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.
4 Transit providers rely on rider surveys to collect a range of data, from customer demograph- ics to rider preferences and satisfaction. One of the most complex types of surveys conducted by transit providers are originâdestination (OD) studies. OD studies attempt to capture the trip patterns and characteristics of transit riders. Great effort is placed in ensuring that the results from these studies are statistically valid, because data from OD surveys play several critical func- tions: providing inputs for regional travel demand models, meeting federal reporting require- ments (notably around Title VI of the Civil Rights Act [FTA, 2012]), supporting transit service planning, and assessing the impacts of transit investments. To accurately conduct an OD survey, the organization administering the survey must field a randomized sample large enough to yield statistically valid results (or, alternatively, a 100 percent census of all riders). The typical rider survey approach is to distribute and collect paper surveys aboard buses and at transit stops. Transit providers and their partners have long struggled with gathering adequate samples, minimizing bias, and containing survey costs with this method. Over the last decade, new technologiesâsuch as handheld devices, high-speed cellular Internet, and automated data collection toolsâhave changed how organizations con- duct surveys. Moreover, the emergence of passive âbigâ data from fare cards, mobile phones, and GPS systems, provides new alternatives to traditional rider surveys. Note on Terminology This study frequently uses the terms on-board survey and rider survey to refer interchangeably to a randomized survey of transit riders where recruitment of participants occurs in public (e.g., on a bus, at a train station). On-board surveys refer specifically to rider surveys that are conducted on board a transit vehicle. Study Objectives This synthesis summarizes the current state of practice in transit rider surveys, most notably how emerging technologies and data sources are affecting the ways that these surveys are con- ducted. Although the synthesis includes examples from other types of surveys, the focus is on OD surveys; they represent the most complex and most costly type of survey regularly performed by a transit provider. This report strives to address several key questions: ⢠What is the relationship between methods of surveying and measures such as costs, response rates, and completion rates? ⢠How has the wide adoption of mobile devices such as tablets affected survey practices? C H A P T E R 1 Introduction
Introduction 5 ⢠How do surveyors avoid sampling bias and maximize survey response and completion rates? ⢠How are emerging methods and data sources being used to complement or supplant on-board, or rider, surveys and being incorporated in transit modeling? The precursor to this study was TCRP Synthesis 63: On-Board and Intercept Transit Survey Techniques (Schaller, 2005). That study contains valuable information on survey design and fielding methodologies. Instead of replicating the work found in TCRP Synthesis 63, this study focuses on describing new survey practices being adopted by the transit industry. The report strives to provide transit industry professionals a broad overview of new technologies and data techniques. It also outlines areas of further research. Organization of the Report The report is divided into five chapters: ⢠Chapter 1: Introduction: Background, overview, organization, and methodology of the report. ⢠Chapter 2: Literature Review: Summary of literature related to the existing survey prac- tice, including federal guidance and requirements; survey approach and instrument design; sampling plan strategies, assessing survey data quality, and survey response expansion; and overview of passive data acquisition methodologies used to gather OD information. ⢠Chapter 3: Current State of Practice: Results of a national survey summarizing survey prac- tices among 57 transit providers and regional planning organizations. ⢠Chapter 4: Case Examples: Five case studies highlighting various survey strategies, includ- ing two agencies that have leveraged passive data to complement or eliminate OD surveys. ⢠Chapter 5: Conclusion: Summary of key findings and areas for further research. ⢠Appendices: Study questionnaire and sample rider survey instruments as submitted by respondents. Full collection of tables summarizing synthesis questionnaire results. Methodology This report relies primarily on three sources of information: existing literature, a survey of organizations responsible for conducting transit surveys, and interviews with representatives from four transit providers and one metropolitan planning organization (MPO). Literature Review The literature review began with a broad search of existing TCRP reports, white papers, academic research, documentation from prior survey efforts, and federal guidance on the following topics: ⢠Federal guidance and requirements related to transit surveys, ⢠Survey approach and instrument design, ⢠Survey sampling plan development, ⢠Survey data quality assessment and management, ⢠Survey expansion methods, and ⢠Passive data. The literature review is intended to provide the reader with a high-level overview of existing survey practices, including transit professionals with no prior experience in survey design or implementation.
6 Public Transit Rider OriginâDestination Survey Methods and Technologies Current State of Practice Survey To better understand the current state of practice in OD studies, an online questionnaire was distributed to 67 organizations responsible for overseeing such studies, including transit providers and regional planning organizations. The questionnaire covered a variety of topics, from the frequency of and justification for OD studies to how new technologies and data sources are integrated into survey efforts (see Appendix A for a copy of the questionnaire). The 67 participants represent a diverse range of transit systems by size, location, and mode. Fifty-seven organizations responded at least partially to the survey (see Table 1 and Figure 1), resulting in an 85 percent response rate (see Table 2). The results of the questionnaire are summarized and discussed in Chapter 3 of this report. Transit Agency Size Definion (annual unlinked trips) Number of Agencies Contacted Number of Responses Received Very large >100 million 13 12 Large 30 to 100 million 13 11 Medium 10 to 30 million 15 13 Small <10 million 26 21 Total 67 57 Response rate 85% Source: National Transit Database, 2016 (FTA, 2016c). Table 1. Survey sample size and response rate. Figure 1. Map of survey respondents.
Introduction 7 # Organization City, State Size 1 Alameda-Contra Costa Transitâdba AC Transit Oakland, CA Large 2 Ames Transit Agency (ATA)âdba CyRide Ames, IA Small 3 Atlanta Regional Commission (ARC) Atlanta, GA Very large 4 Battle Creek Transit (BCT) Battle Creek, MI Small 5 Berks Area Regional Transportation Authority (BARTA) Reading, PA Small 6 Blacksburg Transit (BT) Blacksburg, VA Small 7 Capital Metropolitan Transportation Authorityâdba CapMetro Austin, TX Large 8 Central Florida Regional Transportation Authorityâdba LYNX Orlando, FL Medium 9 Central Ohio Transit Authority (COTA) Columbus, OH Medium 10 Central Oklahoma Transportation and Parking Authority (COTPA)âdba EMBARK Oklahoma City, OK Small 11 Central Puget Sound Regional Transit Authority (CPSRTA)âdba Sound Transit Seattle, WA Large 12 Central Transportation Planning Staff (CTPS) Boston, MA Very large 13 Champaign-Urbana Mass Transit Districtâdba MTD Champaign, IL Medium 14 Charleston Area Regional Transportation Authority (CARTA) Charleston, SC Small 15 Charlotte Area Transit System (CATS) Charlotte, NC Medium 16 Charlottesville Area Transit (CAT) Charlottesville, VA Small 17 Chatham Area Transit Authority (CATA)âdba CAT Savannah, GA Small 18 Chattanooga-Hamilton County Regional Planning Agency (CHCRPA)â dba RPA Chattanooga, TN Small 19 Chicago Transit Authority (CTA) Chicago, IL Very large 20 Chittenden County Transportation Authorityâdba Green Mountain Transit Burlington, VT Small 21 City of Brownsvilleâdba Brownsville Metro Brownsville, TX Small 22 City of Fargoâdba Metropolitan Area Transit Fargo, ND Small 23 City of Phoenix Public Transit Departmentâdba Valley Metro Phoenix, AZ Large 24 Denver Regional Transportation Districtâdba RTD Denver, CO Very large 25 East-West Gateway Council of Governments (EWGCOG) St. Louis, MO Large 26 Gainesville Regional Transit Systemâdba RTS Gainesville, FL Small 27 Greater Buffalo Niagara Regional Transportation Council (GBNRTC) Buffalo, NY Medium 28 Greater Mankato Transit System (GMTS)â dba City Bus Mankato, MN Small 29 Indianapolis and Marion County Public Transportationâdba IndyGo Indianapolis, IN Small 30 Kansas City Area Transportation Authority (KCATA) Kansas City, MO Medium 31 King County Department of Transportationâdba King County Metro Seattle, WA Very large 32 Lane Transit District (LTD) Eugene, OR Medium 33 Los Angeles County Metropolitan Transportation Authority (Metro) Los Angeles, CA Very large 34 Madison Area Transportation Planning Board (MATPB) Madison, WI Very large 35 Maryland Transit Administration (MTA) Baltimore, MD Small 36 Metropolitan Council Minneapolis, MN Large 37 Metropolitan Transit Authority (Nashville MTA) Nashville, TN Small 38 Metropolitan Transit Authority of Harris County, Texasâdba Metro Houston, TX Large 39 Metropolitan Transportation Authority (MTA)âdba New York City Transit New York, NY Very large 40 Metropolitan Transportation Commission (MTC) San Francisco, CA Very large 41 New Jersey Transit Corporation Newark, NJ Large 42 San Bernardino County Public Transitâdba Omnitrans San Bernardino, CA Medium 43 Pinellas Suncoast Transit Authority (PSTA) Saint Petersburg, FL Medium 44 Pioneer Valley Transit Authority (PVTA) Springfield, MA Medium 45 Potomac and Rappahannock Transportation Commission (PRTC) Fredericksburg, VA Small 46 Regional Transportation Commission of Southern Nevadaâdba RTC Las Vegas, NV Large 47 Research Triangle Regional Public Transportation Authorityâdba Triangle Transit Raleigh, NC Small 48 Rhode Island Public Transit Authority (RIPTA) Providence, RI Medium 49 San Diego Association of Governments (SANDAG) San Diego, CA Large Table 2. List of survey respondents. (continued on next page)
8 Public Transit Rider OriginâDestination Survey Methods and Technologies # Organization City, State Size 52 Metropolitan Area Planning Agency (MAPA)/Transit Authority of Omahaâdba Metro Omaha, NE Small 53 Transportation District Commission of Hampton Roadsâdba HRT Norfolk, VA Medium 54 Tri-County Metropolitan Transportation District of Oregonâdba TriMet Portland, OR Very large 55 Utah Transit Authority (UTA) Salt Lake City, UT Large 56 Valley Regional TransitâValleyRide Boise, ID Small 57 Winston-Salem Transit Authority (WSTA) Winston-Salem, NC Small 50 San Francisco Bay Area Rapid Transit Districtâdba BART San Francisco, CA Very large 51 San Francisco Municipal Railwayâdba Muni San Francisco, CA Very large Table 2. (Continued). Case Examples For detailed highlights of emerging trends in survey practice and OD studies, five case exam- ples were selected from among the respondents to the questionnaire. Four transit operators and one MPO provide varied examples on how technologies, new sampling techniques, and passive data can be integrated into OD studies and transit surveys in general. The participants and the reason for their inclusion are as follows: ⢠Metropolitan Transportation Commission (San Francisco Bay Area) for its extensive expe- rience fielding surveys for 23 transit agencies using a variety of methods. ⢠TriMet (Portland, Oregon) for its in-house survey practice and adoption of mobile tablets for all rider surveys. ⢠Los Angeles County Metropolitan Transportation Authority for its use of a two-step survey process and high rate of non-English survey responses. ⢠Massachusetts Bay Transportation Authority (Boston) for its use of a heuristic model that generates boarding and alighting tables based on fare-card data. ⢠Chatham Area Transit (Savannah, Georgia) for its decision to rely on third-party passive data in lieu of conducting future on-board OD surveys.
