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The primary purposes of the survey were to update ridership by the jurisdiction of residence for use in Metrorail’s operating subsidy allocation and to collect demographic, travel, and access data for Title VI compliance, system planning, and operation analyses. It was not a customer opinion survey; the focus was on ridership and travel characteristics.
The primary purposes of the survey were to update ridership by the jurisdiction of residence for use in Metrobus’ operating subsidy allocation and to collect demographic, travel, and access data for Title VI compliance, system planning, and operation analyses. It was not a customer opinion survey; the focus was on ridership and travel characteristics.
This survey was completed as part of the Miami North Corridor project on all 286 Metrobus routes of Miami-Dade Transit, and on Metrorail at all stations.
This Miami-Dade Metrorail Survey obtained ridership characteristics such as origin-destination patterns, trip purpose, and mode of access and egress. The data obtained from this survey was used to update and validate the Southeast Regional Planning Model (SERPM v6.5) and for transportation planning in the region.
All 41 routes in the BCT system were surveyed.
The survey was an on-board survey of transit riders on all regular route services for bus and light-rail in the Minneapolis-Saint Paul metropolitan area. The primary purpose of the study was to gather the data needed to update the mode choice models that are an integral component of the regional travel forecast model maintained by the Metropolitan Council, the metropolitan planning organization for the Minneapolis-Saint Paul metropolitan area. The survey instrument focused on identifying characteristics of the trip taken by each transit rider, including origin, destination, trip purpose, and mode of access. The survey also collected relevant socioeconomic and demographic information.
The Metropolitan Council administered a system-wide transit on-board survey between September and November 2010 as part of the 2010 Travel Behavior Inventory to provide detailed transit usage patterns and rider information to support modeling and planning efforts. The study’s focus was to capture only the most current and reliable data necessary to determine future public transportation needs in the Minneapolis region. The study was structured to collect detailed transit ridership data for different routes during different times of day to develop a disaggregate transit trip table that will support advanced travel demand modeling. The study was designed to leverage existing data sources, such as the 2005 on-board survey, to provide the best quality data to update the Travel Behavior Inventory in the Minneapolis-Saint Paul metropolitan area.
The 1990 transit on-board survey aimed to update the 1988 survey, which was conducted as part of the Preliminary Engineering Study for the Hennepin County Light Rail Transit System. The 1988 study received Regional Transit Board funding, and survey results could be applied to a mode split model for projecting ridership on the proposed Light Rail Transit System. However, this survey was designed with the 1990 Travel Behavior Inventory in mind. The intention had been to update the 1988 survey in 1990 to be compatible with the 1990 Travel Behavior Inventory data. The results of the 1990 survey were used primarily to create a table of observed transit trips between each of the 1,200 traffic analysis zones in the region. This trip table was used to calibrate a new mode split model, which estimated future year travel by mode. The 1990 update survey focused on new routes and routes that had changed significantly since 1988. To preserve compatibility with the 1988 survey, the same survey questionnaire card was used, together with the same survey procedures for data collection. The procedures randomly sampled bus patrons during the transit trip, asking key questions about the patron and the transit trip. The survey card was intended for patrons to fill out quickly so it could be completed during the transit trip. The questions focused on conditions that have proven over time to significantly influence ridership. In all, a total of 20,126 valid survey records were processed. Adding surrogate trips, the survey data file is composed of a total of 27,159 trip records . About 10% of the records were filled out by persons who had answered more than one questionnaire.
The main purpose of the survey was to gather current data on how the transit system was being used in 2010. The survey asked passengers a series of questions about their household characteristics and the trip that they were making on the day of the survey. The survey was conducted in three phases between December 2010 and December 2011. Close to 1.1 million unlinked trips are made on an average weekday on all 140 routes systemwide. The survey results suggest several different populations of users. At the low end of the economic spectrum are passengers who really have no other transit option besides public transit. There is also a group of passengers that appear to be somewhat better off financially. They could be categorized as middle-income households, and many of these passengers may have other options for transportation, but they choose to take public transit because it makes sense for them from a financial or time-savings standpoint. Finally, there may be a smaller group of passengers who may live and work downtown in relatively high-paying positions. These passengers can afford to take a taxi but choose to ride transit because of its convenience.
On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to accurately assess mode share for trips within San Francisco (including trips to or from San Francisco, but excluding through trips) among city residents and residents of other Bay Area counties; provide additional trip details, including trip purpose information for each trip in the mode-share question series; and use the mode-share percentages collected on this survey in conjunction with total trip estimates provided by SFMTA to project the number of trips by mode on an average day in San Francisco.
On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.
On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.
On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.
On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.
This document describes the contents of the following data products: 1. ALL-survey_results_NREL_LiveWire_04_14_2023 (Excel file with two worksheets) 2. eCab_DOE_NREL_LiveWire_04_14_2023_results_all_surveys_except_SP_csv (CSV file) 3. eCab_DOE_NREL_LiveWire_04_14_2023_SP_survey_results_only_csv (CSV file). These data products contain the results of the surveys conducted throughout the DOE-funded Electric First-/Last-Mile On-Demand Shuttle Service for Rural Communities in Central Texas project. The Excel file is the original database of all the survey results (contained in two worksheets). The CSV files are those two worksheets saved as individual CSV files.
### The Colorado Energy Office conducted a mini pilot program study as part of the Can Do Colorado initiative, providing e-bikes to 13 low-income participants. The program aimed to encourage energy-efficient transportation during the COVID-19 pandemic as transit services were reduced and people were concerned about exposure. The insights garnered from this small-scale pilot study informed the design of a full-scale, 2-year pilot in locations across Colorado. For more information about the mini pilot program, see NLR's [Preliminary Results Report](https://www.nlr.gov/docs/fy21osti/79657.pdf). Micromobility options such as e-bikes offer a solution for improving energy efficiency for short-distance trips, especially in urban areas. Pedal-assist e-bikes use an electric motor and battery to help power the bike. The motor amplifies the power behind each pedal stroke, augmenting the energy you put into the bike. #### Data Collection Agency The Colorado Energy Office conducted the survey. #### Survey Methodology Participants in the program received a Momentum LaFree E+ e-bike (Class 1) and accessories at no cost and manually submitted travel data and feedback for 3 months using the CanBikeCo App. The smartphone app, developed in partnership with NLR, used a customized version of the [NLR OpenPATH platform](https://www.nlr.gov/transportation/openpath.html). #### Survey Records and Data Survey records include a total of 13 participants. This dataset contains 3 months of end-to-end, multimodal travel data manually submitted via smartphone app by 13 low-income essential workers in the greater Denver area. The data includes distance, mode (e.g., e-bike, car, transit), trip purpose, and demographic information.
In 2021–2022, the Colorado Energy Office conducted a full-scale pilot program study on e-bike usage as part of the Can Do Colorado initiative, providing e-bikes to low-income participants across the state. A [2020 mini pilot program study](https://www.nlr.gov/transportation/secure-transportation-data/tsdc-2020-can-do-colorado-e-bike-pilot-program.html) informed the full-scale study. Both studies used pedal-assist e-bikes, which feature an electric motor and battery to help power the bike. The motor amplifies the power behind each pedal stroke, augmenting the energy you put into the bike. #### Data Collection Agency The Colorado Energy Office conducted the study in partnership with local organizations in Adams and Broomfield counties (Smart Commute Metro North), Boulder (Community Cycles), Durango (Four Corners Office for Resource Efficiency), Fort Collins (City of Fort Collins), Pueblo (Pueblo County), and Vail (Town of Vail). #### Survey Methodology Program participants received an e-bike and accessories at no cost and manually submitted travel data and feedback via the CanBikeCO smartphone app. Developed in partnership with NLR, the app used a customized version of the open-source [NLR OpenPATH platform](https://www.nlr.gov/transportation/openpath.html). #### Survey Records and Data Survey records include 170 participants. The six datasets contain up to 18 months of partially automated travel diaries, combining sensed and surveyed travel behavior data—patterns of multimodal, end-to-end, individual human mobility—as well as demographic information from participants. The number of e-bike trips and e-bike miles traveled per location are 1,560 and 4,179 for Adams and Broomfield counties; 8,481 and 27,000 for Boulder; 2,815 and 6,307 for Durango; 3,483 and 7,080 for Fort Collins; 4,022 and 14,887 for Pueblo, and 1,206 and 3,3361 for Vail.
In 2022, the City of Durham conducted an e-bike pilot program study to learn more about how electric bikes (e-bikes) could improve the transportation experience in the "Bull City" (a.k.a., Durham, North Carolina). The study used pedal-assist e-bikes, which feature an electric motor and battery to help power the bike. The motor amplifies the power behind each pedal stroke, augmenting the energy you put into the bike. #### Data Collection Agency The City of Durham's Transportation Department conducted the study. #### Survey Methodology Program participants used electric-assist e-bikes for at least 4 weeks between August and November 2022 in exchange for sharing information about their experiences, including tracking their travel via a smartphone app. In addition to the e-bike, participants received maintenance support along with a helmet, bike lock, and other accessories. Data collection was enabled by the [NLR OpenPATH platform](https://www.nlr.gov/transportation/openpath.html). #### Survey Records and Data Survey records include a total of 76 participants. The dataset contains 3 months of partially automated travel diaries, combining sensed and surveyed travel behavior data—patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from participants. The number of total trips was 6,488, the number of e-bike trips was 2,183, and the number of e-bike miles traveled was 5,450.