공간 상관성을 고려한 서울시 택시통행의 영향요인 분석Identifying Key Factors to Affect Taxi Travel Considering Spatial Dependence: A Case Study for Seoul
- Other Titles
- Identifying Key Factors to Affect Taxi Travel Considering Spatial Dependence: A Case Study for Seoul
- Authors
- 이향숙; 김지윤; 추상호; 장진영; 최성택
- Issue Date
- 2019
- Publisher
- 한국ITS학회
- Keywords
- 택시; GPS; Moran’s I 검증; 공간 상관성; 공간회귀모형; Taxi; Global Positioning System(GPS) data; Moran’s I test; Spatial dependence; Spatial regression models
- Citation
- 한국ITS학회 논문지, v.18, no.5, pp.64 - 78
- Journal Title
- 한국ITS학회 논문지
- Volume
- 18
- Number
- 5
- Start Page
- 64
- End Page
- 78
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/2263
- DOI
- 10.12815/kits.2019.18.5.64
- ISSN
- 1738-0774
- 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.
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