[논문]공간 상관성을 고려한 서울시 택시통행의 영향요인 분석

이향숙; 김지윤; 추상호; 장진영; 최성택

doi:10.12815/kits.2019.18.5.64

공간 상관성을 고려한 서울시 택시통행의 영향요인 분석
Identifying Key Factors to Affect Taxi Travel Considering Spatial Dependence: A Case Study for Seoul 원문보기

韓國ITS學會論文誌 = The journal of the Korea Institute of Intelligent Transportation Systems, v.18 no.5, 2019년, pp.64 - 78

이향숙 (인천대학교 동북아물류대학원) , 김지윤 (홍익대학교 도시계획과) , 추상호 (홍익대학교 건설도시공학부) , 장진영 (홍익대학교 도시계획과) , 최성택 (조지아공과대학교 토목.환경공학과)

초록
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본 논문은 공간 상관성을 고려하여 서울의 택시통행에 영향을 미치는 요인을 분석한 것으로 택시의 GPS 자료를 이용하였다. 먼저 이들 자료를 이용하여 택시통행의 평균 통행시간, 평균 통행거리, 시공간적 분포 등의 통행특성을 분석하였다. 분석결과, 상당수의 택시통행이 평일 오전 첨두(8-9시)와 심야(0-1시) 시간대에 집중하는 것으로 나타났으며, 평균 통행거리와 통행시간은 각각 5.9km와 13분으로 나타났다. 이는 택시가 주로 단거리 통행수단이나 심야에 대중교통의 대체수단으로 활용되고 있음을 보여주고 있다. 다음으로 Moran's I 검정을 통해 교통존 기반의 택시통행들이 공간적으로 상관성이 있음을 규명하였다. 따라서 이를 고려한 택시통행에 대한 공간회귀모형(공간시차모형과 공간오차모형)을 추정하였으며, 인구사회 변수(가구수, 고령자수, 여성인구비, 자동차대수 등), 교통서비스 변수(지하철역수, 버스 정류장수 등), 토지이용 변수(인구밀도, 고용밀도, 주거면적 등) 등을 독립변수로 고려하였다. 모형분석 결과 이들 변수들이 통계적으로 유의하게 택시통행에 영향을 미치는 것으로 나타났다.

Abstract ▼ AI-Helper

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.

주제어

AI 본문요약
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문제 정의

This paper explores temporal and spatial travel characteristics of taxis (particularly focusing on taxi departure trips) to identify determinants using taxi GPS data in Seoul collected from digital tachograph. The GPS device provides exclusive information including boarding and alighting time of passengers, distance, and location.

제안 방법

Based on that classification, we estimated two different regression models—a spatially lagged (SL) model and a spatial error (SE) model—using GeoDa 0.9, which is a free software used to conduct spatial data analysis and modeling.
Kim(2015) explored key factors influencing taxi travel in Seoul using the taxi GPS data in Seoul. He developed regression models for taxi departure trips on a weekday, incorporating socio-economic traits, land use characteristics, and public transit use variables. The model results showed that these variables significantly affected taxi travel.
(2011) addressed a few problems in existing urban planning approaches using the taxi GPS trajectories in Beijing, China. They analyzed the distribution of region pairs with salient traffic problems, stratified by the type of day(work days or non-work days), presenting the linking structure and correlation among the regions. Using empirical taxi GPS trace data in San Francisco, Hoque et al.
(2012) analyzed temporal variations of both pick-ups and drop-offs and their association with land use characteristics using the taxi GPS data in Shanghai. They categorized the study area into six traffic sub-areas based on an indicator showing how much the numbers of pick-ups and drop-offs are balanced and corresponding distinctive temporal patterns, land use types, and land use intensity. Kim(2015) explored key factors influencing taxi travel in Seoul using the taxi GPS data in Seoul.
We developed the spatial regression model to account for spatial dependency which may lead to a biased estimation result (we conjectured that TAZs are not spatially independent, demonstrating that association exists in Section 3), thereby determining what factors affect the amount of taxi trips, stratified by the day of the week (weekday, Saturday and Sunday). This approach can be distinguished from existing studies mostly adopting ordinary regression approaches to identify determinants (e.
We first explore taxi travel characteristics (average travel time, average travel distance, and spatial distribution of taxi trips) and conduct spatial dependence tests to examine whether taxi trips are spatially correlated at the traffic analysis zone(TAZ) level. We then develop two types of spatial regression models (spatial lagged and spatial error models) for taxi trips, incorporating socio-demographic traits, transportation services and land use characteristics as explanatory variables.

후속연구

However, we still call for additional data collection and studies to overcome two limitations: (1) the omission of information related to passenger characteristics and (2) exclusion of driver-owned taxi data. If these issues can be handle with in the near future, further empirical studies will provide a comprehensive understanding of future taxi travel demand.
This research may be likely to be of interest to scholars, public-sector planners and policy-makers, researchers and other transportation-related professionals because we provide a better understanding of taxi travel characteristics. However, we still call for additional data collection and studies to overcome two limitations: (1) the omission of information related to passenger characteristics and (2) exclusion of driver-owned taxi data.

참고문헌 (22)

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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
Zhan X., Hasan S., Ukkusuri S. V. and Kamga C.(2015), "Urban link travel time estimation using large-scale taxi data with partial information," Transportation Research Part C, vol. 33, pp.37-49.
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저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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