ABSTRACT
Seoul and the surrounding metropolitan areas heavily rely on the mass public transit including subway and bus line. In case of the subway and railway, the construction requires astronomical burden in terms of the construction cost and time. ...
ABSTRACT
Seoul and the surrounding metropolitan areas heavily rely on the mass public transit including subway and bus line. In case of the subway and railway, the construction requires astronomical burden in terms of the construction cost and time. Further, once the rail line is constructed, it if virtually impossible to alter or relocate. On the contrary, bus service is easily adjusted to accommodate the changed demand.
Despite the flexibility of bus service, its relocation should overcome the following problems: first, Bus line rearrangement should consider the balance between the demand and the supply to enhance the transit equity among the users scattered around the area that Supply against demand imbalances. Second, The existing demand analysis is to crude since the demand was analysed based on TAZ, mainly based on the Dong unit. Utilization of the GIS-GWR and GIS-T data can resolve the problem.
In this paper, the limitation of the conventional transit demand analysis model is overcome by deploying the GWR model, which identifies the transit demand based on the geographic relation between the service location and those of the users. GWR model considers the spatial effect of the bus demand in accordance with the distance to the each bus stops. For this, BIS (Bus Information System) and SCD (Smart Card Data) of the area for one weekday are analysed in this study.
The population, total number of students, total number of employees, and the accumulated commercial area are used as the independent variables for the model. The analysis results shows that the total number of students and total number of employees are the two most important variable that affect to the bus demand. Based on this finding, the potential bus demand of the entire area was computed in an 100 meter by 100 meter spatial unit. This demand map was then superimposes with the existing bus route which identified the areas where the balance between demand and supply is severely skewed. Since the analysis was computed with numerical attribute data, e.g., numbers of passenger and service headway at every bus stops, the shortage and surplus of bus service of entire study area could computed. Further, based on this computational result and considering the entire bus service capacity data, the sample relocation computation of the bus service for Bus routes optimization from the oversupplied areas to the undersupplied area was illustrated. Thus this study clearly compares the benefits the GIS based bus demand analysis.