Abstract
As a sustainable mode of transportation, subways bring great convenience to the society. Although there have been many studies examining the relationship between the built environment and the station-level ridership, those studies focused mainly on the ridership, which is defined as the number of trips for each station. While ridership is an important indicator for evaluating subway demand, passenger-distance is another critical indicator that incorporates distance into demand evaluation, which has not yet been fully explored. To fill this gap, this paper investigates the relationship between the built environment around stations and the station-level passenger-distance (SLPD). As noted in previous studies, the relationship between the built environment and travel demand can vary by space. Therefore, a geographically weighted regression (GWR) model and a mixed geographically weighted regression (MGWR) model have been used to explore this spatially varying relationship using Chengdu, China, as an example case. The results were compared with that of an ordinary least squares (OLS) model. The comparison shows that the MGWR model that considers both global and local variables has the best goodness of fit. Results also show that 11 of the 25 potential variables are significantly related to SLPD. The accessibility of the station, station type, such as transfer or terminal, number of bus stops, number of restaurants, density of building area, density of the national road network, and density of the provincial road network, all have a positive correlation with SLPD. Meanwhile, the variables, whether it is a newly opened subway station, density of living points of interest (POIs), and density of railroad network, are all negatively correlated with SLPD. Ten of the eleven significant variables (except accessibility) have spatially varying relationships with SLPD. These findings can serve a useful reference for transportation planners for the demand evaluation.
Highlights
With urbanization, a growing number of cars are occupying the roadways, which brings along a series of problems, such as vehicular traffic congestion [1], air pollution [2, 3], and fuel consumption [4]
To study the spatial heterogeneity of ridership at the subway station level, three models were developed for analysis and comparison, namely, the ordinary least squares (OLS) model, the geographically weighted regression (GWR) model, and the mixed geographically weighted regression (MGWR) model
Our results show that MGWR model has smaller Akaike Information Criterion (AIC), AICc, and CV values than OLS and GWR models, while the goodness-of-fit (i.e., R2) is greater
Summary
A growing number of cars are occupying the roadways, which brings along a series of problems, such as vehicular traffic congestion [1], air pollution [2, 3], and fuel consumption [4]. Erefore, this may lead to the partial conclusion that when two stations have the same ridership, the station with longer passenger-distance can be regarded as the station with higher demand To address this problem, SLPD has been taken as the dependent variable against the built environment. Chen et al [33] used daily ridership as the dependent variable in their study of daytime patterns of transit riders of the New York City subway system. This response variable could not reflect passenger travel distance. Erefore, it would be worthwhile contribution to literature to explore the spatially varying impact of the built environment on the subway station demand
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