Development of a Data-Driven Platform for Transit Performance Measures Using Smart Card and GPS Data
Publication: Journal of Transportation Engineering
Volume 140, Issue 12
Abstract
To improve customer satisfaction and reduce operation costs, transit authorities have been striving to monitor transit service quality and identify the key factors to enhance it. The recent advent of passive data collection technologies, e.g., automated fare collection (AFC) and automated vehicle location (AVL), has shifted a data-poor environment to a data-rich environment and offered opportunities for transit agencies to conduct comprehensive transit system performance measures. However, most AFC and AVL systems are not designed for transit performance measures, implying that additional data processing and visualization procedures are needed to improve both data usability and accessibility. This study attempts to develop a data-driven platform for online transit performance monitoring. The primary data sources come from the AFC and AVL systems in Beijing, where a passenger’s boarding stop (origin) and alighting stop (destination) on a flat-rate bus are not recorded. The individual transit rider’s origin and destination can be estimated by utilizing a series of data-mining techniques, which are then incorporated into a regional-map platform for transit performance measures. A multilevel framework is proposed to calculate the network-level speed, route-level travel time reliability, stop-level ridership, and headway variance. These statistics are interactively displayed on a map through a simplified transit GIS data model. This platform not only serves as a data-rich visualization platform to monitor transit network performance for planning and operations, it also intends to take advantage of e-science initiative for data-driven transportation research and applications.
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Acknowledgments
The authors are grateful for the funding support from China Postdoctoral Science Foundation (2013M530018), Beijing Postdoctoral Research Foundation and the Fundamental Research Funds for the Central Universities. All data used for this study were provided by Beijing Transportation Research Center (BTRC). We are grateful to BTRC for their data support.
References
Abkowitz, M., and Violette, S. (1985). “Performance measures for New York state intercity buses.” J. Transp. Eng., 521–530.
ArcGIS 10 [Computer software]. Environmental Systems Research Institute (ESRI), Redlands, CA.
Beijing Transportation Research Center. (2012). “The 4th comprehensive transport survey summary report.” Beijing, China.
Bertini, R. L., and El-Geneidy, A. (2003). “Generating transit performance measures with archived data.” Transportation Research Record 1841, Transportation Research Board, Washington, DC, 109–119.
Chapleau, R., and Chu, K. K., and Allard, B. (2011). “Synthesizing AFC, APC, GPS and GIS data to generate performance and travel demand indicators for public transit.” 90th Annual Meeting of the Transportation Research Board (CD-ROM), Washington, DC.
Curries, G., and Mesbah, M. (2011). “Exploring transit operations performance at a network level using AVL and new GIS visualization methods.” 90th Annual Meeting of the Transportation Research Board (CD-ROM), Washington, DC.
Devillaine, F., Munizaga, M., and Trépanier, M. (2012). “Detection of activities of public transport users by analyzing smart card data.” Transportation Research Record 2276, Transportation Research Board, Washington, DC, 48–55.
ESRI. (1998). “ESRI shapefile technical description: An ESRI while paper.” 〈http://www.esri.com/library/whitepapers/pdfs/shapefile.pdf〉 (Apr. 18, 2013).
Federal Highway Administration (FHWA). (2002). “2002 status of the nation’s highways, bridges, and transit: Conditions and performance.” Washington, DC. 〈http://www.fhwa.dot.gov/policy/2002cpr/pdf/execsummary_book.pdf〉 (Jul. 28, 2012).
Federal Highway Administration (FHWA). (2006). “Travel time reliabiliy: Making it there on time, all the time.” Washington, DC. 〈http://ops.fhwa.dot.gov/publications/tt_reliability/〉 (Apr. 18, 2013).
Furth, P. G., Hemily, B., Muller, T. H. J., and Strathman, J. G. (2006). “Using archived AVL-APC data to improve transit performance and management.”, Transportation Research Board, Washington, DC.
Gallucci, G., and Allen, J. G. (2011). “Regional transit performance measures at Chicago’s regional transportation authority.” 90th Annual Meeting of the Transportation Research Board (CD-ROM), Washington, DC.
Google. (2012). “General transit feed specification reference.” Mountain View, CA, 〈https://developers.google.com/transit/gtfs/reference?csw=1〉 (Mar. 12, 2014).
International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC). (2011). “Information technology–database languages–SQL multimedia and application packages–part 3: spatial, ISO/IEC 13249-3.” Switzerland/French.
Kittelson and Associates, Inc.; Urbitran, LKC Consulting Services, Inc.; Morpace Int., Inc.; Queensland University of Technology; and Nakanishi, Y. (2003). “A guidebook for developing a transit performance-measurement system.”, Transportation Research Board, National Research Council, Washington, DC.
Koppelman, F. (1983). “Predicting transit ridership in response to transit service changes.” J. Transp. Eng., 548–564.
Liao, C., and Liu, H. (2010). “Development of data-processing framework for transit performance analysis.” Transportation Research Record 2143, Transportation Research Board, Washington, DC, 34–43.
Lin, Y., Yang, X., Zou, N., and Jia, L. (2013). “Real-time bus arrival time prediction: A case study for Jinan, China.” J. Transp. Eng., 1133–1140.
Lyman, K., and Bertini, R. L. (2008). “Using travel time reliability measures to improve regional transportation planning and operations.” Transportation Research Record 2046, Transportation Research Board, Washington, DC, 1–10.
Ma, X., McCormack, E., and Wang, Y. (2011a). “Process commercial GPS data to develop a web-based truck performance measures program.” Transportation Research Record 2246, Transportation Research Board, Washington, DC, 92–100.
Ma, X., Wang, Y., Feng, C.; and Liu, J. (2012). “Transit smart card data mining for passenger origin information extraction.” J. Zhejiang Univ. Sci. C, 13(10), 750–760.
Ma, X., Wu, Y., and Wang, Y. (2011b). “DRIVE net: An e-Science of transportation platform for data sharing, visualization, modeling, and analysis.” Transportation Research Record 2215, Transportation Research Board, Washington, DC, 37–49.
Ma, X., Wu, Y., Wang, Y., Chen, F., and Liu, J. (2013). “Mining smart card data for transit riders’ travel patterns.” J. Transp. Res. Part C Emerging Technol., 36(2013), 1–12.
Mohring, H., Schroeter, J., and Wiboonchutikula, P. (1987). “The value of waiting time, travel time, and a seat on a bus.” Rand J. Econ., 18(1), 40–56.
Munizaga, M. A., and Palma, C. (2012). “Estimation of a disaggregate multimodal public transport origin—Destination matrix from passive smartcard data from Santiago, Chile.” Transp. Res. Part C, 24(2012), 9–18.
OpenStreetMap Foundation. (2004). “OpenStreetMap.” 〈http://www.openstreetmap.org〉 (Jun. 5, 2014).
Pelletier, M.-P., Trépanier, M., and Morency, C. (2011). “Smart card data use in public transit: A literature review.” Transp. Res. Part C, 19(4), 557–568.
Peng, Z. R., and Kim, E. (2008). “A standard-based integration framework for distributed transit trip planning systems.” J. Intell. Transp. Syst., 12(1), 13–28.
Senevirante, P. (1990). “Analysis of on-time performance of bus services using simulation.” J. Transp. Eng., 517–531.
Simon, J., and Furth, P. (1985). “Generating a bus route O-D matrix from on-off data.” J. Transp. Eng., 583–593.
STARlab. (2013). “Transit performance measure system.” Seattle, WA, 〈http://www.uwdrive.net/TransitNet〉 (Jun. 5, 2014).
Sun, J., Peng, Z. R., Shan, X., Chen, W., and Zeng, X. (2011). “Development of web-based transit trip-planning system based on service-oriented architecture.” Transportation Research Record 2217, Transportation Research Board, Washington, DC, 87–94.
Texas A&M Transportation Institute. (2012). “2012 urban mobility report.” College Station, Texas, 〈http://d2dtl5nnlpfr0r.cloudfront.net/tti.tamu.edu/documents/mobility-report-2012.pdf〉 (Jul. 28, 2012).
Trépanier, M., Morency, C., and Agard, B. (2009). “Calculation of transit performance measures using smartcard data.” J. Public Transp., 12(1), 79–96.
Trépanier, M., Tranchant, N., and Chapleau, R. (2007). “Individual trip destination estimation in a transit smart card automated fare collection system.” J. Intell. Transp. Syst., 11(1), 1–14.
Vuchic, V. R. (2005). Urban transit: Operations, planning and economics, Wiley, New York.
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© 2014 American Society of Civil Engineers.
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Received: Aug 29, 2013
Accepted: May 2, 2014
Published online: Jul 14, 2014
Published in print: Dec 1, 2014
Discussion open until: Dec 14, 2014
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