Community-Wide Evaluation Results Summary:
This section describes the various evaluation methods that were pursued in each pilot community. The descriptions provide an idea of what methods are available to replicate and pursue elsewhere when estimating the impact of nonmotorized investments on travel behavior. This section also presents the results of the data collection and evaluation of community-level travel behavior that the WG members performed to fulfill the statistical reporting requirements of the NTPP's enabling legislation. Throughout the program, the analysis has been useful to the communities for planning and monitoring purposes.
To effectively evaluate the impacts of the NTPP, the WG developed a consistent approach to collect and evaluate data while taking advantage of and encouraging additional data collection and evaluation initiated by individual communities. In addition to the counts and surveys that were administered in all of the pilot communities, some communities conducted additional counts, surveys, and modeling to better understand the impacts and community awareness of the NTPP and its activities. Table 10 displays the range of methods used by the pilot communities to collect the key data required for performance measures to assess travel behavior changes and goals identified in the legislation. In general, the NTPP used directly collected data to fulfill the statistical reporting requirements where possible. When this direct data collection was unnecessary or infeasible, the NTPP supplemented its directly collected data with available local and national sources.
Table 10: NTPP Evaluation Methods
|Evaluation Method||Columbia||Marin County||Minneapolis||Sheboygan County|
|Awareness, parking, or trail user community level surveys and counts||•||•||•||•|
|Vehicle miles traveled and mode share calculations||•||•||•||•|
|Bicycle and pedestrian demand modeling||•|
To gauge an on-the-ground increase or decrease in nonmotorized activity, each community conducted counts of bicyclists and pedestrians on days in the fall at pre-determined locations in 2007 and 2010. The methodology for these counts followed the National Pedestrian and Bicycle Documentation Project, developed by Alta Planning and Design and the Institute of Transportation Engineers.
To be representative of nonmotorized activity in the broader community, Alta advised that the pilot communities designate at least one count location for every 15,000 people. Accordingly, the 2007 populations and minimum number of count locations for each community are provided in Table 11. The 2010 counts were conducted in the same locations, for the same 2-hour period, and on roughly the same days as in 2007 to allow for direct comparison. Count data from this process can be compiled to analyze community-wide activity or used on a location-by-location basis. When feasible, each community attempted to place count locations near or adjacent to areas where NTPP projects have been or will be implemented.
Table 11: Number of Count Locations
|Pilot Community||2007 Population||# of Minimum Count Locations||Actual # of Count Locations Used|
|Minneapolis||351,184||23||23 pedestrian, 30 bicycle|
Figure 42 shows the sum total of bicyclists and pedestrians counted in the pilot communities in the fall of 2007 and 2010 at all of the count locations. The observed change in the sum total equates to an increase of 49 percent for bicyclists and 22 percent for pedestrians between the bookend years of 2007 and 2010. Individually, each community observed more bicyclists and pedestrians in 2010 than in 2007 at these locations.
Figure 42: Total Two-Hour Fall 2007 and 2010
Bicycling and Walking Counts for all Pilot Communities
In addition to the common 2007 and 2010 counts, each community performed a variety of additional counts to meet local requirements for information and reporting. Table 12 summarizes the ways in which each community collected additional count data. While much of these data are collected for internal use, some of the results from these enhanced count data are presented below.
Table 12: Summary of Count Data Collected by Community in Addition to Bookend Counts
|Pilot Community||Additional Locations||Use of Automatic Counters||Annual
|Counts on Weekends||Continuous
Three communities elected to conduct counts annually in addition to counts in the bookend years of 2007 and 2010. Figure 43, Figure 44, and Figure 45 shows the total number of bicyclists and pedestrians counted at all count locations during annual 2-hour counts in Columbia, Marin, and Minneapolis in the fall seasons from 2007 to 2010. Columbia and Marin conducted their counts on a weekday (from 4:00 to 6:00 p.m.) and a weekend day (from 12:00 to 2:00 p.m.) whereas Minneapolis conducted its counts only on a weekday (from 4:00 to 6:00 p.m.). As indicated by results from Columbia and Minneapolis, count totals can fluctuate year to year due to external variables such as weather conditions or special events on the designated count day. However, the trend line shows an overall increase of bicycling and walking in each community. Between 2007 and 2010, bicycling counts increased by 26 percent and walking counts increased by 14 percent in Columbia. Over the same years, bicycling counts increased by 68 percent and walking counts increased by 24 percent in Marin. In Minneapolis, bicycling counts increased by 33 percent and walking counts increased by 17 percent.
Figure 43: Annual Two-Hour Count Results in Columbia
Figure 44: Annual Two-Hour Count Results in Marin
Figure 45: Annual Two-Hour Count Results in Minneapolis
In addition to counts at the locations noted in Table 11, each community administered intercept surveys consistent with the National Pedestrian and Bicycle Documentation Project at a minimum of six locations in the fall of 2007 and the fall of 2010. Minneapolis administered a shortened version of the survey in both the spring and fall of 2010.
The intercept surveys administered at a representative sample of count locations in each of the pilot communities provide a snapshot of travel behavior of bicyclists and pedestrians in 2007 and 2010. Figure 46 shows the percentage of respondents who stated they were bicycling or walking for utilitarian (commute to work, school, shopping/doing errands, or personal business) reasons as opposed to for exercise/recreation reasons in 2007 and 2010. Note that while Columbia and Marin County administered their surveys on both weekdays and weekends, Minneapolis and Sheboygan County only administered surveys in conjunction with counts on a weekday during the morning or afternoon peak commute time (between 7:00 and 9:00 a.m. or between 4:00 and 6:00 p.m.). In each of the communities where surveys were administered on weekdays and weekends, as well as in Sheboygan County, a higher percentage of respondents bicycled or walked for utilitarian purposes in 2010 than respondents did in 2007. Minneapolis surveys showed a decline, but reflected a small sample of recreation and exercise users (10 percent of both walking and bicycling responses) for the 2007 baseline.
Figure 46: Percent of Utilitarian Trips for the Pilot Communities
*Note: Minneapolis surveyed only on weekdays between 4:00 and 6:00 p.m.
Figure 47 shows the percentage of pedestrian and bicyclist trips that people in Columbia and Marin County took that included transit, meaning that the respondent was either walking or bicycling to or from a ride on a public bus, train, or ferry. In each community for each mode, a greater percentage of pedestrian and bicycling trips included transit in 2010 than in 2007.
Figure 47: Percentage of Pedestrian and Bicyclist Trips that Included Transit for Columbia and Marin County
Table 13 shows the estimated average length of pedestrian and bicyclist trips in miles that people in all four communities made in 2007 and 2010. In some communities, the average distances increased; in others, they decreased. Note that these trip distances include both utilitarian and exercise/recreation trips.
Table 13: Average One-Way Estimated Trip Distances
|Minneapolis||Columbia||Marin County||Sheboygan County|
|2007||1.4 miles||6.1 miles||3.0 miles||10.6 miles||2.6 miles||17.8 miles||1.7 miles||N/A|
|2010||1.5 miles||6.6 miles||2.3 miles||7.3 miles||3.0 miles||15.1 miles||2.8 miles||6.2 miles|
Intercept surveys administered by Minneapolis in 2007 were only conducted on weekday peak periods (4:00 to 6:00 p.m.) during the annual counts. The lack of weekend survey data likely skewed the survey results for Minneapolis, which resulted, as reported in the previous section, in a disproportionate number of school and work commute trips when compared to results from the other pilot communities that also conducted weekend surveys. In 2009, Transit for Livable Communities partnered with St. Olaf College in Northfield, Minnesota, to look at the survey results and their use as inputs for the Intercept Survey model and develop recommendations to address this issue and improve the consistency with estimates from the rest of the pilots.
This problem was used as a mathematics practicum for a group of St. Olaf students, and their analysis led to a recommendation that TLC replicate the original effort with a supplemental round of intercept surveys in the spring of 2010 on both weekdays and weekend days and use a statistical test (a chi-square test) to measure the similarity of both rounds of surveys and determine if the new surveys could be used in place of the biased sample from 2007. The TLC administered the survey in the spring of 2010 and ran the statistical test. The test did not find strong similarity for the 2010 results with the 2007 surveys, and thus it was not possible for Minneapolis to replicate the same survey parameters as used in the other communities. This difference in data survey methodology can be seen by the disproportionately high results for Minneapolis utilitarian trips compared to the other communities (see Figure 46).
The pilot communities elected to administer community-level surveys and analyses that focused on issues and questions that were of particular interest to each community. Each of the approaches differed from each other since they were tailored toward unique aspects of their nonmotorized investments. Overall, the results from the community level surveys and analyses point to an increase in awareness of nonmotorized transportation, why people choose to or not to bicycle, more people use bicycle parking if more bicycle racks are provided, and the kind of trips people are taking on multiuse trails.
Columbia contracted with a professional research, evaluation, and analysis firm to assess community awareness and attitudes toward the GetAbout Columbia Program. This effort provided a baseline survey in 2008, a midpoint survey in 2009, and a final awareness and attitudes survey in 2010. The 2008 baseline survey included questions about respondents' expectations for the program. The 2009 and 2010 surveys provided opportunities to test respondents' experience with and overall embrace of the program. For each year, the survey was administered over the phone to over 400 random Columbia residents age 18 and over.
The findings from Columbia's awareness survey represent the impact a program like the NTPP can have on a community's attitude toward bicycling and walking and its level of awareness of an active nonmotorized program. Findings from Columbia's awareness survey point to an increased level of awareness of GetAbout Columbia and an increased sense that Columbia is a pedestrian- and bike-friendly community from 2007 to 2010. However, reasons for or against engaging in bicycling and walking remained generally unchanged over that same period of time. Specific results regarding these findings include:
The Marin County Department of Public Works, in partnership with Caltrans and the Golden Gate Bridge, Highway, and Transportation District conducted surveys at park and ride lots and major transit facilities in Marin County in autumn of 2008. The purpose of the surveys was to evaluate the demand for bicycle parking at these facilities by capturing general travel habits and interest in and input on bicycle parking facilities. The surveys also asked questions about the facility where each particular survey was distributed. Mail-back surveys were distributed at all 10 park and ride lots as well as the San Rafael Transit Center and the Larkspur Ferry Terminal. A total of 536 mail-back responses were returned, of which 244 came from park and ride lots, 231 from the ferry terminal, and 61 from the transit center.
The Marin County Bicycle Coalition used a different questionnaire to conduct an internet survey of its membership with similar questions about bicycling parking facilities at transit stations and park and ride lots. One hundred and nine Internet surveys were completed.
The findings from Marin County's bicycle parking survey point to reasons why people did not bicycle to or park their bicycles at each location. Specific results regarding these findings include the following:
In addition to manual bicycle and pedestrian counts, Minneapolis conducted an evaluation of bicycle parking in two neighborhood business districts and two schools (Washburn High School and Roosevelt/Wellstone High School) to examine the before and after impact of NTPP-funded bicycle parking installations. An inventory of existing bicycle parking facilities and multiple observations of bicycle parking were made in May and July of 2009. Additionally, to examine the perception about the quality and availability of bicycle parking postcard spoke surveys were distributed in the business district locations.
Follow up inventory of new bicycle parking and observations were made in May and July of 2011. The results of the observations show increases in the observed number of bicycles at the new parking installations. Table 14 shows the observation averages for schools and business district.
Table 14: Observations of Bicycle Parking at High Schools and Two Neighborhood Business Districts before and after Installation of NTPP Bicycle Parking
|Observation||Number of Bicycle Racks||Total Available Parking||Total Number of Bicycles Observed||% Using Bicycle Rack||% Using Non-Rack Parking|
At the two high school locations, bicycle parking installations increased bicycle parking availability 279 percent, leading to a 47 percent increase in students bicycling to school and a 66 percent decrease in the number of students securing bicycles at non-rack locations. It should be noted that much of the existing bike parking consisted of substandard or obsolete bike racks and all NTPP parking is consistent with best practices for secure bicycle parking.
At the two business districts, bicycle parking installations increased bicycle parking availability 92 percent, leading to a 40 percent increase in the number of observed bicycles, and a 27 percent decrease in the number of bicycles secured at non-rack locations. During both observation periods, it was often noted that clusters of parked bicycles would exceed available parking at popular destinations, resulting in significant numbers of bicycles secured to objects other than bike racks.
In all cases, the increase of bicycle racks resulted in increase of observed bicycles parked; however, the rate of increase was disproportionate. This observation is reasonable when considering that bicycle parking is most effective when there are ample spaces available and providing new bicycle parking expands the likelihood that a bicyclist will be successful in locating an open rack. On the other hand, if the parking use increases at a rate similar to parking expansion, it no longer creates new incentive to bicycle to a destination. Where the need for parking is identified, it is reasonable to provide facilities in excess of anticipated demand and increase the confidence that space will be available for each bicyclist who arrives.
Sheboygan County administered a trail user survey between July and October in 2009 and 2010. Over 550 people completed the survey in 2009 and over 380 completed the survey in 2010, which mainly focused on economic development questions as well as attitudes and characteristics surrounding nonmotorized trips. The survey was administered along two of the county's major trails.
The findings from Sheboygan County's trail user survey provide a snapshot of the kinds of trips people in Sheboygan County are taking on shared-use trails. Over 60 percent of the users were bicyclists and over 30 percent of the users were using the trail for utilitarian purposes. Over 50 percent of respondents were daily users of the trails, around 25 percent were weekly users, about 10 percent were monthly users, and about 10 percent were first-time users. Over 50 percent of the trips were over 5-miles long, about 15 percent were 3 to 5 miles, about 20 percent were 1 to 3 miles. Finally, over 50 percent of respondents reported spending money while using the trails.
The pilot communities contracted with the University of Minnesota's (UMN) Center for Transportation Studies in collaboration with NuStats, a survey research firm, to administer two bookend surveys: one in 2006 and one in 2010. The UMN research team designed and implemented surveys to collect travel behavior data to establish a baseline or "before" information on travel by bicycling and walking in the four pilot communities (and in the control site of Spokane, Washington). The research team used this baseline data in comparisons to "after" data that it collected with the same surveys in fall 2010 to identify changes in travel behavior in the pilot communities. Information on the research team's methodology can be found in the team's reports, available here: http://www.cts.umn.edu/Research/ProjectDetail.html?id=2007026.
The results of the household surveys are inconclusive. Several factors (such as having a limited sample size) contribute to the inability to detect consistent and statistically significant impacts of the NTPP's investment in pedestrian and bicycle facilities and programs over the past 3 years. A full discussion of the factors and the outcomes of the household survey in general are discussed in sections 2.2 and 2.3 of UMN's Nonmotorized Transportation Pilot Program Evaluation Study Phase 2 report, which is available here: http://www.cts.umn.edu/Research/ProjectDetail.html?id=2007026.
Because there is no recognized standard approach to quantifying bicycle and pedestrian mode share, the WG used two estimation methods to examine transportation-related changes over time. The WG used these methods, which it identified as the Intercept Survey method and the NTPP method, to determine whether they converged on similar results. The methods were used to estimate changes in mode share, the number of additional nonmotorized trips by community and per person in 2010, and based on that number, VMT (Vehicle Miles Traveled) averted. The VMT averted is an important measure for calculating the impacts of the program in terms of energy, the environment, and health. Table 15 outlines differences between these models.
Table 15: Summary of Model Inputs and Output
|Method||Major Primary Data Inputs||Secondary Data Inputs||Estimates Averted VMT?||Estimates
|Intercept Survey||Bookend counts and intercept surveys||ACS, NHTS||Yes||Yes (for ped and bike)||Yes|
|NTPP||Bookend counts||ACS, NHTS||Yes||Yes||Yes|
The Intercept Survey model, developed by Alta Planning + Design, uses the bookend intercept surveys to calculate VMT averted by nonmotorized travel as well as to calculate the modal share for pedestrian and bicycle modes for the years 2007 and 2010. The Intercept Survey model uses National Household Travel Survey (NHTS) data for total home based trips and trip distances by nonmotorized mode and American Community Survey (ACS) data for commute to work mode shares. Using trip purpose data collected in the bookend intercept surveys, the Intercept Survey model uses trip purpose ratios to estimate the total number of nonmotorized trips by trip purpose and nonmotorized mode. The model then sums the utilitarian trips by mode and multiplies them by average trip distances by mode. These calculations result in averted VMT by nonmotorized mode for 2007 and 2010. The change in averted VMT due to bicycling and walking between these 2 years is based on the trip purpose results of the intercept survey as well as the change - in this case increase - in the community-wide nonmotorized counts in each of the communities.
Because no standard method for calculating nonmotorized mode share and VMT averted exists, the WG developed a second model, termed the NTPP model, to see if results from these new methods converge. The NTPP model uses bookend community-wide count data to calculate mode share and VMT averted due to nonmotorized travel for the years 2007 and 2010. The NTPP model uses NHTS data for baseline mode share, trips per household, vehicle occupancy, and trip distances by nonmotorized mode and ACS data for households per community. The NTPP model uses NHTS mode share data based on metropolitan statistical area size to establish an assumed baseline for the pilot communities. The model then uses the count data to estimate a percent change in nonmotorized mode share between 2007 and 2010 and calculates the total number of trips per mode for 2010.
The NTPP model controls for the number of households in the communities over time and assumes that any increases or decreases in nonmotorized mode shares would result in a corresponding decrease or increase in vehicle trips. Transit trips are held constant since there are no consistent data available on how transit ridership might have changed within each of the four pilot communities over this time period. Changes in the total number of trips per nonmotorized mode are then multiplied by trip distances by mode to estimate VMT averted. A small group of academic peer experts reviewed this model and provided suggestions for its improvement, which were evaluated and incorporated. In short, the main difference between the Intercept Survey model and NTPP model is that the former uses trip purpose ratios, generated from the intercept survey results, to estimate the total number of nonmotorized trips by trip purpose and nonmotorized mode while the latter simply uses NHTS mode share data to estimate the total number of trips by mode.
Because not all communities conducted counts and surveys annually, both the Intercept Survey and NTPP models provide results for the bookend year (2010). To estimate the results over 3 years, it was assumed that any changes between 2007 and 2010 were linear. Therefore, the results for 2009 were assumed to be one-third smaller than 2010 and the results for 2008 are two-thirds smaller than 2010.
Using the NTPP model, Table 16 presents the estimated number of additional nonmotorized trips by community and per person that were made in 2010. These numbers are in addition to the baseline number of nonmotorized trips that people in the communities made in 2007 and controls for population growth. On average, people in the pilot communities made 4.7 more bicycle trips and 23.1 walking trips in 2010 than they did in 2007.
Table 16: Estimated Number of Additional Nonmotorized Trips by Community and Per Person in 2010 as Compared to 2007
|Community||Total Additional Nonmotorized Trips by Community||Additional Nonmotorized Trips
Per Person > 16 Years
Table 17 presents the estimated results of the NTPP and Intercept Survey models for averted VMT. For the NTPP model, the number of trips in Table 17 was multiplied by the national average trip distance for nonmotorized trips according to the NHTS: 2.26 miles for a one-way bicycling trip and 0.7 miles for a one-way walking trip. The estimates for both models are similar: an estimated 16 million miles were walked or bicycled that would have otherwise been driven in 2010, and an estimated 32 million miles were averted between 2007 and 2010. The number of averted VMT is similar between the modes because though the models estimated more walking trips than bicycling trips, bicycling trips are on average three times longer than walking trips. The results for 2007-2010 are twice the results of 2010 because the model calculates totals for 2010 compared to 2007, and not for 2008 or 2009. Due to the incremental nature in which projects were completed in the pilot communities between 2007 and 2010, it was assumed that results for 2008 were one-third of the results for 2010 and results for 2009 were two-thirds of the results for 2010. Accordingly, the total results for 2007-2010 are twice the result amounts for 2010.
While the number of averted VMT is only an estimate, there are some reasons to suggest that the estimate is low. Two considerations point to averted VMT being under-reported: 1) the intercept surveys indicate longer average nonmotorized trip distances than the NHTS national average nonmotorized trip distances (see Table 13) and 2) the models assume a one-to-one mileage trade-off between vehicle trips and nonmotorized trips; it is likely that vehicle trips are often longer than walking and bicycling trips, particularly for discretionary utilitarian trips (like shopping or dining out). However, NHTS data (for all modes) include trips made for social/recreational purposes, such as exercising, going to the gym, visiting friends, and visiting a public place. The portion of nonmotorized trips that were made strictly for exercising and would have otherwise not been made by a vehicle likely balance out the other two considerations that would have otherwise undercounted averted VMT.
There will likely be further increases in nonmotorized travel in 2011 as more projects are completed and in the years that follow after the NTPP projects are more fully integrated as key components of each communities' multimodal network.
Table 17: Estimated Averted VMT Total for 2010 and 2007-2010
|Model||Averted VMT in 2010||Total Averted VMT 2007 to 2010|
Table 18 shows estimated 2010 community-by-community totals for increases in total miles of bicycling and walking as well as per person (over the age of 16 years) averages for annual increases of bicycling and walking, based on the estimates of the NTPP model.
Table 18: Estimated Increases in Miles of Bicycling and Walking by Community and Per Person for 2010
|Pilot Community||Total Increases in Miles||Increases in Miles Per Person
The WG also used the NTPP model to estimate mode share changes between 2007 and 2010 (Table 19). In each community, both modes increased, but walking increased more than bicycling. For the communities in sum, bicycling mode share increased overall by 0.4 percent (i.e., a 36 percent increase from 2007), walking mode share increased 1.8 percent (i.e., a 14 percent increase), and driving mode share decreased 2.2 percent (i.e., three percent decrease) between 2007 and 2010. One of the assumptions of the NTPP model was that increases or decreases in walk and bicycle mode share would be directly balanced by a corresponding decrease or increase, respectively, in driving. Accordingly, since bicycling and walking increased in each community, the model assumed a total driving decrease equal to the increase in walking plus the increase in bicycling.
Table 19: Estimated Change in Mode Share (and Percent Change) between 2007 and 2010
|Columbia||+ 0.2 (15.8%)||+ 0.4 (4.4%)||- 0.5 (- 0.6%)|
|Marin County||+ 0.6 (64.4%)||+ 3.0 (21.0%)||- 3.6 (- 4.7%)|
|Minneapolis||+ 0.3 (26.3%)||+ 1.5 (10.9%)||- 1.8 (- 2.3%)|
|Sheboygan County||+ 0.3 (26.2%)||+ 1.2 (14.8%)||- 1.5 (- 1.7%)|
|Total||+ 0.4 (35.9%)||+ 1.8 (14.2%)||- 2.2 (- 2.7%)|
To establish a reference point for comparison of these changes in context with national trends, the pilot changes from 2007 to 2010 can be compared with the NHTS national change from 2001 to 2008 by examining the annual average increase over the time period of each. Table 20 compares the change in mode share experienced annually in the pilot communities with that of the Nation according to data from the NHTS. These annual averages indicate that mode share increases in the pilot communities to bicycling and walking and away from driving from 2007 to 2010 generally outpaced the national annual average from 2001 to 2008.
Table 20: Estimated Annual Change in Mode Share in the Pilot Communities
Per Year between 2007 and 2010 and Nationally Per Year between 2001 and 2008
|Columbia, 2007-10||+ 0.05||+ 0.10||- 0.13|
|Marin County, 2007-10||+ 0.15||+ 0.75||- 0.90|
|Minneapolis, 2007-10||+ 0.08||+ 0.38||- 0.45|
|Sheboygan County, 2007-10||+ 0.08||+ 0.30||- 0.38|
|Total, 2007-10||+ 0.09||+ 0.45||- 0.55|
|National, 2001-08||+ 0.03||+ 0.26||- 0.31|
In 2010, students from the UMN Humphrey Institute of Public Affairs developed a regression model to estimate bicycle and pedestrian infrastructure use in Minneapolis based on count data provided by BWTC and the city of Minneapolis. The purpose of this effort was to provide transportation managers with new information and a tool to help plan, manage, evaluate, and optimize investments in nonmotorized facilities. The process included assembling and cleaning the data, computing descriptive statistics, computing scaling factors for extrapolating the counts, estimating 12-hour daily counts from the extrapolated counts, and then modeling pedestrian and bicycling traffic accordingly.
The work resulted in predictive 12-hour maps (Figure 48Figure ) for bicycling and walking on Minneapolis' street system. Similar maps for the city's off-street trail maps were developed as well. Although the analysis identified some gaps in data collection needs, the outcome provides the basis for establishing normal travel behavior and conditions for Minneapolis. This type of tool can help better inform decision-makers about where to best invest in improvements based on relevant performance measures. By overlaying current bicycle and pedestrian infrastructure, decisionmakers can see if there are areas where more infrastructure is needed given the demand. The city of Minneapolis and Transit for Livable Communities now partner with the University of Minnesota Humphrey Institute for Public Policy for an ongoing Capstone Program focused on nonmotorized transportation.
Figure 48: Predicted 12-Hour Counts (6:30 a.m. - 6:30 p.m.) for Bicycling (left) and Walking (right) in Minneapolis
Source: J. Borah, University of Minnesota, May 2010
One goal of the NTPP was to develop a network of infrastructure facilities for walking and biking that connect directly with transit stations and community activity centers, including education, work, and recreation sites, and other important destinations. These connections are a vital component of a complete transportation system, as they promote walking and bicycling as a viable option to access every day needs, and enhance community livability and accessibility, particularly for low-income residents with limited resources to invest in private transportation. As a way to gauge connectivity, the pilot communities estimated the number of connections that each project made to various types of activity centers. These activity centers included schools, universities, downtown and employment districts, senior facilities, hospital/medical clinics, parks and recreation, grocery stores, and museums and tourist attractions.
Figure 49 shows the percentage of projects that include at least one connection to one of a variety of activity centers. In many cases, the same project connects to multiple destinations.
Figure 49: Percent of Projects with Connections to Activity Centers
The enabling legislation for the NTPP calls for developing "statistical information on changes in motor vehicle, nonmotorized transportation, and public transportation usage in communities participating in the program and assess how such changes decrease congestion and energy usage, increase the frequency of bicycling and walking, and promote better health and a cleaner environment."  The previous sections presented a discussion on changes in motor vehicle, nonmotorized transportation, and the frequency of bicycling and walking. This section presents a discussion about changes in public transit usage and congestion. Chapter 5 presents a discussion about changes in energy usage, health, and the environment.
The legislation asked the NTPP to measure modal travel, including by public transit. Transit usage rates are particularly important for NTPP because many pilot projects are designed to improve walking and bicycling connectivity to transit, which can replace lengthy automobile trips. Because there is reliable consistent community-level data available, the NTPP obtained unlinked trip data from the Federal Transit Administration's National Transit Database (NTD) for years 2006 to 2009 as a proxy for public transit usage in the pilot communities. Unlinked trips are the total numbers of passenger boardings on bus, rail, and paratransit services. A person's journey between an origin and destination may require multiple unlinked trips if the person has to transfer between services. Note that the NTD data reflects the operations of transit systems, which do not always align with city or county borders. Specifically, the data for Minneapolis covers multiple cities within the Minneapolis-St. Paul region.
Unlinked trip data results vary year to year for the pilot communities (Table 21). Transit use increased dramatically in Columbia between 2006 and 2009 due to service expansion, and modestly over that time period for Marin County. From 2006 to 2009, Sheboygan County decreased its number of transit routes due to budget cuts. Accordingly, Sheboygan County witnessed a notable decrease in transit trips when comparing 2006 to 2009, but less of a decrease when comparing 2007 and 2008 to 2006. Keeping pace with the national trend, Minneapolis transit trips increased over the 4-year period with trips on the system peaking in 2008.
Table 21: NTD Unlinked Trips for the Pilot Communities and Nationally, 2006-09
|Pilot Community||2006||2007||2008||2009||% Change 2006-09|
|Sheboygan County||551,267||532,835||531,714||452,605||- 17.9%|
|National||9.75 billion||9.95 billion||10.28 billion||10.13 billion||+ 3.9%|
The NTPP could not develop a suitable measure for changes in levels of congestion in each of the pilot communities over the years of the pilot project. As a proxy for community-wide vehicle congestion, the NTPP considered using U.S. Census ACS results for daily annual commute to work times, but this information is not suitable for congestion since other factors could affect travel time, such as people living further away from where they work. Congestion levels on roadways - measured annually on consistent days and times with traffic counts - would be a better measure, but standard data do not exist for this measure throughout all four communities.