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Page 61
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Public Transit Rider Origin–Destination Survey Methods and Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25428.
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Page 62
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Public Transit Rider Origin–Destination Survey Methods and Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25428.
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Page 62
Page 63
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Public Transit Rider Origin–Destination Survey Methods and Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25428.
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Page 63
Page 64
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Public Transit Rider Origin–Destination Survey Methods and Technologies. Washington, DC: The National Academies Press. doi: 10.17226/25428.
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Page 64

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

61 AECOM. Transit Survey White Paper. FSUTMSonline. Florida Department of Transportation Forecasting and Travel Trends Office. 2009. Available: http://www.fsutmsonline.net/images/uploads/reports/Transit_ Survey_White_Paper_v6_5-9-2011_edit.doc [accessed: December 2017]. Agrawal, A. W., S. Granger-Bevan, G. Newmark, and H. Nixon. Comparing Data Quality and Cost from Three Modes of On-Board Transit Passenger Surveys. Publication CA-MTI-15-1206. Mineta Transportation Insti- tute, San Jose, CA, 2015. Baltes, M. R. Customer Surveying for Public Transit: A Design Manual for Customer On-Board Surveys. Publication No. DTRS98-G-0032. National Center for Transit Research, Tampa, FL, 2002. Beatty, P., and D. Herrmann. To answer or not to answer: Decision processes related to survey item nonresponse. Pp. 71-86 in Survey Nonresponse, R. M. Groves, D. A. Dillman, J. L. Eltinge, and R. J. Little, eds., John Wiley & Sons, New York, 2002. Cambridge Systematics (CS), 1-Click | CS Software, Medford, MA. Available: http://camsys.software/products/ 1-click. Cherrington, L. K. Recommended Practices for Transit Onboard Surveys. Presented at 86th Annual Meeting of the Transportation Research Board, Washington, DC, Report No. 07-1908, 2007. Cervenka, K. Telephone interview with A. Zalewski, June 13, 2018. COTA (Central Ohio Transit Authority). 2013 On-Board Transit Survey. Columbus, 2014. Available: https:// www.cota.com/wp-content/themes/gotravel-child/images/upload/solicitations%20files/2017/09/25/ Exhibit%20A%2003_31_2014_COTA_2013_OnBoardSurvey_DraftReport.pdf. Cummins, B., G. Spitz, T. O’Malley, and M. Campbell. How Close is Close Enough? Statistical Equivalence of Onboard Versus Online Surveys of Transit Customers. Transportation Research Record: Journal of the Trans- portation Research Board, No. 2351, Transportation Research Board of the National Academies, Washington, D.C., 2013, pp. 23–29. CUTR (Center for Urban Transportation Research). GTFS-flex. University of South Florida, Tampa, 1996. Available: https://github.com/CUTR-at-USF/gtfs-flex [accessed Feb. 5 2018]. DemandTrans Solutions. Development of Transactional Data Specifications for Demand-Responsive Transpor- tation. TCRP G-16. Transportation Research Board of the National Academies, Washington, D.C., 2018. Available: http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4120 [accessed Feb. 5, 2018]. Dong, B., B. McHugh, and V. Shank. Multiple correspondence with A. Zalewski and A. Cohen April 2019. Dumas, R. Analyzing Transit Equity Using Automatically Collected Data. Chapter 2 in Origin, Destination, and Interchange Inference at the MBTA. Master’s thesis, Massachusetts Institute of Technology, 2017. ETC Institute, Valley Metro 2010-11 Transit On-Board Survey Final Report, Valley Metro Regional Public Transit Authority, December 2011. Available: https://www.valleymetro.org/sites/default/files/legacy-images/ uploads/projects/2010-2011_Transit_On-Board_Survey_Final_Report.pdf. FTA (Federal Transit Administration). Title VI Requirements and Guidelines for Federal Transit Administra- tion Recipients. FTA Circular 4702.1B. Washington, D.C., 2012. Available: https://www.transit.dot.gov/ regulations-and-guidance/fta-circulars/title-vi-requirements-and-guidelines-federal-transit. FTA (Federal Transit Administration). Overview of Stops. Washington D.C., 2013. FTA (Federal Transit Administration). Americans with Disabilities Act (ADA): Guidance. FTA Circu- lar 4710.1. Washington, D.C., 2015. Available: https://www.transit.dot.gov/sites/fta.dot.gov/files/docs/ Final_FTA_ADA_Circular_C_4710.1.pdf. FTA (Federal Transit Administration). National Transit Database: Chatham Area Transit 2016 Annual Agency Profile, 2016a. Available: https://www.transit.dot.gov/sites/fta.dot.gov/files/transit_agency_profile_doc/ 2016/40025.pdf. References

62 Public Transit Rider Origin–Destination Survey Methods and Technologies FTA (Federal Transit Administration). National Transit Database: Massachusetts Bay Transportation Authority 2016 Annual Agency Profile, 2016b. Available: https://www.transit.dot.gov/sites/fta.dot.gov/files/transit_ agency_profile_doc/2016/10003.pdf. FTA (Federal Transit Administration). National Transit Database (NTD): 2016 Metrics, 2016c. Available: https://www.transit.dot.gov/ntd/data-product/2016-metrics. Gordon, J., H. Koutsopoulos, N. Wilson, and J. Attanucci. Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 2343, Transportation Research Board of the National Academies, Washington, D.C., 2013, pp. 17–24. Hunsinger, E. Iterative Proportional Fitting for a Two-Dimensional Table. Presentation at University of California, Berkeley, Department of Demography, May 2008. 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In Transportation Research Record: Journal of the Transportation Research Board, No. 2643, Transportation Research Board of the National Academies, Washington, D.C., 2017, pp. 19–27. Memarian, B., H. Jeong, and D. Uhm. Effects of Survey Techniques on On-Board Survey Performance. Transport Policy, Vol. 21, 2012, pp. 52–62. Moore, J. C. The Effects of Questionnaire Design Changes on Asset Income Amount Nonresponse in Waves 1 and 2 of the 2004 SIPP Panel. Survey Methodology, Vol. 1, 2006, pp. 1–19. Moore, J. C., L. L. Stinson, and E. Welniak. Income Reporting in Surveys: Cognitive Issues and Measurement Error. pp. 155–174 In Cognition and Survey Research, M. G. Sirkiin, D. J. Herrmann, S. Schecter, N. Schwarz, J. M. Tanur, and R. Tourangeau, eds., John Wiley, New York, 1999. Neff, J., and L. Pham, A Profile of Public Transportation Passenger Demographics and Travel Characteristics Reported in On-Board Surveys. American Public Transportation Association, Washington, D.C., 2007. 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References 63 Rahman, S., J. Wong, and C. Brakewood. Use of Mobile Ticketing Data to Estimate an Origin–Destination Matrix for New York City Ferry Service. In Transportation Research Record: Journal of the Transportation Research Board, No. 2544, Transportation Research Board of the National Academies, Washington, D.C., 2016, pp. 1–9. Richardson, A. J., E. S. Ampt, and A. H. Meyburg. Weighting and Expansion of Data. In Survey Methods for Transport Planning, Eucalyptus Press, Parkville, Australia, 1995. Schaller, B. TCRP Synthesis 63: On-Board and Intercept Transit Survey Techniques. Transportation Research Board of the National Academies, Washington D.C., 2005. Schmitt, D., On-Board Transit Rider Surveys: Synthesis of Practice, FSUTMSonline, Florida Department of Transportation Forecasting and Travel Trends Office, 2012. Available: http://www.fsutmsonline.net/images/ uploads/reports/On_Board_Survey_Synthesis.pdf [accessed December 2017]. Seaborn, C., J. Attanucci, and N. Wilson. Analyzing Multimodal Public Transport Journeys in London with Smart Card Fare Payment Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 2121, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 55–62. Silver, D., J. Seltzer, S. Hsieh, D. Seidl, and M. Rhindress. Do Seasons Really Matter in Transit Ridership Profiles and Activities? A Closer Look at Seasonal Differences in Travel Patterns from the Long Island Rail Road Origin and Destination Survey. Presented at 95th Annual Meeting of the Transportation Research Board, No. 16-3935, 2016. Singer, E., H. J. Hippler, and N. Schwarz. Confidentiality Assurances in Surveys: Reassurance or Threat? Inter- national Journal of Public Opinion Research, Vol. 4, No. 3, 1992, pp. 256–268. Sparks, G. Telephone interview with G. Macfarlane, March 29, 2018. Spitz, G., F. L. Niles, and T. J. Adler. TCRP Synthesis 69: Web-Based Survey Techniques. Transportation Research Board of the National Academies, Washington D.C., 2006. Tierney, K., S. Decker, K. Proussaloglou, T. Rossi, E. Ruiter, and N. McGuckin. Travel Survey Manual. No. FHWA- PL-96-029, 1996. Transit app. Montréal, Quebec. Available: https://transitapp.com [accessed Feb. 5, 2018]. TRB (Transportation Research Board). Expansion Factors for Transit Survey Responses. Research Needs Statement, Committee ABJ40, Travel Survey Methods, August 2007. Available: https://rns.trb.org/ dproject.asp?n=14119 [accessed January 2018]. Wang, W., J. Attanucci, and N. Wilson. Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection Systems. Journal of Public Transportation, Vol. 14, No. 4, 2011, pp. 131–150.

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TRB’s Transit Cooperative Research Program (TCRP) Synthesis 138: Public Transit Rider Origin–Destination Survey Methods and Technologies captures the state of the practice among agencies of different sizes, geographic locations, and modes and evaluates the opportunities for and challenges of conducting surveys in an era of emerging technologies.

The report presents the reality and complexity of conducting origin–destination surveys and will allow agencies to compare what they are currently doing with what others are doing, get ideas about what other strategies are possible, and make better decisions about surveying in the future.

The report includes case examples of five transit systems that present an in-depth analysis of various survey strategies and include two agencies that have leveraged passive data to complement or eliminate origin–destination surveys.

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