Abstract
Many large cities rely on Mass Rapid Transit (MRT) to increase passenger mobility. For efficiency, MRT stations should be arranged to attract maximal number of travelers. It is therefore important to develop methods for estimating MRT ridership forecasting models, which are important for policies on land use development or new MRT lines. Direct ridership models (DRMs) at the station level are superior in estimating the benefits of transit-oriented development policies. In this paper, a principal component regression (PCR) is proposed to overcome the issue of multicollinearity that widely occurs in multivariate regression analyses for DRM modeling, especially the ordinary least squares regression. Based on the analysis of 72 MRT stations in Wuhan, China, four principal components are obtained to explain the potential linkage to MRT ridership, which include built-environment related factors, jobs-housing spatial structure related factors, station attributes, and the large compound. Nineteen significant determinants have been identified, among which the four factors of office building area, land use mix, the number of restaurants, and financial institutions are the most influential factors. Built-environment-related factors exert more significant impact on MRT ridership than others. The distance to city center and the number of bus lines around stations have negative association with MRT demand. The proposed PCR-based DRM provides insights for forecasting transit demand brought about by new metro lines and forecasting the consequences of land use development.
Highlights
Chinese planners and government agents are aware that Mass Rapid Transit (MRT) occupies an important place in urban transport systems
This paper aims to set up a framework for forecasting MRT ridership to explore the casual relationship between the determinants of station environments and transit ridership
The correlation test between candidate factors and ridership is used to identify the independent variable set for Direct ridership models (DRMs). ird, the principal component regression (PCR) method is applied to generate several principal components. ese principal components are cited from a professional viewpoint, which may reveal potential influencing links between explanatory variables and MRT ridership
Summary
Chinese planners and government agents are aware that Mass Rapid Transit (MRT) occupies an important place in urban transport systems. MRT, especially metro transit, has become increasingly popular in China. Especially in Wuhan, was open every year from 2012 to 2015 (Figure 1). 14 subway lines were being built at the same time in 2016. By the end of 2021, the total length of rail transit lines in Wuhan will reach 400 km. E metro transit is responsible for up to 25% trips of public transit in Wuhan, and the ratio kept increasing to 35% in July 2017, while it was 53% in Shanghai at the same time. MRT plays an important role in shaping travel modal structure and encouraging land development around stations. It is regarded as a major solution to transport problems in megacities
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