[논문]Bus arrival time calculation model based on smart card data

Zhou, Yuyang; Yao, Lin; Chen, Yanyan; Gong, Yi; Lai, Jianhui

doi:10.1016/j.trc.2016.11.014

Bus arrival time calculation model based on smart card data

Transportation research. Part C, Emerging technologies, v.74, 2017년, pp.81 - 96

Zhou, Yuyang (Corresponding author.) , Yao, Lin , Chen, Yanyan , Gong, Yi , Lai, Jianhui

Abstract ▼ AI-Helper

Abstract Bus arrival time is usually estimated using the boarding time of the first passenger at each station. However, boarding time data are not recorded in certain double-ticket smart card systems. As many passengers usually swipe the card much before their alighting, the first or the average alighting time cannot represent the actual bus arrival time, either. This lack of data creates difficulties in correcting bus arrival times. This paper focused on developing a model to calculate bus arrival time that combined the alighting swiping time from smart card data with the actual bus arrival time by the manual survey data. The model was built on the basis of the frequency distribution and the regression analysis. The swiping time distribution, the occupancy and the seating capacity were considered as the key factors in creating a method to calculate bus arrival times. With 1011 groups of smart card data and 360 corresponding records from a manual survey of bus arrival times, the research data were divided into two parts stochastically, a training set and a test set. The training set was used for the parameter determination, and the test set was used to verify the model’s precision. Furthermore, the regularity of the time differences between the bus arrival times and the card swiping times was analyzed using the “trend line” of the last swiping time distribution. Results from the test set achieved mean and standard error rate deviations of 0.6% and 3.8%, respectively. The proposed model established in this study can improve bus arrival time calculations and potentially support state prediction and service level evaluations for bus operations. Highlights Smart card and manual survey data are combined for bus arrival time calculation. The ridership versus the seating capacity affects the riders’ card swiping behaviors. The trend of time gap between the card swiping and bus arrival time is portrayed. Model validation shows only 0.6% and 3.8% of mean and standard error rate deviations.

주제어

참고문헌 (17)

Transp. Res. Part B Methodol. Berrebi 81 377 2015 10.1016/j.trb.2015.05.012 A real-time bus dispatching policy to minimize passenger wait on a high frequency route

상세보기
Transp. Res. Part C Emerg. Technol. Brakewood 53 59 2015 10.1016/j.trc.2015.01.021 The impact of real-time information on bus ridership in New York City

상세보기
Tourism Manage. Cantis 52 133 2016 10.1016/j.tourman.2015.06.018 Cruise passengers’ behavior at the destination: investigation using GPS technology

상세보기
Transp. Res. Part C Emerg. Technol. Carrion 35 9 305 2013 10.1016/j.trc.2012.10.010 Valuation of travel time reliability from a GPS-based experimental design

상세보기
J. Highway Transp. Res. Dev. Chen 29 05 102 2012 An approach on station ID and trade record match based on GPS and IC Card Data
Transp. Res. Part C Emerg. Technol. Cortes 19 4 695 2011 10.1016/j.trc.2010.12.008 Commercial bus speed diagnosis based on GPS-monitored data

상세보기
Transp. Res. Record, No.2063 Chu 63 2008 10.3141/2063-08 Enriching archived smart card transaction data for transit demand modeling

상세보기
Guo 2005 The 1st Conference of China Intelligent Transportation The method confirming the station of bus IC card passengers and its application
10.3141/2418-13 Hernandez, T, 2014. Flex-scheduling for bus arrival time prediction. Transportation Research Board 93st Annual Meeting.

상세보기
Jin Young Park, Dong Jun Kim, 2008. The potential of using smart card data to define the use of public transit in Seoul. CD-ROM. Transportation Research Board 87st Annual Meeting.
Transp. Res. Part C: Emerg. Technol. Kusakabe 46 179 2014 10.1016/j.trc.2014.05.012 Behavioral data mining of transit smart card data: A data fusion approach

상세보기
Niu 2010 Research on Bus Arrival Time Prediction
Acta Geographica Sinica Long 67 10 1339 2012 Identifying commuting pattern of Beijing using bus smart card data
Shi Xinyi, Lin Hangfei, 2014. The analysis of bus commuters’ travel characteristics using smart card data: the case of Shenzhen, China. Transportation Research Board 93st Annual Meeting.
Transp. Res. Part A Policy Practice Sun 69 C 447 2014 10.1016/j.tra.2014.09.007 Models of bus boarding and alighting dynamics

상세보기
Transp. Res. Part C Emerg. Technol. Tang 22 5 146 2012 10.1016/j.trc.2012.01.001 Ridership effects of real-time bus information system: a case study in the City of Chicago

상세보기
Transp. Res. Part C: Emerg. Technol. Tirachini 30 5 239 2013 10.1016/j.trc.2011.11.007 Estimation of travel time and the benefits of upgrading the fare payment technology in urban bus services

상세보기

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증

Bus arrival time calculation model based on smart card data

Abstract ▼ AI-Helper

주제어

참고문헌 (17)

이 논문을 인용한 문헌

관련 콘텐츠

원문 URL 링크

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트