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

Daisik Nam · Hyunmyung Kim · Kijung Ahn · R. Jayakrishnan

32 citations0/0 files storedTransportation Research Record Journal of the Transportation Research Board

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Transportation Planning and Optimization Urban Transport and Accessibility Transportation and Mobility Innovations

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.

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References

1997Travel demand and the 3Ds: Density, diversity, and design 1998Geographically Weighted Regression 2012Understanding intra-urban trip patterns from taxi trajectory data 2015Uncovering urban human mobility from large scale taxi GPS data 2015Spatial variation of the urban taxi ridership using GPS data 2013From taxi GPS traces to social and community dynamics 1998A network model of urban taxi services 2000A macroscopic taxi model for passenger demand, taxi utilization and level of services

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