can consider the spatial autocorrelation by means of an explanatory variable, obtained from a spatially lagged dependent variable, or by the residual term of the model, and both of them include a spatial weight matrix normally based on the distance between the points of the database (Fotheringham et al 2003).…”