Analysis of Grid Cell–Based Taxi Ridership with Large-Scale GPS Data

Abstract

Understanding the spatial variation of taxi ridership is of critical importance to many government agencies and taxi companies because taxis’ location dependency on spatial pattern of passenger demand results in spatially unbalanced taxi demand and supply. This study presents an analysis of the spatial distribution of taxi ridership by using large-scale GPS taxi trip data collected from Seoul, South Korea. To capture the spatial variations better in taxi ridership, GPS entities were disaggregated into units of a uniform size with a grid cell decomposition method. A geographically weighted spatial regression was applied to model spatial correlations of factors associated with transit and urban density to taxi ridership. Results from the proposed method demonstrated a higher relationship between taxi and subway ridership in the regions where lower accessibility to subway stations existed. In these regions, taxis were found to perform as a complementary mode to subway. In residential and commercial districts, this analysis showed that population and employment were highly related to taxi ridership. In contrast, in central business districts it was the building area (floor space), rather than population and employment, that was highly related to taxi ridership.

The Potential of Demand-Responsive Transport as a Complement to Public Transport: An Assessment Framework and an Empirical Evaluation

“…Future studies could include these aspects in their evaluation by incorporating travel survey data. Also, further research is needed to analyze how urban development characteristics and transit-related factors influence DRT usage, as already researched for taxi ridership ( 31 ).…”

Carsharing Trip Characteristic Analysis: Do Users Choose Carsharing Rather Than Taxi to Economize?

“…Certainly, taxi GPS data is also used to extract travel information ( 17 , 18 ), and reveal the operation conditions of taxi services ( 19 , 20 ). However, due to the large number of taxi vehicles, also human daily life mobility and activity information can be reflected by the passenger flow of taxis ( 21 ). In addition, taxi GPS data can be used to analyze, and provide advice about more efficient routes to taxi drivers ( 22 ).…”

Measuring Taxi Accessibility Using Grid-Based Method with Trajectory Data

“…A novel integrated access measure was introduced to compute the accessibility to points of interest (POIs), which was able to capture the temporal dynamics by taking into account the speed variability using floating car data during peak and off-peak hours on weekdays and weekends [20]. Furthermore, a geographically weighted spatial regression model was applied to find that a higher relationship between taxi and metro ridership in the regions where lower accessibility to metro stations existed [21]. Furthermore, a strong link between demand for taxi, land use patterns, and accessibility to other modes is found [22].…”