Comparative Analysis of the Multi-Modal Transportation Environment in the Northgate
                           and Capitol...
Table of Contents
Introduction and Project Summary ..........................................................................
Introduction and Project Summary
The Puget Sound is experiencing rapid growth in population and employment, especially in ...
three for transit, three for bike, and three for pedestrian travel. The indicators and methodology

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

These metrics are de...
can then be compared using a ratio of total bike lane miles per road lane mile, creating a picture

of the distribution an...
Average Vehicle Speed Limit per Bike Lane Mile
Vehicle speed limits1,3,6 have also been indicated to be an important facto...
center that can support walking trips.” 10 The first step of this analysis involved joining the King

County Assessor’s da...
distribution of those land uses becomes more equitable. Diversity of pedestrian-friendly land use

types is frequently men...
Average Vehicle Speed per Sidewalk Mile
As most streets within each of our urban centers have abundant sidewalk coverage, ...
quarter mile buffer around all transit stops in the urban center. This metric will measure the

residential density within...
Analysis and Interpretation of Results
Our analysis involved calculating each metric for the Northgate and Capitol Hill ur...
Bike
Overall, the three metrics used to calculate LOS for the bicycle network produced similar results

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

similar scores, with 0.789...
Transit
Two of the three transit metrics used to measure LOS produced different results and one

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

transportation planning field has many...
identified as contributing to lack of housing affordability in new developments. More broadly, in

the energy sector, it h...
Appendix A: Project Maps




Rowe/Perlmutter – Multi-Modal Level of Service   18
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Appendix B: Data Dictionary

Name               Description             Type          Geometry   Coordinate     Source
   ...
residential parcel in
                   King County, WA
6. Urban Center    Urban Center            Shapefile   Polygon   ...
Works Cited
Bui, T. King County Department of Transportation, Metro Transit Division, GIS Group. (2009).
        King coun...
Rowe/Perlmutter – Multi-Modal Level of Service   37
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Comparative Analysis of the Multi-modal Transportation Environments in the Northgate and Capitol Hill Urban Centers

  1. 1. 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
  2. 2. 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
  3. 3. 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: Rowe/Perlmutter – Multi-Modal Level of Service 3
  4. 4. 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 Rowe/Perlmutter – Multi-Modal Level of Service 4
  5. 5. 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 Rowe/Perlmutter – Multi-Modal Level of Service 5
  6. 6. 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. Rowe/Perlmutter – Multi-Modal Level of Service 6
  7. 7. 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. Rowe/Perlmutter – Multi-Modal Level of Service 7
  8. 8. 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/ Rowe/Perlmutter – Multi-Modal Level of Service 8
  9. 9. 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. Rowe/Perlmutter – Multi-Modal Level of Service 9
  10. 10. 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). Rowe/Perlmutter – Multi-Modal Level of Service 10
  11. 11. 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. Rowe/Perlmutter – Multi-Modal Level of Service 11
  12. 12. 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 Rowe/Perlmutter – Multi-Modal Level of Service 12
  13. 13. 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 Rowe/Perlmutter – Multi-Modal Level of Service 13
  14. 14. 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. Rowe/Perlmutter – Multi-Modal Level of Service 14
  15. 15. 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 Rowe/Perlmutter – Multi-Modal Level of Service 15
  16. 16. 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 Rowe/Perlmutter – Multi-Modal Level of Service 16
  17. 17. 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. Rowe/Perlmutter – Multi-Modal Level of Service 17
  18. 18. Appendix A: Project Maps Rowe/Perlmutter – Multi-Modal Level of Service 18
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  34. 34. 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 Rowe/Perlmutter – Multi-Modal Level of Service 34
  35. 35. residential parcel in King County, WA 6. Urban Center Urban Center Shapefile Polygon Washington WAGDA – City boundaries boundaries State Plane, of Seattle delineated by Puget North Zone 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 7. Urban Center Urban Center Shapefile Polygon Washington WAGDA – City boundaries boundaries State Plane, of Seattle delineated by Puget North Zone Sound Regional Council (PSRC) Rowe/Perlmutter – Multi-Modal Level of Service 35
  36. 36. 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 San Francisco bay area. American Journal of Public Health, 93(9), Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447996/ Crunican, G, & Wentz, W.M. City of Seattle, Department of Transportation. (2006). Traffic flow map. Dixon, L.B. (1996). Bicycle and pedestrian level-of-service performance measures and standards for congestion management systems. Transportation Research Board, 1538, 1-9. 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/metada ta2009/geoguide2.htm King County Department of Assessments, (2009). King county parcel record Retrieved from http://info.kingcounty.gov/assessor/DataDownload/default.aspx 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. 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. Lynch, Chad. (2009, November 9). GIS Supervisor, City of Seattle Department of Transportation. Interview. 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. 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. Sprinkle Consulting. (2007). Bicycle Level of Service Applied Model, pp. 1-9. 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. 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. Rowe/Perlmutter – Multi-Modal Level of Service 36
  37. 37. Rowe/Perlmutter – Multi-Modal Level of Service 37

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