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J Corey, Y Lao, Y Wu, Y Wang (2012)
Detection and correction of inductive loop detector sensitivity errors by using Gaussian Mixture ModelsTransp Res Rec J Transp Res Board, 2256
D Peña, FJ Prieto (2001)
Multivariate outlier detection and robust covariance matrix estimationTechnometrics, 43
A Lu, A Reddy (2012)
An algorithm to measure daily bus passenger miles using electronic farebox data for national transit database (NTD) section 15 reportingTransp Res Rec J Transp Res Board, 2216
PG Furth (1996)
Integration of fareboxes with otherTransp Res Rec J Transp Res Board, 1557
H Lee, B Coifman (2012)
Identifying and correcting pulse-breakup errors from freeway loop detectorsTransp Res Rec J Transp Res Board, 2256
Y Wu, G Zhang, Y Wang (2010)
Volume data correction for single-channel advance loop detectors at signalized intersectionsTransp Res Rec J Transp Res Board, 2160
J Hardin, DM Rocke (2005)
The distribution of robust distancesJ Comput Gr Stat, 14
M-P Pelletier, M Trépanier, C Morency (2011)
Smart card data use in public transit: a literature reviewTransp Res Part C Emerg Technol, 19
S Robinson, B Narayanan, N Toh, F Pereira (2014)
Methods for pre-processing smartcard data to improve data qualityTransp Res Part C, 49
EM Knorr, RT Ng, V Tucakov (2000)
Distance-based outliers: algorithms and applicationsVLDB J Int J Very Large Data Bases, 8
S Lee, M Hickman (2014)
Trip purpose inference using automated fare collection dataPublic Transp, 6
X Ma, YJ Wu, Y Wang, F Chen, J Liu (2013)
Mining smartcard data for transit riders’ travel patternsTransp Res Part C Emerg Technol, 36
DS Navick, PG Furth (2007)
Estimating passenger miles, origin-destination patterns, and loads with location-stamped farebox dataTransp Res Rec J Transp Res Board, 1799
Transit agencies require a constant stream of operations performance data to support standard planning, scheduling and operations management activities. Ridership and revenue statistics play a critical role in strategic system design, policy development, and budgeting decisions at all levels of transit management. Many agencies rely on electronic fare collection devices as a primary source for ridership and revenue data. The quality of this data will greatly affect transit-related reporting and decision making. This study proposes a systematic, data-driven approach to process revenue and ridership data pulled off electronic farebox equipment installed on a bus fleet operating in the St. Louis region. Three major farebox data errors are identified and impacts of these data errors are further evaluated and discussed at the system and trip level. Results indicate ridership and revenue may be overestimated by up to 8.05 and 9.95 %, respectively, due to farebox data errors. The results of this development effort offer a range of low-cost error identification and processing techniques that transit staff could easily and quickly implement. Even though the St. Louis Metro Transit data was used for analysis, these proposed approaches can be considered as a general framework and used by other transit agencies.
Public Transport – Springer Journals
Published: Jun 17, 2015
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