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빅 데이터를 이용한 승용-버스 간의 이동성 특성 분석: 5개 광역시를 중심으로

Analysis of Mobility Characteristics between Passenger Cars and Buses Using Big Data: Focusing on Five Metropolitan Cities

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초록

본 연구에서는 5대 광역권의 대중교통 이동성 수준을 평가하기 위해, 교통카드와 내비게이션 데이터 등 교통 빅데이터를 활용하여 개인교통수단 대비 노선버스의 이동성을 권역별로 측정하고 실증하였다. 분석 결과, ‘승용차 통행시간’에 대한 ‘버스 통행시간’의 상대비율은 1.20~ 2.42배 수준으로 나타났다. 5대 광역시 중 승용차 대비 버스 통행시간 차이가 가장 낮은 광역시는 대구(1.20배)이며, 그 차이가 가장 큰 광역시는 울산(2.42배)으로 분석되었다. 추가적으로 각권역별로 승용차·버스 간의 통행속도 상대지표에 대한 첨두·비첨두 집단간 통계검증을 수행하였으며, 시간대에 대한 대중교통 이동성 지표 차이도 유의한 수준으로 분석되었다. 본 연구를통해 대도시권별 노선버스 모빌리티 특성을 기반으로 분석된 대중교통 이동성 지표는 교통수요예측 과정에서의 버스 특성을 현실적으로 모사하기 위한 수단선택모형 기초자료 및 대중교통 정책 의사결정 등에 활용될 수 있을 것으로 기대된다.

Abstract

This study used big data on transportation, including transit cards and navigation data, to measure and empirically analyze the mobility of public buses compared to personal vehicles across five major metropolitan areas. The analysis showed that the ratio of ‘bus travel time' to ‘private car travel time' ranged from 1.20 to 2.42 times. Among the five metropolitan cities, Daegu showed the smallest difference in bus travel time compared to private car travel time (1.20 times), while Ulsan showed the largest difference (2.42 times). In addition, statistical tests were conducted for peak and off-peak groups concerning the relative indicators of car and bus travel speeds for each region, and significant differences in bus mobility measures according to the time of day were analyzed. The bus mobility measures analyzed based on the characteristics of public urban buses in large metropolitan areas are expected to be used as fundamental data for mode choice models to analyze bus characteristics realistically in transportation demand forecasting processes and for decision-making in public transportation policies.

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