Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 745–748, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-745-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 745–748, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-745-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

EXPLORING DRIVING FACTORS OF HIGHER PAID TAXI TRIPS USING ORIGIN–DESTINATION GPS DATA (CASE STUDY: GREEN TAXIS OF NEW YORK CITY)

P. Mojtabaee, M. Molavi, and M. Taleai P. Mojtabaee et al.
  • Faculty of Geomatics, K. N. Toosi University of Technology, Tehran, Iran

Keywords: GPS Trip Data, NYC Green Taxi, Spatial Patterns, Cost and Fare, Driving Factors, Socioeconomic

Abstract. Investigating the influential factors of the areas where people use taxis is a crucial step in understanding the taxi demand dynamics. In this study, we intend to analyze higher-paying taxi trips by putting forward an approach to explore a dataset of green taxi trips in New York City in January 2015 together with some demographic, housing, social and economic data. The final goal is to find out whether the chosen factors are statistically significant to be considered as potential driving forces of demand location for trips with a higher-paid fare. Since airports are major attracting sources for taxi travels, all the steps are taken separately for three scenarios that the trip drop-offs are in 1) LaGuardia Airport, 2) John F Kennedy Airport or 3) other areas. First, the spatial pick-up distribution of these higher-paying trips is mapped to enable visual comparison of the urban movement patterns. Then, taking into account the pick-up density as the response variable, the densities of: foreign-born’s population, number of houses with no vehicles, the private wage and salary workers’ population, the government workers’ population and the self-employed workers’ population in own not incorporate business were considered as the explanatory variables. These variables were examined to find important factors affecting the demand in each neighborhood and different results in each of the three scenarios were discussed. This study gives a better insight into discovering driving factors of higher-paid taxi trips when considering airports as destinations which attract travels with potentially different characteristics.