Providing You a Ride to Work and Home

-- A service coverage analysis of the City of Gainesville Regional

Transit System (RTS)

1 Introduction

1.1 Research Question

This paper borrows from the research of Stephen Crim at the Mobility Lab to

analyze the City of Gainesville Regional Transit System’s (RTS) ability to provide

transportation to individuals traveling between home and work. Here, the word “ability”

refers to the possibility of travel. In other words, can people travel between home and

work using the transit system? To answer this question, the research looked at the

extent to which RTS covers transit supportive areas in the community.

1.2 Study Area

The boundary of the Metropolitan Transportation Planning Organization (MTPO)

for the Gainesville Urbanized Area defines the research study area. Although the bulk of

RTS’s service is within the City of Gainesville municipal limits, service is provided to a

number of areas just on the periphery of that boundary. Considering the existing service

area1 or the municipal limits would provide a more realistic picture of service coverage

based on current funding, but it would ignore relevant employment and residential

population pockets immediately surrounding the city and therefore discount the need

for service expansion. Moreover, given that the MTPO dictates long-term transportation

policy and expenditures in the area operated by RTS, its boundary seemed to be the

most appropriate choice.

1 The RTS service area has traditionally been defined as the area within a three quarter mile buffer of RTS routes.

1.3 Data Sources

The research utilized the Longitudinal Employer Household Dynamics (LEHD2)

Origin-Destination Employment Statistics (LODES) Workplace Area Characteristics (WAC)

and Origin-Destination (OD) datasets from the US Census Bureau. The program tracks

employment, earnings, and job flows down to the census block level. More specifically it

contains information on where people live and work, which allows for the simulation of

local commuting patterns. According to the dataset, there are 32,123 unique3 O-D pairs

inside the MTPO boundary totaling 44,581 jobs.4

Additional datasets include 2010 census block data from the Florida Geographic

Datasets Library (FGDL) and internal RTS shapefiles for bus stops and routes.

2 Methodology

2.1 Transit Supportiveness

The WAC files included in the LODES dataset contain information on the total

number of jobs in each census block. Joining the 2010 WAC data to the 2010 census

block data provides both employment and demographic information in one dataset.

Each census block was then assigned a transit supportiveness score using the criteria

outlined in the Transportation Cooperative Research Board’s Transit Capacity and

2 See: http://lehd.ces.census.gov/data/ 3 Unique refers to the combination of work-home census blocks. Most combinations in the study were only associated with one job. 4 It is important to note that certain classes of jobs, like the self-employed are not represented in the LODES dataset and this figure only includes individuals who both live and work in the study area.

Quality of Service Manual (2nd Edition). Table 2-1 shows the thresholds used to define

transit supportiveness.5

Table 2-1: Transit Supportive Index

Transit Supportive

Index Census block with

-1 Residential density < 3 dwelling units per acre and job density < 4 jobs per acre.

1 Residential density ≥ 3 dwelling units per acre and job density < 4 jobs per acre.

2 Job density ≥4 jobs per acre and residential density < 3 units per acre.

3 Residential density ≥ 3 dwelling units per acre and job density ≥ 4 jobs per acre.

There are a total of 3,571 census blocks inside the MTPO boundary, 1,216 of

which are identified as transit supportive. While representing only 13% of the total land

area inside the MTPO boundary, they contribute 47% of the total population6 and 75%

of the total employment; see Figure 2-1. This distribution immediately highlights the

difficulty RTS faces in covering both home and work locations. While most jobs may be

aggregated to core areas, the majority of individuals are dispersed in low density areas

where transit service is expensive to provide.

Figure 2-2 shows the spatial distribution of the transit supportive blocks

throughout the MTPO area. It’s clear that RTS provides service in relatively close

proximity to most of the areas identified as transit supportive. Areas outside of the RTS

5 It is important to note that this does not consider any of the socioeconomic metrics that are often used to define transit dependent, like vehicle availability, which in turn makes an area transit supportive. Many of the areas within the MTPO boundary that are transit dependent are marked by low density, single family residential development. 6 The total population in the MTPO boundary is 190,132.

service area that are identified as transit supportive, like Haile Plantation, Hunters

Crossing, Jonesville, and Turkey Creek have all been identified for service in various

short- or long-range transportation planning documents.

Figure 2-1: Transit supportive block's share of employment and population in the MTPO area

Figure 2-2: 1,218 Transit Supportive Blocks inside the MTPO Boundary

2.2 Two Kinds of Traditional Coverage

To identify service coverage levels for those blocks identified as transit

supportive, a Euclidian quarter-mile buffer was placed around each RTS bus stop and

intersected with the underlying census block layer; a quarter mile was used because this

is generally considered the longest distance individuals will walk to access a bus. To

better capture actual pedestrian accessibility the analysis was then refined by using a

quarter-mile service area buffer7 around each bus stop using ArcGIS Network Analyst.

2.3 Origin-Destination Coverage

The approach above is limited since it provides no indication of whether both

ends of a particular home-work trip are covered. To evaluate RTS service coverage at

the trip level, the LODES O-D dataset was employed. Each part of the O-D pair was

assigned a transit supportiveness value based on the thresholds outlined above and

then selected based on whether their centroid fell within the service area buffers

created earlier. The result was a table showing the coverage extent of origins and

destinations by transit supportiveness value. Figure 2-3 identifies the top 50 commute

patterns ranked by number of jobs. The red points symbolize work locations and the

green points represent home locations. The lines between the work and home locations

are symbolized to show the different amount of commutes covered.

7 Service area buffers were generated using a road network dataset. The dataset has information on a variety of roadway facilities but does not include features like footpaths. As a result, there will be some underestimation of the actual service area around each stop.

2.4 Route Coverage

To approximate the ease of travel and move beyond a theoretical possibility of

travel, those O-D pairs that both fell within the service area buffer were evaluated to

determine how many routes were required to move between them.8 For example, each

part of an O-D pair may be covered, but if they are located in such a manner where an

individual would have to make an excessive amount of transfers then it is unlikely they

will use transit even if the possibility exists.

This refined method used a Python script to evaluate service coverage by

calculating the number of commutes covered by each individual route, as well as those

covered by making a single transfer between different routes. Although a quarter-mile

network buffer is still used, the bus stops are grouped by the routes to which they are

associated with instead of being treated as independent points.9

8 Only routes that operate at least 10 hours a day were included in the analysis. 9 Within Python a data dictionary was created that identified all the routes that serve a particular stop. This established all single transfer route connections. For a certain O-D pattern, the Python script would first look at which routes cover the work location, and then find which routes cover the home location. If a single route did not cover both the work and home locations, the Python script would then go into the data dictionary to find whether a connection existed between the routes covering the work location and routes covering the home location. As its output it would then list all possible route combinations.

Figure 2-3: Top 50 commute patterns by number of jobs

3 Results

3.1 Traditional Coverage

As described in the previous chapter, two kinds of buffers were used to assess service

coverage – a quarter-mile Euclidean buffer and a quarter-mile service area buffer. Figures 3-1

and 3-2 show the degree to which transit supportive blocks are covered by each buffer type.

Population and employment figures for each block were calculated on the proportion by which

the block was covered by the buffer. Figures 3-3 and 3-4 show the sharp contrast in coverage

differences when network pathways are considered.10

10 It is worth noting that there is little change in coverage if only the current RTS service area is considered. The percentage of each attribute would be: area 60%; number of jobs 78%; population 66%; number of households 67%.

Figure 3-1: A quarter-mile Euclidean buffer of RTS bus stops

Figure 3-2: A quarter-mile network buffer of RTS bus stops

Figure 3-3: Service coverage using a quarter-mile Euclidean buffer of bus stops

Figure 3-4: Service coverage using a quarter-mile network buffer of bus stops

1,903

5,46913,643 6,050

7,82380% 67,417

92%

75,49385%

33,64185%

0%

20%

40%

60%

80%

100%

Area (acre) Number of Jobs Population(2010) Number of Households

Euclidean Buffer Coverage of Transit Supportive Blocks

Not Covered Covered

3,641

13,521

28,956 12,801

6,08563% 59,365

81%

60,18068%

26,89068%

0%

20%

40%

60%

80%

100%

Area (acre) Number of Jobs Population(2010) Number of Households

Service Area Buffer Coverage of Transit Supportive Blocks

Not Covered Covered

3.2 Origin-Destination Coverage

Table 3-5 shows RTS service coverage of local commuting patterns. The table

reflects the challenges faced by transit agencies that operate in low density areas.

Almost 65% of the commutes within the MTPO boundary begin in a non-transit-

supportive home location with only 28% of commutes consisting of a home and work

census block that is transit supportive. RTS covers approximately 40% of the 12,611 jobs

where both the home and work locations are transit supportive. Figure 3-7 shows an

example of one such covered commute.

Table 3-1: RTS service coverage of commute patterns

Total Number of Jobs (2010)

Work Location

Grand Total Not Transit Supportive Transit Supportive

Home Location Not Covered Covered Not Covered Covered

Not Transit Supportive 5,013 1,209 6,020 16,523 28,765

Not Covered 3,995 919 4,567 12,483 21,964

Covered 1,018 290 1,453 4,040 6,801

Transit Supportive 2,511 694 3,482 9,129 15,816

Not Covered 1,190 305 1,535 4,091 7,121

Covered 1,321 389 1,947 5,038 8,695

Grand Total 7,524 1,903 9,502 25,652 44,581

Approximately 88% of the commutes that begin and end in a transit supportive

area are covered on at least one end by RTS,11 but over half of those commutes (48% of

11 This represents O-D pairs that fall under the following categories: [Transit Supportive Home Location Covered]-[Transit Support Work Location Not Covered] (1,947) + [Transit Supportive Work Location Covered]-[Transit Supportive Home Location Not Covered] (4,091) + [Transit Supportive Work Location Covered]-[Transit Supportive Home Location Covered] (5,038).

total commutes) are not covered on both the home and work end.12 Figure 3-5 shows

12 home locations which represent 1,091 of the 4,091 commutes where the transit

supportive home location is not covered by RTS but the transit supportive job location is.

Conversely, Figure 3-6 shows 10 work locations which are the destinations for 623 of the

1,947 commutes whose transit supportive work location is not covered but whose

transit supportive home location is. These maps highlight the reason why many agencies

are adding route deviated services to their transit offerings. While almost all the areas

shown on the map are adjacent to a RTS route, traditional cul-de-sac road networks

place many individuals in a community outside the traditional walking distance buffer

used in most accessibility analyses.

The results also highlight current deficiencies in the methodology that

undoubtedly underestimate actual coverage. Figure 3-8 shows several transit supportive

areas that are considered not covered despite having transit coverage on one or more

sides on the census block boundary. This was due to several factors including private

roadways not being included in the roadway dataset so therefore not factored into the

service coverage buffer and the centroid of the census block being located in an area

outside of where most development in the block is located.

12 [Transit Supportive Home Location Covered]-[Transit Support Work Location Not Covered] (1,947) + [Transit Supportive Work Location Covered]-[Transit Supportive Home Location Not Covered] (4,091).

Figure 3-5: 12 home locations generate 1,091 commutes where the home location is uncovered but work location is covered by RTS

Figure 3-6: 10 work locations receive 623 commutes in which the home location is covered but the work location is uncovered by RTS

Figure 3-7: An example of commute whose origin and destination are both covered by RTS service

Figure 3-8: Uncovered census blocks surrounded by RTS routes

3.3 Route Coverage

Table 3-2 lists the top 10 routes ranked by the number of commutes they cover.13

Table 3-2: Routes covering the most commutes and jobs

Rank Route Number of Jobs14 Number of Unique O-D Pairs

1 43 432 330

2 8 339 252

3 11 335 224

4 5 300 258

5 6 286 248

6 1 270 215

7 15 269 233

8 41 223 185

9 25 216 177

10 10 211 187

Approximately 7% (3,22115) of the commutes within the MPTO boundary can be

satisfied by a single route. The coverage number increases to 18% (8,220), if a single transfer is

considered. The most significant transfer pattern involves the routes 1 and 75. Figure 3-9

identifies the top 6 transferring patterns. In terms of commutes that have both their origin and

destination covered by RTS, 33% of the commutes can be taken without a transfer, 51% require

exactly one transfer, and 16% of the covered trips require more than one transfer.

13 All other routes account for 1,531 jobs representing 1,026 commutes. 14 It should be noted that some of the commutes can be covered by multiple routes so the numbers presented are not unique. 15 The analysis did not include RTS routes which run less than 10 hours per day. This includes routes 11C, 27, 36T, 38T, 39, 62, 77, 126, 128 and all Later Gator routes. Two exceptions to this restriction were made for the routes 2B and 24B since they are splits and operate during both the morning and afternoon periods when people are liking commuting between home and work. It is worth noting that each individual Later Gator route would rank in the top 10. The number of covered jobs by individual Later Gator routes is shown in the following table.

Route Number of Jobs Number of Unique O-D Pairs

301 348 259

305 313 240

303 302 241

300 237 204

302 180 154

Figure 3-9: Top 6 single transfer pattern

4 Conclusions

The service-coverage analysis looked at the level of accessibility RTS provides to and

from transit supportive census blocks and home-work trips in general.

Of the 1,218 transit supportive blocks inside the MTPO boundary, the RTS service area

covers 1,093 of them, which equates to 99% of the total jobs and 95% of the total population in

transit supportive areas. While a large number of jobs and individuals live outside this area,

they would be expensive to serve.

The analysis then used two traditional buffer types to test the extent of transit

supportive block coverage by RTS service. As expected, coverage decreased when using a

service area buffer rather than a Euclidian buffer. Since individuals are not able to move “as the

crow flies,” this kind of coverage is more accurate than a Euclidian buffer but it has its own

limitations which likely lead to an underestimate of actual coverage. Under this scenario only

63% percent of the population and 81% of the jobs that are covered.

The OD file of LODES dataset allowed for further refinement of the analysis. RTS

completely covers 9,757 out of 44,581 commutes (22%); it is important to note that 72% of

commutes begin or end in a non-transit supportive area. Figures 3-5 and 3-6 showed 22

uncovered transit supportive blocks that if better served would greatly increase the number of

transit supportive commutes covered by RTS service.

The route coverage evaluation focused the analysis to the trip level by analyzing which

commutes could be completed using one route or a single transfer between different routes.

Although most jobs are concentrated around the University of Florida, covering that particular

area does not equate to offering transit to people who work there. For example, the route 1

spans from Butler Plaza to Downtown and goes through the University yet it only covers 270

commutes.

Ultimately, the biggest challenge RTS will face in the future is how to efficiently and

cost-effectively provide service to the 72% of the commutes inside the MTPO boundary that

either have their origin or destination in a non-transit supportive area.