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http://dx.doi.org/10.12815/kits.2019.18.5.64

Identifying Key Factors to Affect Taxi Travel Considering Spatial Dependence: A Case Study for Seoul  

Lee, Hyangsook (Graduate School of Logistics, Incheon National University)
Kim, Ji yoon (Dept. of Urban Planning, Hongik University)
Choo, Sangho (Dept. of Urban Design & Planning, Hongik University)
Jang, Jin young (Dept. of Urban Planning, Hongik University)
Choi, Sung taek (School of Civil and Environmental Eng.. Georgia Institute of Technology)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.18, no.5, 2019 , pp. 64-78 More about this Journal
Abstract
This paper explores key factors affecting taxi travel using global positioning system(GPS) data in Seoul, Korea, considering spatial dependence. We first analyzed the travel characteristics of taxis such as average travel time, average travel distance, and spatial distribution of taxi trips according to the time of the day and the day of the week. As a result, it is found that the most taxi trips were generated during the morning peak time (8 a.m. to 9 a.m.) and after the midnight (until 1 a.m.) on weekdays. The average travel distance and travel time for taxi trips were 5.9 km and 13 minutes, respectively. This implies that taxis are mainly used for short-distance travel and as an alternative to public transit after midnight in a large city. In addition, we identified that taxi trips were spatially correlated at the traffic analysis zone(TAZ) level through the Moran's I test. Thus, spatial regression models (spatial-lagged and spatial-error models) for taxi trips were developed, accounting for socio-demographics (such as the number of households, the number of elderly people, female ratio to the total population, and the number of vehicles), transportation services (such as the number of subway stations and bus stops), and land-use characteristics (such as population density, employment density, and residential areas) as explanatory variables. The model results indicate that these variables are significantly associated with taxi trips.
Keywords
Taxi; Global Positioning System(GPS) data; Moran's I test; Spatial dependence; Spatial regression models;
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