Benefits of Real-Time Transit Information and Impacts of Data Accuracy on Rider Experience

被引:40
作者
Gooze, Aaron [1 ]
Watkins, Kari Edison [2 ]
Borning, Alan [3 ]
机构
[1] Georgia Inst Technol, Sch Civil & Environm Engn, Atlanta, GA 30308 USA
[2] Georgia Inst Technol, Sch Civil & Environm Engn, Atlanta, GA 30332 USA
[3] Univ Washington, Dept Comp Sci & Engn, Seattle, WA 98195 USA
关键词
PREDICTION; SYSTEM;
D O I
10.3141/2351-11
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
When presented in a practical format, real-time transit information can improve sustainable travel methods by enhancing the transit experience. This paper identifies the positive shift realized by the continued development of the OneBusAway set of real-time transit information tools. In addition, the paper analyzes real-time prediction errors and their effects on the rider experience. In 2012, three years after the development of location-aware mobile applications, a survey of current OneBusAway users was conducted to compare the results with the previous 2009 study. Results show significant positive shifts in satisfaction with transit, perceptions of safety, and ridership frequency as a result of the increased use of real-time arrival information. However, this paper also provides a perspective of the margin of error riders come to expect and the negative effects resulting from inaccuracies with the real-time data. Although riders on average will ride less when they have experienced errors, a robust issue-reporting system as well as the resolution of the error can mitigate the initial negative effects. With this understanding, the paper provides transit agencies and developers with guidance to realize the full potential of real-time information and error-reporting systems.
引用
收藏
页码:95 / 103
页数:9
相关论文
共 18 条
[1]  
[Anonymous], 2010, KING COUNTY METRO 20
[2]  
BART, 2012, DEV RES
[3]   A prescription for transit arrival/departure prediction using automatic vehicle location data [J].
Cathey, FW ;
Dailey, DJ .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2003, 11 (3-4) :241-264
[4]  
Chen D., 2012, 91 ANN M TRANSP RES
[5]  
DAILEY DJ, 1999, P 6 ANN WORLD C INT
[6]  
Eboli Laura., 2007, J PUBLIC TRANSPORT, V10, P21, DOI DOI 10.5038/2375-0901.10.3.2
[7]  
Ferris B, 2010, CHI2010: PROCEEDINGS OF THE 28TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P1807
[8]   Bus arrival time prediction using artificial neural network model [J].
Jeong, R ;
Rilett, LR .
ITSC 2004: 7TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, PROCEEDINGS, 2004, :988-993
[9]   Error correction of arrival time prediction in real time bus information system [J].
Kim, Seungil ;
Lee, Chungwon ;
Kim, Youngchan ;
Lee, Seungjae ;
Park, Dongjoo .
JOURNAL OF ADVANCED TRANSPORTATION, 2010, 44 (01) :42-51
[10]   An Integrated Framework to Predict Bus Travel Time and Its Variability Using Traffic Flow Data [J].
Mazloumi, Ehsan ;
Rose, Geoff ;
Currie, Graham ;
Sarvi, Majid .
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2011, 15 (02) :75-90