TM #12 Existing and Desired Travel Pattern · less than $39,999 per year (see Figure 6), as compared with the 14% of non-transit riders who fall within the ... TM#12: EXISTING AND

TM #12 – Existing and Desired Travel Pattern March 2016

Greater Minnesota Transit Investment Plan

TM#12: EXISTING AND DESIRED TRAVEL PATTERNS | FINAL

Community Design Group (CDG) and Nelson\Nygaard Consulting Associates, Inc. | i

Table of Contents

Page

Introduction ............................................................................................................................................................................1 Methodology and Survey Tools ........................................................................................................................................1 Transit Usage ........................................................................................................................................................................4 Demographics .......................................................................................................................................................................6 Existing and Desired Travel Patterns ............................................................................................................................ 14 Conclusion ........................................................................................................................................................................... 29



Community Design Group and Nelson\Nygaard Consulting Associates, Inc. | 1

INTRODUCTION

An interactive online mapping tool was developed to better understand the travel patterns of

current transit users and non-transit users. Information collected through this tool can help

quantify the need for public transportation within and across communities. Analyzing

destinations and travel patterns helps transit agencies and communities identify where

additional or enhanced transit service is desired and where there is a need for further inquiry and

outreach on existing service.

METHODOLOGY AND SURVEY TOOLS

Wikimapping Tool

An interactive online mapping project was created using a Wikimapping platform to gather

transit user and non-transit user existing and desired destination locations. First-time

participants created a login by providing an email address and choosing a username.

Participants tagged their usual destinations on a Google map and could also provide the

destination type such as “work.” Each user’s data was tied to a unique identifier, allowing for

origin-destination analysis and for users to revisit and update the site multiple times without

needing to create a new account.

Instructions on how to interact with the tool were provided through a short tutorial video and

were also written at the top of the mapping page. Participants were asked a series of questions,

including their current level of transit usage, how frequently they travel, and the primary purpose

for traveling to each destination. There was no limit to the number of destinations each user was

able to contribute.

Destination Survey Tool

A simpler destination survey was also available for users uncomfortable with or unable to use the

Wikimapping tool. Similar questions were asked in the survey, including current transit use,

common destinations, and trip purpose. Rather than entering destinations on a map, participants

typed addresses and descriptions of destinations. These destinations were later geocoded and

combined with the destination information received through the Wikimapping tool to

comprehensively map and analyze travel patterns.

Distribution of Tools

The Wikimapping and destination survey tool were available from mid-December 2015 to the end

of February 2016. Both were accessible through the “Get Involved” page of the project website

(Click here to visit the GMTIP Project Website) and were shared in several email blasts to various

stakeholders. Links to the surveys were posted on MnDOT social media pages weekly throughout

the open survey period.

http://www.dot.state.mn.us/transitinvestment/




Figure 1 Wikimapping Tool

Total respondents and destinations

Destination responses were received from every MnDOT District and several outlying states. In

total, the Wikimap and destination survey yielded 1,481 responses from 341 unique users. Of the

341 unique users, 153 used Wikimaps to enter destinations (45% of users) and 188 used the

destination survey to enter destinations (55% of users). However, Wikimap users entered an

average of 7 destinations per user, while destination survey users entered an average of 2

destinations per user.

Wikimap users entered a total of 1,090 destinations (74% of destinations), while destination

survey users entered 391 destinations (26% of destinations).

Figure 2 depicts the distribution of destinations received from Minnesota. Participants indicated

the primary purpose for travel to each destination received through the Wikimap and destination

survey. Destinations were classified by travel purpose, and were grouped as either Home

locations or Destination locations. To analyze origin-destination travel patterns, Home locations

were assumed to be the origin of each participant’s trip and non-home locations were assumed to

be the destination for each trip. Figure 2 shows all participant home and non-home destinations.




Figure 2 Minnesota Destinations Received (Wikimapping and Destination survey)

Figure 3 shows the distribution of destinations within each MnDOT District.




Figure 3 Respondent Destinations by MnDOT District

District Number

Name

Number of Destinations

Percent of Total

1 Duluth 122 8%

2 Bemidji 57 4%

3 Brainerd 185 12%

4 Detroit Lakes 72 5%

6 Rochester 176 12%

7 Mankato 211 14%

8 Willmar 123 8%

- Outside of MN 49 3%

Metro Metro 486 33%

Total - 1481

Metro and Out of State Destinations

Generally, destinations from other states and the Twin Cities region are outside the scope of the

Greater Minnesota Transit Investment Plan (GMTIP) and were removed from demographic and

destination analysis. Several rural communities, however, are located within the Metro District

but are eligible to receive funding from MnDOT for transit services and are therefore within the

scope of the GMTIP. Trips that either started or ended in the Metro District but had either an

origin or destination within a rural area were included in analysis. Of the 1,481 destinations, 993

(66% of responses) were either located outside of the Metro District or were associated with a trip

that began or ended in a non-metro area.

Of the 341 unique participants, 252 provided destinations within the area included in the project

scope (73%).

TRANSIT USAGE

Participants were asked to describe their current transit usage, as shown in Figure 4. The majority

of respondents described themselves as non-transit users (76%).




Figure 4 Transit Usage Responses

Daily (5-7 days per week)

7%

Often (2-4 days per week)

3%

Regularly (a few times per month)

5%

Sometimes (less than once per

month) 9%

No, I do not currently ride

transit 76%

Trip Purpose

Wikimap and destination survey participants were asked to describe their primary purpose for

traveling to each identified destination. As shown in Figure 5, the most common responses were

home (41% of destinations) and work (37% of destinations). There was not a significant difference

in trip purpose between transit users and non-transit users.




Figure 5 Primary Trip Purpose: All Respondents

DEMOGRAPHICS

Participants who responded to the destination survey (55% of respondents) were asked a series of

demographic questions. Wikimap users (45% of respondents) are not included in this sample, as

the format of the mapping program was not able to accommodate a long series of questions.

Income

Respondents who are transit riders are more likely to have lower incomes, with 29% of respondents earning less than $39,999 per year (see Figure 6), as compared with the 14% of non-transit riders who fall within the same income bracket (see Figure 7). There is also a higher percentage of high earners ($100,000+ annually) among transit riders compared with 17% of non-transit riders. Many respondents chose the “Prefer not to answer” option (15% of transit users and 4% of non-transit users). Though every question in the survey was optional, providing a “Prefer not to answer” for questions that may be perceived as potentially intrusive or sensitive can help users feel more comfortable while providing personal information.




Figure 6 Income: Transit Users

Figure 7 Income: Non-Transit Users




Automobiles per Household

The number of vehicles per household, including motorcycles, was one of the largest single

determinants of whether a participant was a transit user. Whereas 19% of transit users had no

vehicles in their household (see Figure 8), only 1% of non-transit users had no vehicles (see Figure

9). Whereas only 44% of transit users had 2 or more vehicles available for their household, 78% of

non-transit users had two or more vehicles in their household.

Figure 8 Automobiles per Household: Transit Users

4+ 7%

3

15%

2 22%

0 19%

1

37%

Figure 9 Automobiles per Household: Non-Transit Users

4+ 0

14% 1%

1 21%

3

25%

2 39%




Household Size

Transit users are slightly more likely to have larger households, with 54% of respondents living in

households of more than 3-4 people, compared to 38% of non-transit users (see Figure 10 and

Figure 11). Living in larger households with fewer vehicles per household member may be a

contributing factor in whether households use transit for daily transportation needs.

Figure 10 Household Size: Transit Users

7+ (5-6) 4% 11%

(1-2) 46%

(3-4) 39%

Figure 11 Household Size: Non-Transit Users

(5-6) 7+ 8% 0%

(3-4) 30%

(1-2) 62%




Employment

Transit users are slightly less likely to be employed full-time than non-transit users and are more

likely to be employed part-time (see Figure 12 and Figure 13). The number of transit users

employed part-time may indicate the need for transit service to be available during non-

traditional work hours.

Figure 12 Employment: Transit Users

Unemployed Retired 4%

4%

Employed part- time 22%

Employed full- time 70%

Figure 13 Employment: Non-Transit Users

Unemployed Retired 3%

9%

Employed part-

time 10%

Employed full- time 78%




Disability

Figure 14 and Figure 15 show the percentage of respondents who describe themselves as having a

disability. Transit users are more likely to identify as having a disability (25%) as compared to

non-transit users (10%).

Figure 14 Persons with a Disability: Transit Users

Yes 25%

No 75%

Figure 15 Persons with Disability: Non-Transit Users

Yes 10%

No 90%




Household Member Age: Over 65

Figure 16 and Figure 17 show that transit users are slightly less likely to have household members

that are over age 65 (13%) as compared with non-transit users (16%). Households with more than

two members over 65 are more likely to be transit users (10% of households) than households

with only one or two members over age 65 (0% of households).

Figure 16 Household Members Over Age 65: Transit Users

(7+)

(1-2)

3%

(3-6) 3% 7%

None 87%

Figure 17 Household Members Over Age 65: Non-Transit Users

(1-2) 16%

None 84%




Gender

Female respondents were less likely to be transit riders than male respondents (see Figure 18 and

Figure 19). Only 16% of female respondents were transit users, whereas 34% of male-identified

respondents were transit users. Overall for the destination survey, respondents were more likely

to be female than male (see Figure 20).

Figure 18 Female Transit and Non-Transit Users

Transit User 16%

Non-Transit

User 84%

Figure 19 Male Transit and Non-Transit Users

Transit User

34%

Non-transit user 66%




Figure 20 Gender: All Respondents

Prefer not to answer

2%

Male 31%

Other 1%

Female 66%

EXISTING AND DESIRED TRAVEL PATTERNS

Destinations associated with home addresses were analyzed as origin-destination pairs to identify

existing and desired travel patterns. Of the 993 destinations received that fall within the spatial

scope of the project, 100 home addresses could be assigned to a specific geographic location to

create 567 destination pairs. The majority of these participants identified as non-transit users

(72%).

Travel Patterns by Destination

Figure 21 provides an overview of existing and desired travel patterns throughout Minnesota.

Each line of arrows represents a single trip. Orange lines represent non-transit users, while blue

lines designate the trip patterns of transit users.




Figure 21 Existing and Desired Travel Patterns




Figure 22 shows the number of origin-destination pairs per District. While the majority of trips

(73%) are made within Districts (highlighted in blue) there are many travel patterns between

Districts, especially the metro area.

Figure 22 Origin–Destination Trips by District

DISTRICT Desti-nation

Home

1

2

3

4

METRO

6

7

8

Non- MN

Total

1 56 2 4 62

2 2 14 1 9 1 13 40

3 1 39 2 12 54

4 1 10 1 1 7 1 21

METRO 4 7 46 3 1 3 64

6 5 93 6 104

7 13 1 96 1 111

8 2 3 9 2 8 64 3 91

Out of State 14 2 3 1 - 20

Total 79 14 54 13 98 101 106 71 31 567

Percent of Total 14% 2% 10% 2% 17% 18% 19% 13% 5% 100%

Existing and Desired Travel Patterns by District

An overview of key themes for each District’s origin-destination travel patterns is provided below

that show travel patterns within and across Districts at a higher level of detail. Districts 2 and 4

had similar regional travel patterns and were grouped together. District descriptions are included

below each District Travel Pattern map.




Figure 23 District 1

District 1

District 1 had several long distance destinations, with 4 outside of the state, many along the shore

of Lake Superior, and several to the Twin Cities metro area. Intra-district trips were most

concentrated in the Duluth area. Within Duluth, there were a higher proportion of transit-user

respondents than the rest of the District. One non-transit user in the Virginia/Chisholm area

entered 24 unique destinations, accounting for nearly 38% of the origin-destination pairs in

District 1.Travel patterns for District 1 are illustrated in Figure 23.




Figure 24 District 2 and 4

District 2 and 4

District 2 and 4 had the lowest levels of participation, with a combined 61 origin-destination pairs

(less than 11% of total origin-destination pairs). Many of these trips covered long distances, with

one non-transit user in Roseau entering 26 destinations, many of which are in the Twin Cities

metro area. The southern portion of the Detroit Lakes District contained a few trips to nearby

areas in District 3 and 8. Travel patterns for District 2 and 4 are illustrated in Figure 24.





District 3

Like most Districts, District 3 had more intra-District trips (72%) than inter-District trips (28%).

Most of this District’s trips are concentrated in the southern portion of the District, nearest the

Twin Cities metro area. District 3 had the second most trips crossing into the Twin Cities metro

area (12). Non-transit users in Sartell and Zimmerman and transit-users in Elk River and east of

Becker contributed the majority of the responses in District 3. Travel patterns for District 3 are

illustrated in Figure 25.





District 6

District 6 had a high number of intra-District trips (89%) with the majority of travel within

Winona and Rochester. Other travel patterns included travel to the metro area and west along

the southern border of the District. Travel patterns for District 6 are illustrated in Figure 26.





District 7

Districts 7 had the most intra- and inter-District trips. Many of the short distance trips were

within and near Mankato and the Le Sueur area. District 7 respondents most often traveled

northeast toward the Twin Cities metro area, or to the east or west throughout southern

Minnesota. Travel patterns for District 7 are illustrated in Figure 27.





District 8

Districts 8 participants traveled within the District, to six (6) different Districts, and several traveled to

destinations out of state to the southwest. The majority of respondents in District 8 traveled

northeast, toward the Twin Cities metro area with several traveling further to District 1 and 3.

Respondents also traveled east to District 6 and southeast to District 7. Travel patterns for District

8 are illustrated in Figure 28.




Travel Patterns by County

Similar patterns can be seen when summarizing origin-destination pairs by county. Figure 29

shows travel patterns across counties (shown as lines) and travel patterns within counties (shown

by color and texture). Thin lines indicate a single trip pair, while thicker lines indicate multiple

origin-destination pairs between counties. Travel patterns within counties are shown by color

and texture. Counties that are shown in darker shades indicate a greater numbers of intra-

county trips. Areas without texture indicate counties that had no intra-county trips identified in

the Wikimapping or destination survey exercise. Overall, there are 35 counties with trips within

the counties and 135 unique travel patterns between counties.




Figure 29 Travel Patterns Summarized by County




Travel Patterns by Zip code

Figure 30 shows travel patterns within zip codes and between zip codes. Travel within zip codes is

shown through color and texture, with the darker colors indicating a larger number of trips

within the zip code. Trips across zip codes are shown in lines indicating the total summed

number of trips from the same home zip code to the same destination zip code. Thin lines

indicate a single trip, whereas thicker lines represent greater combined numbers of trips

identified by participants.




Figure 30 Travel Patterns Summarized by Zip Code




Mil

es

City-to-City Travel Patterns

As shown in Figure 31, respondents in District 1 and 2 were more likely on average to travel long

distances to reach city destinations, 137 and 148 miles, respectively. Non-transit users were more

likely to identify destinations that were on average 14 miles farther than transit-user destinations

(see Figure 32).

Figure 33 shows travel patterns between cities in Greater Minnesota. Of the 567 total trips, 235

were between cities, with a large proportion of these entering the metro area. The remaining 332

trips either began, ended, or did not have their origin or destination with a municipal boundary.

Figure 31 City-to-City Travel Patterns: Average Miles per Origin Destination Pair

District Average Miles per Origin-Destination Pair

1 137

2 148

3 28

4 46

METRO 18

6 17

7 28

8 27

Total Average 56

Figure 32 Average Miles per Origin Destination: Transit User vs. Non-Transit User

50

40

30

20

10

0

Transit User Non-Transit User

Average Miles per Trip




Figure 33 City-to-City Travel Patterns




CONCLUSION

The Wikimap and online destination survey provided valuable insight into desired and existing

travel patterns. The online tool also provided information on demographic and travel trends and

differences between non-transit and transit riders. According to the data collected through these

tools:

Transit users are more likely to have lower incomes, have larger households and fewer cars

per household, and were more likely to be employed part-time than non-transit users.

The majority of respondents to the Wikimap and online destination survey were

non-transit users (76%). Destinations and travel patterns identified by Wikimap

participants can aid transit agencies in identifying underutilized markets and unmet

transportation needs in Greater Minnesota:

o Non-transit users and transit users identified a need for trips crossing

county lines and connections to cities in other Districts. Transit providers

may need to provide service opportunities that cross county lines and connect cities

in other Districts to reach this new market. This may require updating service areas,

funding boundaries, and/or current funding structure. Coordinating service

schedules and/or co-locating transit stops to allow for transfers among neighboring

transit agencies may be a near-term solution to provide long-distance or city-to-city

service. In some cases, consolidating transit providers may enable agencies to cover

larger areas to further meet the need for longer distance travel.

o Many non-transit users identified nearby destinations that could be

served by transit. Non-transit users that have access to transit may be more likely

to use transit if transit also served additional nearby destinations. The Wikimapping

and destination survey tool, as well as other GMTIP surveys, may provide support to

modify existing service to serve more short distance trips through transit, including

information on trip purposes, desired time of day travel to nearby destinations, and

reasons for not using transit. Short distance travel patterns may indicate an

opportunity to provide more information on available transit service to potential

riders and an opportunity for transit providers to increase farebox revenue through a

greater number of short distance trips.

TM #12 Existing and Desired Travel Pattern · less than $39,999 per year (see Figure 6), as compared with the 14% of non-transit riders who fall within the ... TM#12: EXISTING AND

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