Comparative Analysis of the Multi-modal Transportation Environments in the Northgate and Capitol Hill Urban Centers

Comparative Analysis of the Multi-Modal Transportation Environment in the Northgate
and Capitol Hill Urban Centers

Submitted by:

David Perlmutter
Daniel Rowe

December 8, 2009

URBDP 422: Geospatial Analysis
Professor Marina Alberti, Matt Marsik

Table of Contents
Introduction and Project Summary ................................................................................................. 3
Project Questions ............................................................................................................................ 4
Methodology ................................................................................................................................... 4
Bicycle Metrics ........................................................................................................................... 5
Bike Lane Miles per Road Mile .............................................................................................. 5
Average ADT (Average Daily Traffic) per Bike Lane Mile .................................................. 6
Average Vehicle Speed Limit per Bike Lane Mile ................................................................. 7
Pedestrian Metrics ....................................................................................................................... 7
Diversity of Land Uses in the Pedestrian Environment .......................................................... 7
Number of Living Units within ¼ mile of the Pedestrian Friendly Land Use Cluster per
Acre ......................................................................................................................................... 9
Average Vehicle Speed per Sidewalk Mile .......................................................................... 10
Transit Metrics .......................................................................................................................... 10
Number of Living Units within ¼ mile of a Transit Stop per Square Mile .......................... 10
Average Service Frequency per Route ................................................................................. 11
Average Service Span per Route .......................................................................................... 11
Table 1: Summary of Metrics .................................................................................................... 12
Analysis and Interpretation of Results .......................................................................................... 12
Analysis..................................................................................................................................... 12
Bike ....................................................................................................................................... 13
Walk ...................................................................................................................................... 13
Transit ................................................................................................................................... 15
Limitations ................................................................................................................................ 15
Implications............................................................................................................................... 16
Appendix A: Project Maps............................................................................................................ 18
Appendix B: Data Dictionary ....................................................................................................... 34
Works Cited .................................................................................................................................. 36

Rowe/Perlmutter – Multi-Modal Level of Service 2

Introduction and Project Summary
The Puget Sound is experiencing rapid growth in population and employment, especially in its

urban centers, which have been identified by the Puget Sound Regional Council (PSRC) as areas

to focus this growth. As these areas grow and become denser, it will be critical to maintain high

levels of mobility to ensure the efficient movement of people and goods. It is anticipated that

roadways alone will not be able to meet this additional demand. To create a healthy and

prosperous region, the PSRC urban centers will need to invest in a multi-modal transportation

network, including transit, bike and walk facilities and services. As our centers begin to develop

this network, it will be important to benchmark and measure the success of each investment.

Multi-modal level of service (LOS) is an emerging concept aimed at developing metrics to

measure such investments. Multi-modal LOS metrics are used to evaluate various transportation

modes and impacts. LOS, or quality of service, refers to the speed, convenience, comfort and

security of transportation facilities and services as experienced by users. Employing LOS

measurements will be a valuable exercise for urban centers to track their progress in creating

multi-modal transportation networks to meet the needs of the growing population.

Our research uses GIS analysis to explore different metrics that can be applied to multi-modal

LOS measurements. Our research is not intended to calculate an LOS score or to make definitive

statements about different alternative transportation environments, like some recent studies have

attempted, but it aims to identify and calculate different metrics for alternative modes of

transportation and evaluate the effectiveness of each metric in measuring LOS. This research will

measure qualities and levels of multi-modal transportation service in two different Urban Centers

in Seattle, WA. We used three indicators to measure each alternative mode of transportation in

the Northgate and Capitol Hill Urban Centers. In total, our research has explored nine indicators:


three for transit, three for bike, and three for pedestrian travel. The indicators and methodology

are described below in the Methodology section. The resulting analysis of the metrics enables a

more in-depth comparative analysis of the two urban centers by comparing the multi-modal

environment, as opposed to measuring each mode separately. Our research utilizes metrics

identified in peer-reviewed literature from the transportation planning field. This project aims to

identify the principal differences in the alternative transportation environments of the Capitol

Hill and Northgate Urban Centers, as well as evaluating the effectiveness of the nine metrics we

have employed in our analysis of the bike, pedestrian, and transit infrastructure within these

centers.

Project Questions
Our project questions are the following:

• What is the difference in alternative transportation environments in the Northgate and

Capitol Hill Urban Centers?

• How effective are the nine metrics in determining the alternative transportation

environment?

Methodology
For the purposes of clarity in our methodology, the importance of separating our methodology

into bike, pedestrian, and transit segments was clear from the beginning of this project. Although

some of the literature provided examples of the methods to produce a function that would

synthesize the indicators of all three modes of transportation to create a single figure representing

multi-modal LOS, our research was limited by available data and time and focused on an

exploration of different metrics and their effectiveness in measuring each mode. As a result of

our limited time to complete this research, our analysis focused on three metrics per mode of


transportation to explore a sample of available metrics and their effectiveness in applied settings.

These metrics are described in detail in the following sections.

Bicycle Metrics
Bike Lane Miles per Road Mile
According to a study of Bicycle Level of Service 1, the presence of a bike lane or paved shoulder

was a significant factor in cyclists’ assessment of roadway safety, a key factor constituting

Bicycle LOS. Bike lane classes are defined in the City of Seattle GIS Bicycle Routes Data

Dictionary2 by a hierarchy including Bicycle Path, Bicycle Lane, Urban Connector, and

Neighborhood Connector. These bike lane classes are characterized by the width of the overall

outside travel lane, which includes the bike lane or shoulder width if present 3. The relationship

between bike lane width and LOS can be best expressed by a weighting each bike lane according

to its width. To visually portray the different bicycle lane classes, each class was assigned a

different color in the resulting map symbology. The Bicycle Routes layer was then overlain on

top of the King County Transportation Network layer 4. Using the Statistics feature on each of the

respective Attribute Tables, the sum of Bicycle Lane segment lengths (in miles) of each urban

center was divided by the sum of Road segment lengths (in miles). The relative weights of each

bike lane class were not included in this calculation, because in the reviewed literature there was

no consensus on the extent to which different bike lane classes represented directly proportional

improvements in Bicycle LOS according to their width5. The two urban centers’ Bicycle LOS

1
Pertrisch, T.A., Landis, B.W., Huang, H.F., McLeod, P.S., & Lamb, D. (2007). Bicycle level of service for
arterials. Transportation Research Board, 34-42.
2
Folsom, R. City of Seattle, Department of Transportation. (2009). Bicycle routes shapefile Seattle, WA: Retrieved
from https://wagda.lib.washington.edu/data/geography/wa_cities/seattle/metadata/cd_3/metadata2009/geoguide2.htm
3
Dixon, L.B. (1996). Bicycle and pedestrian level-of-service performance measures and standards for congestion
management systems. Transportation Research Board, 1538, 1-9.
4
Bui, T. King County Department of Transportation, Metro Transit Division, GIS Group. (2009). King county
transportation network (TNET) Retrieved from
http://www5.kingcounty.gov/sdc/Metadata.aspx?Layer=trans_network


can then be compared using a ratio of total bike lane miles per road lane mile, creating a picture

of the distribution and availability of bicycle infrastructure within the urban center.

Average ADT (Average Daily Traffic) per Bike Lane Mile
Traffic volumes have been regularly mentioned as an important factor impacting Bicycle LOS. 5

Generally speaking, LOS literature has indicated an inverse relationship between traffic volumes

and Bicycle LOS 6, as high traffic volumes impede bicyclists’ sense of safety in the traffic

environment. 2006 ADT (Average Daily Traffic) data from SDOT 7 provides each street’s

average daily vehicle volume totals by linear street segments of uneven lengths. The bike lanes

were therefore analyzed by linear segments so that each bike lane segment is assigned a single

ADT value. It was determined that the most useful, easily transferrable linear metric would be to

record the length of each bike lane segment in miles, as most roadway infrastructure is measured

in miles. The bike route shapefile was edited to incorporate ADT by adding a field for ADT and

adding attribute data based on the SDOT ADT information. After selecting all bike lanes with

their assigned ADT value, a metric of “Average ADT per Bike Lane Mile” was created by

aggregating the total ADT values for all bike lane segments and dividing this sum by the

aggregate length (in miles) of all bike lanes in the urban center. The resulting values for the

average ADT per bike lane mile roughly corresponds to the average traffic levels for each street

containing bicycle infrastructure, a key factor of Bicycle LOS.

5
Phillips, R.G. and Guttenplan, M. (2003). Center for Urban Transportation Research. A review of approaches for
assessing multi-modal quality of service. 6(4): 73-81.
6
Sprinkle Consulting. (2007). Bicycle Level of Service Applied Model, pp. 1-9. In this model, increasing the bike
lane width from zero (baseline) to three feet resulted in a 10% improvement in the Bicycle LOS (p. 6). Widening
from zero to five feet increased the LOS by 18%. Future applications of this research could take these metrics into
account.
7
Crunican, G, & Wentz, W.M. City of Seattle, Department of Transportation. (2006). Traffic flow map.


Average Vehicle Speed Limit per Bike Lane Mile
Vehicle speed limits1,3,6 have also been indicated to be an important factor in assessing Bicycle

LOS, though significantly less so than ADT totals 8. Using speed limit data from the King County

Transportation Network layer, each bike lane segment was assigned a single speed limit. Similar

to vehicle traffic volumes in the previous exercise, an aggregate statistic of average speed limit

per bike lane mile was calculated for each urban center.

Pedestrian Metrics
Diversity of Land Uses in the Pedestrian Environment
One crucial step in determining Pedestrian LOS is the identification of land use concentrations

that have a high potential to generate pedestrian travel. Using Anne Moudon’s “Targeting

Pedestrian Infrastructure Improvements” 9 as a guide for our methodology, parcels within each

urban center were selected and grouped into pedestrian-friendly land use clusters based on their

potential to generate pedestrian trips. Rather than use Moudon’s method of using aerial

photography to identify pedestrian-friendly land use clusters, we selected a less time-intensive

and sophisticated method was using each parcel’s current land use data and selecting land uses

identified by Moudon as pedestrian-friendly. Identical to Moudon’s analysis, we selected parcels

that corresponded to medium and high-density residential development, neighborhood retail and

services, and school campuses9. “Neighborhood retail and services” are identified as “retail

stores that cater to daily shopping needs – supermarkets, drugstores, restaurants, cafes, video

stores, dry cleaners, hair and barber shops, and hardware stores – as components of a commercial

8
According to Pertrisch, Landis, et al (2006), 58 study participants considered Traffic Volumes to be the most
significant factor of Bicycle LOS, compared to 17 who thought Traffic Speed was most important (p. 17).
9
Vernez-Moudon, A. (2001). Targeting Pedestrian Infrastructure Improvements: A Methodology to Assist
Providers in Identifying Suburban Locations with Potential Increases in Pedestrian Travel (Research Report T1803,
Task 11: “Pedestrian Infrastructure”). Seattle, WA: Washington State Department of Transportation, p.48.


center that can support walking trips.” 10 The first step of this analysis involved joining the King

County Assessor’s data 11 with the City of Seattle parcel data. This step is necessary because for

pedestrian-friendly land use clusters to be populated, it was necessary to know each parcel’s

current land use as well as zoning designation, information only available in the Assessor’s table.

This point was further articulated in our interview with Chad Lynch of SDOT 12. We then

constructed a hierarchy of pedestrian-friendly land uses identified in the related literature as

being generators of pedestrian trips. These land uses include medium and high-density multi-

family residential, mixed-use development, school campuses, grocery stores, neighborhood retail

services, and post offices. Other literature 13 identified libraries, community centers, churches,

and playgrounds as pedestrian-friendly land uses, although we did not include these land uses

because Moudon’s analysis, which is the most similar to our own, did not include them. Once the

parcels meeting pedestrian-friendly criteria were selected, we exported the pedestrian-friendly

parcels and rasterized them using the current land use designation as the associated data for each

raster cell. Doing so enabled us to analyze pedestrian-friendly land uses within the urban centers

as patches of land in FRAGSTATS. Our next step was to use the Simpson’s Diversity Index

(SIDI) function through FRAGSTATS, which gave a more accurate picture of the diversity of

each urban center’s pedestrian-friendly land uses. SIDI is a measurement of the probability that

two raster cells randomly selected from a sample will be of the same patch type, or land use in

this case, and is valued between zero to one. A greater value of SIDI (approaching a score of

one) means the urban center has a greater number of different land uses and the proportional

10
Vernez-Moudon, A, Hess, P, Snyder, M.C., & Stanilov, K. Washington State Transportation Center, (1997).
Effects of site design on pedestrian travel in mixed-use, medium-density environments (T99034, Task 65). Seattle,
WA: Washington State Department of Transportation, pp. 16-24.
11
King County Department of Assessments, (2009). King county parcel record Retrieved from
http://info.kingcounty.gov/assessor/DataDownload/default.aspx
12
Lynch, Chad. (2009, November 9). GIS Supervisor, City of Seattle Department of Transportation. Interview.
13
Cervero, R, & Duncan, M. (2003). Walking, bicycling, and urban landscapes: evidence from the san francisco bay
area. American Journal of Public Health, 93(9), Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447996/


distribution of those land uses becomes more equitable. Diversity of pedestrian-friendly land use

types is frequently mentioned as a key factor in determining pedestrian LOS10.

Number of Living Units within ¼ mile of the Pedestrian Friendly Land Use
Cluster per Acre
Our next metric allowed us to assess how accessible to residents each pedestrian-friendly land

use cluster was within its respective urban center. Creating a ¼ mile buffer 14 around the

pedestrian-friendly land use cluster showed the number of living units that are within walking

distance of the pedestrian-friendly cluster. To capture all of the residential parcels within the

buffer, the King County Assessor’s tables for residential parcels, including Apartment Complex,

Condo Complex and Units, and Residential Building, were joined with the Seattle Parcel

shapefile. This enabled a selection of parcels intersecting with the buffer and a calculation of the

total living units within the selected parcels. Having this information in GIS also enabled a

classification of living units per parcel, as shown in the corresponding map in the Appendix,

which provides visual identification of the urban form and spatial distribution of living units.

Calculating the density of total living units within walking distance of the cluster to total living

units in the urban center was a useful metric because it provides a measure to contrast the

residential densities of each urban center. While diversity of land uses within each pedestrian

cluster, as measured in the previous metric, is important, the pedestrian-friendly clusters are of

little use to the surrounding urban center if few residents are located within walking distance and

the cluster has effectively little pedestrian service area.9

14
The standard distance cited throughout the literature as the average pedestrians were willing to walk to seek
commercial services or transit.


Average Vehicle Speed per Sidewalk Mile
As most streets within each of our urban centers have abundant sidewalk coverage, the vehicle

traffic volumes were determined to have less significance for pedestrian safety than for bicycle

safety, which a previous bicycle metric addressed. Vehicle Speed, however, has been repeatedly

studied as a major factor related to pedestrian safety and the efficacy of pedestrian infrastructure

improvements. 15 We intersected the vehicle speed limits data provided by King County with the

City of Seattle sidewalks layer to create a Field Statistics mean speed limit for all street segments

with sidewalks. This provided a good comparison of an important pedestrian safety factor

between each urban center.

Transit Metrics
Number of Living Units within ¼ mile of a Transit Stop per Square Mile
The presence of a transit stop near one’s origin and destination is an important factor to whether

an individual will use transit or a personal automobile. This measure of availability assesses how

easily potential passenger can use transit for various kinds of trips. 16 This metric will provide the

number of living units within walking distance (one quarter mile 17) to a transit stop per square

mile in each urban center. This metric was calculated using the same methodology as the

Number of Living Units within ¼ mile of the Pedestrian Friendly Land Use Cluster per Acre

metric, except the buffer polygon was a different shape, as it was produced by creating a one

15
Landis, B.W., Vattikuti, V.R., Ottenberg, R.M., et.al. (2007). Modeling the Roadside Walking Environment: A
Pedestrian Level of Service (Transportation Research Board Paper 01-0511). Lutz, FL: Sprinkle Consulting.
16
Kittelson & Associates, Transit Cooperative Research Program, United States, Transit Development Corporation,
and National Research Council (U.S.). 2003. Transit capacity and quality of service manual. Washington, D.C.:
Transportation Research Board. Pg. 3-3.
17
“Although there is some variation between cities and income groups among the studies represented in the exhibit,
it can be seen that most passengers (75 to 80% on average) walk one-quarter mile (400 meters) or less to bus stops.
At an average walking speed of 3 mph (5 km/h), this is equivalent to a maximum walking time of 5 minutes.”
(Transit capacity and quality of service manual, 2003).


quarter mile buffer around all transit stops in the urban center. This metric will measure the

residential density within walking distance to transit.

Average Service Frequency per Route
How frequent transit service is provided during the day is an important factor in one’s decision to

use transit. 18 The more frequent the service is provided the shorter the wait time for a rider. This

allows more flexibility for customer in his or her trip planning. This metric will measure average

service frequency by route using the following times of day: weekday AM peak, PM peak, mid-

day, evening and night and weekend. Each route serving the urban center (meaning it has a bus

stop inside the urban center boundary) was edited by adding fields for frequency by time of day.

These frequencies were averaged for each route and a total average for the entire urban center

was calculated. This measurement indicates one level of transit service metric for each urban

center, often referred to as headways.

Average Service Span per Route
How long during each day that transit service is provided is also an important factor in one’s

option to using transit as opposed to other modes of transportation. 19 If transit service is not

provided during certain times of the day when people want to ride, transit will not be an option

for them. Thus, increasing the number of hours that service is provided will increase the potential

number of trips taken using transit. Each route serving the urban center was edited by adding

fields for service span, both weekday and weekend. The two spans were averaged for each route

and a total average for the entire urban center was calculated. This metric will measure the

average service span, or hours of day each route provides service to the urban center, per route.

18
Ibid, pg. 3-16.
19
Ibid.


Analysis and Interpretation of Results
Our analysis involved calculating each metric for the Northgate and Capitol Hill urban centers.

We then compared and contrasted the two urban centers based on their multi-modal

transportation environments. One potential further effort to ground-truth the results of our

analysis would be to validate our metrics by comparing our findings to drive-alone rates and

other transportation behavior, as reported by the U.S. Census. We will also compare our findings

to personal observations each urban center. First, it is important to identify limiting factors that

may have impacted the quality of our analysis. We will also reflect on our analysis and its

application to future research in transportation and land use planning.

Analysis
Using the metrics described in the previous section, our research included calculating each

metric within the Northgate and Capitol Hill Urban Centers. Table 1 below summarizes the

results for the nine metrics studied. A discussion of results of each metric will be included in the

following sections.

Table 1: Summary of Metrics
Mode Metric Northgate Capitol Hill
Bike Bike Lane Miles per Road Lane Mile 0.11 0.10
Bike Average ADT per Bike Lane Mile 9709.07 11098.60
Bike Average Vehicle Speed Limit per Bike Lane Mile 29.04 29.23
Walk Diversity of Land Uses in Pedestrian Environment 0.7894 0.7958
Number of Living Units within ¼ mile of the Pedestrian
Walk Friendly Land Use Cluster per acre 12.17 30.04
Walk Average Vehicle Speed per Sidewalk Mile 27.77 27.11
Number of Living Units Within ¼ mile of a Transit Stop per
Transit Acre 10.31 28.54
Transit Average Service Frequency per Route 33.27 27.68
Transit Average Service Span per Route 15.00 15.50


Bike
Overall, the three metrics used to calculate LOS for the bicycle network produced similar results

between the two urban centers. The bike lane miles per road lane mile metric, intended to

calculate the general availability of bicycle lane infrastructure in each urban center, produced

very similar results, with 0.11 in Northgate and 0.10 in Capitol Hill. Although Capitol Hill

contains 3.9 more bike lane miles than Northgate, it also has 39 more road lane miles. This large

difference is due to the density of the street grid in Capitol Hill, including shorter blocks and

more street network connections. One problem with this metric is it does not account for the

quality of the bike lane (see Methods section for the reasons for not including this in element in

the calculation). It also does not account for the option of riding a bike on a road without a bike

lane. The average ADT per bike lane mile metric shows over 1,000 more cars on the road in

Capitol Hill compared to Northgate. Although this indicates busier streets in Capitol Hill,

presenting more opportunities for accidents, it does not account for the difference in number of

activity centers that create bicycle trips. Finally, the average speed limit per bike lane mile also

produced very similar results, with each urban center having an average of approximately 29

miles per hour for vehicles on roads that contain bike infrastructure. This is probably due to the

fact that bike lanes are often cited on roads with lower speed limits to ensure safety of the rider.

A better metric for vehicle speed would be to measure the actual speed traveled by vehicles, not

the posted speed limit. This would require more time and resources, but would show streets that

suffer from speeding vehicles that create dangerous situations for bicyclists.

Walk
Two of the three metrics used to measure LOS for pedestrian infrastructure produced very

similar results and one produced a large difference. The first metric, diversity of land uses in the

pedestrian environment used SIDI to measure the diversity and distribution of pedestrian friendly


land uses in the two urban centers. Using FRAGSTATS to calculate this metric, the results show

similar scores, with 0.7894 in Northgate and 0.7958 in Capitol Hill. This is an interesting result,

as Capitol Hill clearly contains a larger and more robust framework of pedestrian friendly land

uses. This similar scoring presents issues with using SIDI to measure land use diversity on the

urban center scale. This calculation is showing that although Northgate contains a smaller land

use cluster, it is equally diverse and distributed when compared to Capitol Hill. Perhaps a better

metric to calculate the difference in pedestrian friendly land uses would be a combination of

diversity, lot size, sidewalk width, and street connectivity. As mentioned in the Methodology

section, this study was limited from pursing these other metrics, but they could provide options

for future research. A second metric, number of living units within ¼ mile of the pedestrian

friendly land use cluster per acre, resulted in a large difference between the two urban centers.

Capitol Hill resulted in 30.04 living units per acre as opposed to Northgate with 12.17. This

clearly shows the difference in residential density between the two study areas. This metric

provides a valuable indicator to assess the residential population that can access each pedestrian

friendly land use. When viewing the distribution of the residential populations, (see Appendix A

for maps) it is clear that Capitol Hill’s residential population is distributed throughout the urban

center and not in a donut shape like Northgate. Finally, the average vehicle speed per sidewalk

mile metric produced very similar results between the two urban centers, both with an average of

27 miles per hour along streets with sidewalks. Similar to the average speed limit along bike

lanes metric, a better metric for vehicle speed along sidewalks would be to measure the actual

speed traveled by vehicles, not the posted speed limit.


Transit
Two of the three transit metrics used to measure LOS produced different results and one

produced very similar results. The number of living units within ¼ mile of a transit stop per acre

metric showed a large difference in residential density around transit service, with Northgate

having 10.31 living units per acre and Capitol Hill having 28.54 living units per acre. Similar to

the residential density metric for the walk mode, this metric shows that Capitol Hill has many

more people living within walking distance of a transit stop, which means these people will be

more likely to use transit as a mode of transportation. The second metric, average service

frequency per route, resulted in a minor difference in transit service between the two urban

centers, with Northgate having an average frequency of 33.27 minutes and Capitol Hill having an

average frequency of 27.68 minutes. The analysis of transit frequency could be sharpened in

future research by segmenting the Average Frequency per Transit Route metric into morning and

evening peak shifts, when higher transit frequencies are in greater demand by commuters. A

more focused analysis could also be performed on the Average Span per Transit Route metric by

comparing weekday and weekend transit spans in each urban center. New transit developments,

such as Sound Transit’s newly-constructed Central Link Light Rail and numerous bus rapid

transit lines have not been included in this project due to the lack of available data. Future

research on multi-modal level of service should take these new pieces of infrastructure into

account when analyzing Transit Level of Service in their respective urban centers.

Limitations
Several limitations to the metrics used in this project have been identified. First, there was

conflicting literature regarding which metric was most applicable to each mode of transportation.

For instance, the definition of what constituted a “pedestrian-friendly land use” was identified in


Moudon’s work, which was selected as most applicable to this project. However, the

transportation planning field has many other works that identify slightly different applicable land

uses. Asserting with more certainty what constitutes a pedestrian-friendly land use, perhaps by

developing an independent metric to assess a parcel’s pedestrian-friendliness through measuring

the number of trips it generates, is necessary to make our “Diversity of Land Uses in the

Pedestrian Environment” more useful. Transferability of the data also represents a potential

problem area. While it was acceptable in this project to compare multi-modal LOS analyses for

different urban centers within the City of Seattle, making similar comparisons between urban and

suburban areas is more problematic because of the fundamentally different characteristics of the

built environment in these areas. The quality and availability of data was also a limiting factor.

Width of sidewalks and the presence of parked cars were widely identified in the pedestrian LOS

literature as important factors, yet we could not find high-quality data to perform these metrics.

Finally, the time and resources allotted to complete this project limited the complexity of the

analysis we could perform.

Implications
This analysis of multi-modal level of service has many applications in the transportation

planning, real estate, and energy sectors. In transportation planning, public transit service

allocation and upgrades could be determined by examining neighborhoods’ transit LOS and

using one of the metrics identified in this project in assessing which area has the greatest need

for new infrastructure. In the real estate development sector, the design guidelines for parking

requirements in new buildings could potentially be linked to the availability of multi-modal

infrastructure in the immediate vicinity. This could mitigate the problem of over-provision of

parking spaces in new medium and high-density multi-family housing, a factor that has been


identified as contributing to lack of housing affordability in new developments. More broadly, in

the energy sector, it has been widely documented that one important step to reducing greenhouse

gas emissions and curbing single-occupancy vehicle trips is by improving the availability of

multi-modal transportation infrastructure.

In addition to these planning applications, multi-modal LOS has been identified as a

supplemental metric to evaluating transportation concurrency under Washington’s Growth

Management Act (GMA). Currently, roadway LOS, generally roadway capacity, is used as a

metric to determine if transportation infrastructure is adequate to accommodate new trips

generated by proposed new development. While appropriate for some communities, this roadway

concurrency metric often suggests improvements to accommodate more vehicle capacity, not

multi-modal capacity. Communities that have existing multi-modal capacity could benefit from

concurrency metrics that measure transit, bike, and walking facilities. This analysis could use the

GMA transportation concurrency law to foster smart growth in urban centers and potentially help

fund multi-modal infrastructure improvements. Multi-modal LOS provides a new approach to

measuring transportation infrastructure. This measurement will be critical to plan and allocate

resources as our urban centers prepare to accommodate new growth and provide sustainable

transportation solutions.


Appendix A: Project Maps


Appendix B: Data Dictionary

Name Description Type Geometry Coordinate Source
System
Bike
1. Bicycle Location of bike Shapefile Polyline Washington WAGDA – City
Routes lanes in the City of State Plane, of Seattle
Seattle North Zone
2. Street Location of arterial Shapefile Polyline Washington WAGDA – City
Arterials streets in the City State Plane, of Seattle
of Seattle North Zone
3. ADT totals Traffic Flow Map: PDF N/A N/A City of Seattle –
for street ADT (Average SDOT7
segments Daily Traffic) totals
for arterial streets
in the City of
Seattle
4. Speed Limits Speed Limits of Shapefile Polyline Washington WAGDA – King
street segments in State Plane, County
King County, WA North Zone Department of
Transportation,
Metro Transit,
GIS Group
5. Urban Center Urban Center Shapefile Polygon Washington WAGDA – City
boundaries boundaries State Plane, of Seattle
delineated by Puget North Zone
Sound Regional
Council (PSRC)
Walk
1. Sidewalks Location of Shapefile Polyline Washington WAGDA – City
sidewalks in the State Plane, of Seattle
City of Seattle North Zone
2. King County Parcel data listings Attribute Database N/A King County
Parcel Record of current land uses table file Assessor’s
for King County, Office11
WA
3. Parcels – City Parcel data listing Shapefile Polygon Washington WAGDA – City
of Seattle current land uses in State Plane, of Seattle
the City of Seattle North Zone
4. Speed Limits Speed Limits of Shapefile Polyline Washington WAGDA – King
street segments in State Plane, County
King County, WA North Zone Department of
Transportation,
Metro Transit,
GIS Group
5. Residential Parcel data listing Attribute Database N/A King County
Living Units number of living table file Assessor’s
units in each Office11


residential parcel in
King County, WA
Sound Regional
Council (PSRC)
Transit
1. Transit Transit routes in Shapefile Polyline Washington WAGDA – King
Routes King County, WA State Plane, County
North Zone Department of
Transportation,
Metro Transit,
GIS Group
2. Transit Stops Transit stop Shapefile Polygon Washington WAGDA – King
locations in King State Plane, County
County, WA North Zone Department of
Transportation,
Metro Transit,
GIS Group
3. King County Parcel data listings Attribute Database N/A King County
Parcel Record of current land uses table file Assessor’s
for King County, Office11
WA
4. Transit Route Frequency of Attribute Database N/A King County
Frequency transit service in table file Department of
Data King County, WA Transportation,
Metro Transit
5. Transit Route Span of transit Attribute Database N/A King County
Span Data service in King table file Department of
County, WA Transportation,
Metro Transit
Sound Regional
Council (PSRC)


Works Cited
Bui, T. King County Department of Transportation, Metro Transit Division, GIS Group. (2009).
King county transportation network (TNET) Retrieved from
http://www5.kingcounty.gov/sdc/Metadata.aspx?Layer=trans_network
Cervero, R, & Duncan, M. (2003). Walking, bicycling, and urban landscapes: evidence from the
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