Exploring the multiscale relationship between built environment and the metro station ridership: a case study of Bangkok mass rapid system | |
| Author | Achira Karawapong |
| Call Number | AIT Thesis no.TE-21-05 |
| Subject(s) | Subway stations--Thailand--Bangkok Transportation--Thailand--Bangkok--Planning |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Transportation Engineering |
| Publisher | Asian Institute of Technology |
| Abstract | A lot of big cities set a metro system as a main transportation mode to increase passenger mobility and decrease a severe level of traffic congestion. To achieve efficiency, the metro stations should be set in a master plan for the first priority of transportation mode. To obtain a precise amount of passenger volume transit ridership modeling which is the important method in the transportation planning and traffic management. Implementation of direct demand models (DDMs) at the metro station level are beneficial in capturing the relationship between influential factors of transit-oriented development principle and metro ridership. In this study, Multiscale Geographically Weighted Regression (MGWR) is conducted to deal the issue of the spatial autocorrelation which found in Ordinary Least Squares (OLS) regression and previous version Geographically Weighted Regression (GWR) in terms of bandwidth of each variable that examines both local and global factors based on the analysis of Bangkok metro stations. Moreover, the MGWR model provides better goodness of fit considering Residual Sum Squared, R-Squared, and AIC than other models. This study aims to explain the effect of explanatory factors on MRT ridership, which include land-use-related factors, intermodal transportation attributes, and the network structure including centrality analysis. Furthermore, this study provides an understanding of influencing factors to each station by using heatmap data visualization and the functional characteristic of each station in regions based on K-mean clustering analysis to rectifying the land use plan. These findings also can be use a as reference for consideration of increasing a number of metro ridership. |
| Year | 2022 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Civil and Infrastucture Engineering (DCIE) |
| Academic Program/FoS | Transportation Engineering (TE) |
| Chairperson(s) | Kunnawee Kanitpong; |
| Examination Committee(s) | Ampol Karoonsoontawong;Santoso, Djoen San; |
| Scholarship Donor(s) | Transport Engineering Scholarship Fund;AIT Scholarships; |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2022 |