DATA PROJECT SUMMARY AND RECOMMENDATIONS - Spokane ...
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Spokane Regional Transportation Council DATA PROJECT SUMMARY AND RECOMMENDATIONS STAKEHOLDER REVIEW DRAFT Report │ October 5, 2020 PREPARED FOR: SPOKANE REGIONAL TRANSPORTATION COUNCIL SUBMITTED BY: 55 Railroad Row RSG White River Junction, VT 05001 802.295.4999 IN COOPERATION WITH: www.rsginc.com DKS ASSOCIATES, INC. AND PLANGINEERING, LLC
Spokane Regional Transportation Council DATA PROJECT SUMMARY AND RECOMMENDATIONS CONTENTS 1 INTRODUCTION ................................................................................ 1 2 EXISTING DATA AND TOOLS ......................................................... 3 2.1 CURRENT DATA ....................................................................................3 2.2 CURRENT TOOLS .................................................................................5 2.2.1 LAND USE FORECASTING METHODOLOGY ..........................5 2.2.2 TRAVEL DEMAND MODEL ......................................................11 2.2.3 SCENARIO PLANNING ............................................................16 2.2.4 CURRENT PERFORMANCE MEASURES AND ABILITY OF CURRENT TOOLS TO ADDRESS THEM ........................16 2.2.5 POTENTIAL FUTURE PLANNING REQUIREMENTS AND NEEDS .........................................................................................24 3 LITERATURE REVIEW.................................................................... 26 3.1 SCENARIO PLANNING TOOLS AND PROCESSES ...........................26 3.2 NEW DATA SOURCES AND METHODS OF DATA COLLECTION .......................................................................................28 3.3 STATE OF THE PRACTICE AND INNOVATIONS IN TRAVEL DEMAND FORECASTING....................................................................29 4 STAKEHOLDER ENGAGEMENT ................................................... 30 4.1 STAKEHOLDER ENGAGEMENT PROCESS ......................................30 4.1.1 STAKEHOLDER QUESTIONNAIRE .........................................30 4.1.2 STAKEHOLDER LISTENING SESSIONS ................................31 4.1.3 INTERACTIVE WEBSITE .........................................................31 4.2 SUMMARY OF KEY STAKEHOLDER THEMES ..................................32 4.2.1 TRAVEL MODEL IMPROVEMENTS ........................................32 4.2.2 LAND USE ANALYSIS..............................................................33 i
4.2.3 TRAVEL BEHAVIOR SURVEYS AND PASSIVE DATA ...........33 4.2.4 IMPROVED REGIONAL DATA COORDINATION ....................34 4.2.5 SCENARIO PLANNING ............................................................35 5 PHASE II OPTIONS AND RECOMMENDATIONS ......................... 36 5.1 LAND USE FORECASTING .................................................................36 5.1.1 LAND USE MODELS OVERVIEW ............................................36 5.1.2 LAND USE ANALYSIS TOOL REQUIREMENTS .....................38 5.1.3 LAND USE MODEL APPROACHES.........................................40 5.1.4 LAND USE MODEL DEVELOPMENT PROCESS ....................42 5.2 HOUSEHOLD TRAVEL SURVEY.........................................................45 5.2.1 WHAT IS A HOUSEHOLD TRAVEL SURVEY? .......................45 5.2.2 SRTC’S 2005 HOUSEHOLD TRAVEL SURVEY ......................46 5.2.3 RECENT HOUSEHOLD TRAVEL SURVEY EXAMPLES...........................................................................................46 5.2.4 HOUSEHOLD TRAVEL SURVEY RECOMMENDATIONS FOR SRTC .....................................................47 5.2.5 MEDIUM & LONG-TERM RECURRENT SURVEY PROGRAM OPTIONS ..........................................................................53 5.2.6 HOUSEHOLD TRAVEL SURVEY BUDGET AND TIMELINE ESTIMATES ........................................................................55 5.3 PASSIVE DATA PROCESSING AND ANALYSIS ................................55 5.3.1 PASSENGER DATA .................................................................56 5.3.2 PASSIVE FREIGHT DATA .......................................................63 5.4 TRANSIT ON-BOARD SURVEY...........................................................65 5.5 TRAVEL DEMAND MODEL UPDATE ..................................................66 5.5.1 RECOMMENDED MODEL DEVELOPMENT ACTIVITIES ..........................................................................................66 5.5.2 MODEL DEVELOPMENT TASKS.............................................69 5.6 STRATEGIC MODEL ............................................................................70 5.6.1 USES FOR AN SRTC STRATEGIC MODEL ............................70 5.6.2 TYPES OF QUESTIONS ANALYZED ......................................71 5.6.3 STRATEGIC MODEL ADVANTAGES & DISADVANTAGES ...............................................................................72 5.6.4 STRATEGIC MODEL DESIGN AND CALIBRATION ................73 5.6.5 STRATEGIC MODEL DEVELOPMENT AND APPLICATION TASKS .........................................................................74 5.6.6 BUDGET AND TIMELINE ESTIMATES: STRATEGIC MODEL .................................................................................................74 5.7 REGIONAL TRAFFIC COUNT CLEARINGHOUSE ..............................75 5.7.1 TRAFFIC COUNT CLEARINGHOUSE OPTIONS FOR SRTC .. .................................................................................................75 5.7.2 REGIONAL COUNT PROTOCOLS ..........................................76 5.7.3 BUDGET ESTIMATE: TRAFFIC COUNT CLEARINGHOUSE ...............................................................................77 5.8 ONLINE DATA HUB .............................................................................77 ii
5.8.1 ONLINE DATA HUB DEVELOPMENT PROCESS ...................81 5.8.2 BUDGET ESTIMATE: ONLINE DATA HUB ..............................82 6 CONCLUSIONS AND NEXT STEPS ............................................... 83 LIST OF FIGURES FIGURE 1. SRTC TRANSPORTATION ANALYSIS DISTRICTS ..................................... 10 FIGURE 2: TRAVEL DEMAND MODEL TRANSPORTATION ANALYSIS ZONE (TAZ) MAP ............................................................................................................... 12 FIGURE 3: HORIZON 2040 EVALUATION TOOL CRITERIA ......................................... 18 FIGURE 4. SCENARIO AND POLICY INPUTS FOR INTEGRATED LAND USE AND TRAVEL MODELS .......................................................................................... 37 FIGURE 5: EXAMPLE BRANDED POSTCARD MAILED TO INVITED HOUSEHOLDS ........................................................................................................ 49 FIGURE 6: RSG’S PASSIVELY COLLECTED DATA PROCESSING PIPELINE ............ 57 FIGURE 7: RSG PROVIDES A TRANSPARENT, ‘CLEAR BOX’ SOLUTION FOR PROCESSING PASSIVELY COLLECTED DATA ........................................... 59 FIGURE 8: RSG'S ONLINE PASSIVE DATA VISUALIZATION PLATFORM .................. 62 FIGURE 9: TABLET PC ON-BOARD SURVEY INSTRUMENT EXAMPLE (SANDAG) ............................................................................................................... 66 FIGURE 10: PIMA ASSOCIATION OF GOVERNMENTS VOLUME-DELAY FUNCTION............................................................................................................... 67 FIGURE 11: OREGON METRO COST-BENEFIT TOOL EXAMPLE ............................... 68 FIGURE 12: STRATEGIC MODEL INPUTS.................................................................... 73 FIGURE 13: PSRC REGIONAL DATA PROFILE (PUGET SOUND EXAMPLE) ............ 79 FIGURE 14: MTC VITAL SIGNS (SAN FRANCISO BAY AREA EXAMPLE) ................. 79 FIGURE 15: SANDAG DATA SURFER (SAN DIEGO REGION EXAMPLE) ................. 80 FIGURE 16: SFCTA TNCS TODAY (SAN FRANCISCO COUNTY EXAMPLE).............. 80 FIGURE 17: BOSTON REGION INTERACTIVE PERFORMANCE DASHBOARD EXAMPLE ........................................................................................ 81 LIST OF TABLES TABLE 1. SRTC LAND USE CATEGORIES BY TYPE .................................................... 7 TABLE 2: FEDERAL AND REGIONAL PERFORMANCE MEASURES .......................... 17 TABLE 3: CONGESTION MANAGEMENT PROCESS (CMP) CORRIDOR PERFORMANCE MEASURES ................................................................................ 19 TABLE 4: VEHICULAR TRAVEL TIME LEVEL-OF-SERVICE (INTERRUPTED FLOW CORRIDORS) ............................................................................................... 23 TABLE 5: PLANNING AND POLICY QUESTIONS THAT AN HOUSEHOLD TRAVEL SURVEY CAN AND CANNOT ANSWER.................................................. 45 TABLE 6: EXAMPLE REGIONAL RECURRENT HOUSEHOLD TRAVEL SURVEY PROGRAM: PSRC REGION .................................................................... 53 TABLE 7: EXAMPLE STATEWIDE RECURRENT HOUSEHOLD TRAVEL SURVEY PROGRAM: OHIO DOT ........................................................................... 54 TABLE 8: PASSIVE DATA OPTIONS ............................................................................ 60 TABLE 9: PASSIVE FREIGHT DATA OPTIONS ............................................................ 63 TABLE 10: STRATEGIC MODEL EVALUATION TOPICS ............................................. 72 TABLE 11: TRAFFIC COUNT CLEARINGHOUSE OPTIONS ........................................ 76 TABLE 12: LINKS TO EXAMPLE ONLINE DATA HUBS ............................................... 78 TABLE 13: POTENTIAL PHASE II ACTIVITIES AND BENEFITS .................................. 83 TABLE 14: POTENTIAL PHASE II ACTIVITIES AND ESTIMATED COSTS .................. 85 iii
DATA Project Summary and Recommendations 1 INTRODUCTION The Spokane Regional Transportation Council (SRTC) and other transportation planning agencies face significant challenges in analyzing the benefits of transportation projects and policies. The coming decade(s) will bring substantial changes in travel behavior and patterns, and it is likely that the pace of these changes will continue to pick up speed. Travel behavior is changing due to a plethora of factors in the transportation industry such as new modes (e.g., ride-hailing (Uber, Lyft), scooter share, bikeshare, etc.), trip substitution or shifting due to telecommuting and eCommerce (e.g., goods delivery, food/meal delivery, Amazon lockers), new types of vehicles (EVs), and the early, evolving stages of vehicle automation. In parallel, demographic shifts are multifaceted and will also directly impact travel behavior changes. Younger generations indicate different preferences such as delaying obtaining driver licenses, marrying later, and more readily adopting new travel modes. Likewise, the baby boomer generation faces retirement, with an increased desire to maintain autonomy over their travel behavior and age-in-place. And of course, there are emerging changes in travel behavior or demographic behavior that we do not yet anticipate but we will want to have data to understand changes as they happen. Data and tools available for transportation planning by metropolitan planning organizations (MPOs) must evolve to meet these needs. While network-based models such as SRTC’s current four-step travel demand model will continue to play an important role in infrastructure planning and design, other tools such as strategic models, accessibility quantification, and visualization software are increasingly important to help understand the risk and uncertainty of different transportation policies and investments, and to communicate benefits to stakeholders and the public. Web visualization, in particular, joins information from disparate sources such as surveys, passive data, and the travel model, and presents it to planners and the public in an accessible and clear way. Fortunately, the same technology that is disrupting travel behavior is also providing access to new data sources that offer insights into travel like never before. We are now collecting household travel survey data with GPS-enabled smartphone technology, reducing respondent burden and providing multiple days per respondent at the same or lower costs, and with greater accuracy, than surveys conducted just a few years ago. Passively collected data from a multitude of smartphone apps provides spatial detail and sample rates that were previously unimaginable. And with a rapidly changing transportation landscape, agencies are increasingly adopting a data collection program that includes both cross-sectional and continuous data sources to keep planning tools current and monitor performance over time. In 2019, SRTC initiated the DATA Project (Data Applications for Transportation Analysis) with several objectives in mind: 1
Spokane Regional Transportation Council • Improve confidence in data and information used for transportation decision-making. • Help align regional data and tools with member agency planning needs. • Increase stakeholder agency input into existing tools, such as the regional travel demand model, and development of potential new tools; and • Look for innovative ways to analyze and respond to emerging transportation trends. A team led by RSG was selected to perform this project, and work began in early 2020. The project is organized using a 'design build' approach; the first phase of the project includes an analysis of SRTC's current data and toolset and their ability to address current and potential future planning needs, a review of relevant literature, and stakeholder listening sessions. These activities culminate in recommendations for investments in data and tools to be implemented in the second phase of the project. The purpose of this document is to inform a discussion with SRTC and member agencies that will lead to a prioritization of activities to fund in the implementation phase of the project. This report describes the results of the first phase of the DATA project. In the next section, we describe the current data, tools, and resources available to SRTC and partner agencies for regional transportation planning. We also discuss potential future planning needs in Section 2. Next, we review relevant literature pertaining to regional planning efforts and examples of data and tools from cohort agencies as well as federal guidance. We then summarize the findings from the stakeholder listening sessions held in late spring and summer 2020. Finally, we provide a description of potential data collection and tool development or enhancement activities, as well as the costs and benefits of each potential activity. 2
DATA Project Summary and Recommendations 2 EXISTING DATA AND TOOLS Project team members met with Spokane Regional Transportation Council (SRTC) staff on the afternoon of March 2, 2020, to begin the information gathering stage of the project. Objectives of this first site visit included: • Understand current data, tools, and methods SRTC uses to: o Manage performance of the regional transportation network o Evaluate transportation and land-use policies and investments o Collect and process travel demand, land use and transportation infrastructure data • Understand SRTC’s current performance measures and the ability of current tools to address them • Understand potential future planning requirements and needs (performance measures, new modes, etc.) The following section documents the findings of the initial meeting and a review of documentation provided by SRTC to the project team. 2.1 CURRENT DATA Following is a summary of current data maintained by SRTC for transportation planning and performance assessment. Household Travel Survey. Travel behavior surveys are important datasets for transportation planning. They provide information on local trip rates, trip lengths and origin/destination patterns, and modal distributions, and are the primary source of information for calibration of the regional travel demand model. The region’s most recent household travel behavior survey was performed in 2005 and used to update the travel model shortly thereafter. Given changes in the quantity and spatial distribution of regional households and employment 1, changes in travel behavior due to e-commerce and telecommuting, the emergence of new modes of transportation such as Transportation Network Companies, and other trends, the 2005 travel survey information is probably out of date. Transit On-Board Survey. Spokane Transit Authority (STA) regularly conducts transit rider surveys. A Title VI Rider Survey was last conducted in October 2018 2, and contains data on socio-demographic attributes of transit riders, transit fare used, and transit access mode. STA 1 Spokane County population in 2005 was approximately 440k. Population in 2019 was approximately 523k, an increase of approximately 19%. 2 see STA Title VI Rider Survey, Robinson Research, 2018. 3
Spokane Regional Transportation Council also conducted a community perception survey across households in the STA service area in 2019. This survey captured public opinions about transportation issues such as ranking transportation issues in the region, satisfaction with transit service, and frequency of transit use. STA operates less than 50 fixed routes and therefore is not subject to the more stringent federal reporting requirements of larger transit agencies. A comprehensive ridership survey including trip origin, destination, purpose, and other information has not been conducted 3, but would provide additional useful information upon which to calibrate the regional travel demand model and use as a basis for decision-making. Freight Data. The 2040 Regional Transportation Plan summarized commodity flow data from an Inland Pacific Hub (IPH) Regional Freight Profile report that used Global Insight TRANSEARCH data for 2007, and forecasts for 2017 and 2027. SRTC has obtained American Transportation Research Institute (ATRI) data and is summarizing the data for use in corridor- level analysis. SRTC is planning to compare ATRI disaggregate freight data to WSDOT’s freight corridor volumes. Staff are not sure if WSDOT would accept the ATRI numbers, but a comparison would indicate where truck volume counts might be needed. Truck classification counts are not centralized at WSDOT. WSDOT has a system for designating freight corridors which is often used to determine grant eligibility. However, freight volume data used for the corridor designations is often dated and SRTC is unable to confirm its accuracy. Traffic Counts. For previous model updates, SRTC coordinated a regional traffic count program which provided consistently collected and formatted traffic volume data for state and local roadways that could be used for model validation. Traffic count coordination was suspended in 2009 as funding resources became an issue. Also, at that time SRTC had discussed revisiting their screen line approach to model validation, potentially changing how and where traffic counts would be needed. WSDOT continues to provide good count data for their facilities, collected consistently from year to year. Count data for local roadways is less consistent. Local counts are done at different times, with varying levels of detail. Since local agencies do not use a common format when providing their data to SRTC, considerable manual processing is required to use the local data. Bicycle and Pedestrian Data. As part of the WSDOT statewide bicycle and pedestrian documentation project, SRTC has access to eleven permanent counters. The counters track hourly, daily an annual directional data. SRTC uses annual trends to track bicycle and trail usage. Also, using an inventory process, SRTC tracks the annual amount of bicycle and trail facilities that are added to the regional system each year. 3An example of a regional comprehensive transit on-board survey conducted for a relatively small transit operator is available here (for Rogue Valley Transit District): https://www.rvtd.org/Files/2014_Pass_Survey_Final_Report.pdf 4
DATA Project Summary and Recommendations Transportation Network Company (TNC) data. SRTC does not know of any regional or local policies or operating agreements with ride hailing services that require the TNCs to provide data. E-Bike/E-Scooter Data. E-bikes and e-scooters are emerging modes of transportation. Spokane’s Wheelshare program, operated by Lime, ran for seven months in 2019, from May to November. Through mid-November, riders logged 643,000 miles and 581,000 individual trips on the shared bicycles and scooters. This represents approximately 3,000 daily miles and 2,700 daily rides. The City of Spokane's agreement with Lime requires them to make their data available to the City. SRTC could request the aggregated data from the City if SRTC is interested in accounting for e-scooter use in travel demand models or in performance monitoring. Passive Data Opportunities. SRTC is interested in leveraging passive data if it is cost- effective to purchase and validate. Passive data (Streetlight) was used recently for a WSDOT study of the North Spokane Corridor, and has also been secured by Fehr and Peers for SRTC’s two ongoing corridor studies. The Spokane Regional Transportation Management Center (SRTMC) has been installing Bluetooth traffic counters at select locations around the region. These data are not currently being used by SRTC. Data Management. SRTC has access to numerous existing data sets; however, often similar types of data are collected and stored in different formats and are not centrally available to SRTC staff or partner agencies. 2.2 CURRENT TOOLS 2.2.1 Land Use Forecasting Methodology SRTC estimates base-year and future-year quantities of land use at a Transportation Analysis Zone level, including housing units, employment by type, and college/university enrollment. This data is used in the travel demand model and for various performance measures. The current method 4 and proposed future method 5 are summarized below. SRTC updates future land use information in the model every four years to correspond with the MTP update. 2.2.1.1 Current Land Use Forecasting Method The current base year for land use data is 2015, and forecasts are available for 2040. In 2016, the SRTC Policy Board adopted the medium range population forecast developed by Washington State’s Office of Financial Management (OFM) for 2037 and 2040. The employment forecasts had higher growth rates than the population forecasts, so SRTC staff adjusted the employment forecast to reflect the lower population forecast but maintained the 4 Appendix B, Horizon 2040 Land Use Documentation and Planning Assumptions for Spokane Metropolitan Transportation Plan 5 Spokane Regional Transportation Council, Land Use Forecasting Methodology 5
Spokane Regional Transportation Council allocation shares of this growth to local jurisdictions. These employment forecasts were adopted by the SRTC Policy Board along with the population forecasts in October 2016. Each jurisdiction in Spokane County is responsible for developing a land quantity analysis (LQA) report. This provides quantitative information regarding the theoretical ability of existing urban areas to accommodate additional residential and non-residential growth. The LQA is used to provide land capacities as one element of the Urban Growth Area (UGA) determinations. The analysis methodology for LQA was adopted by the Steering Committee of Elected Officials in 1995 amended in 2017 (Board of County Commissioners Resolution 17-0958). The LQA produces the following data items for use in land use planning and forecasting: • total number of existing platted lots in cities, towns, and urbanized county areas • total number of lots in approved preliminary plats in cities, towns, and urbanized county areas broken down by year of approval and sunset date for the preliminary plat approval • total number of approved, but un-built, multi-family units in cities, towns, and urbanized county areas • total areas of vacant commercial and industrial land, sorted according to parcel size ranges (i.e.: less than .25 acre; .25 acre to 1 acre; 1 acre to 5 acres; 5 acres to 10 acres; etc.) • total acres of unplatted land available for development, sorted according to generalized existing zoning categories • future capacity projections, based upon high, medium, and low-density scenarios SRTC maintains base and future year land use data in 12 categories, as shown in Table 1. Various population data sources were used to develop the data, including the 2010 Census, the Office of Financial Management’s (OFM) Small Area Estimates Program 6 (SAEP) and local jurisdictions’ residential building permits. The allocations of population growth by jurisdiction were derived from the OFM medium forecast. The SAEP estimates are not the official state population estimates used for revenue distribution and program administration but are derived from the 2000 and 2010 Census data and provided as a consistent set of population and housing data for Washington. Housing unit vacancy rates by TAZ were applied to estimate occupied housing units for each TAZ. 2010 countywide vacancy rates are applied to any new housing units to estimate occupied housing units for 2015 and forecasts to 2040. 6https://www.ofm.wa.gov/washington-data-research/population-demographics/population- estimates/small-area-estimates-program 6
DATA Project Summary and Recommendations TABLE 1. SRTC LAND USE CATEGORIES BY TYPE Land Use Land Use Land Use Description Number Type Measurement LU1 Population Housing Units Single-family, duplex, triplex, manufactured or mobile home LU2 Population Housing Units Four our more residential units on a single parcel LU3 Other Rooms/campsites Hotel, motel, or campsite LU4 Employment Employees Agriculture, forestry, mining, industrial, manufacturing, and wholesale LU5 Employment Employees Retail trade (non-CBD) LU6 Employment Employees Services and offices LU7 Employment Employees Finance, insurance, and real estate services (FIRES) LU8 Employment Employees Medical LU9 Employment Employees Retail trade (CBD) LU10 Other Students College and university commuter students LU11 Employment Employees Education employees (K–12) LU12 Employment Employees Education employees (college and university) Washington State’s Employment Security Department (ESD) was the primary source for base year employment, by location and industrial classifications (NAICS codes). SRTC staff adjusted employment locations for jobs reported at corporate or organizational headquarters to assign employment to multiple locations across the county. Employment forecasts by jurisdiction were developed through a historical (1995, 2000 and 2010) assessment of growth trends compared to the same assessment from the U.S. Census Bureau program for Local Employment Dynamics (LED) in the same timeframe. These two sources provided consistent employment trends. The allocations of employment growth by jurisdiction reflect the same distribution adopted in 2013 but were scaled down to reflect the lower population forecast in the new future-year OFM data. SRTC staff consulted the commercial and industrial LQA report and determined that there is "excess capacity" for both commercial and industrial acreage. As a result, capacity was not a constraint for commercial and industrial employment. Hotel, motel, and other temporary accommodations were derived from Washington State Department of Health (DOH) on transient accommodations supplemented by staff research. SRTC staff applied a flat rate of growth and then adjusted this growth rate for fast growing jurisdictions (Cheney, Airway Heights, Liberty Lake, unincorporated Spokane County and Deer Park). Following this analysis, SRTC contacted Kalispel and Spokane Indian Tribes resulting in 600 additional rooms added to Airway Heights. College and university commuter students were estimated by contacting the higher education establishments in Spokane County and subtracting resident student populations from total enrollment to produce commuter students for each higher education establishment. SRTC researched each university and college Master Plan to apply their annual growth rates and contacted those institutions where Master Plans were not available. 7
Spokane Regional Transportation Council The final step in the current land use forecasting method is to allocate growth to TAZs. SRTC staff asked local jurisdictions to assist in distributing the future growth to TAZs. Many jurisdictions used their LQA to assist in distributing their population growth and housing units and then SRTC applied vacancy rates by TAZ. 2.2.1.2 Proposed Land Use Forecasting Method SRTC recently proposed (and the SRTC Board adopted) a revised methodology for forecasting future land-use. The proposed base year is 2019 (from 2015 in the current approach) and the forecast year is 2045 (from 2040 in the current approach). This proposed land use forecasting method retains the countywide control totals and accounts for recent and planned developments directly. Changes in the approach to determine capacity and distribute growth to TAZs are noted in the forecasting approach discussion below. Population Forecasting. The population forecast is developed in four steps: 1. Establishing the countywide population control from the 2017 Growth Management Act county projection. 2. Determining the population capacity through parcel-level land quantity analysis (LQA) data from each jurisdiction. SRTC does not directly apply a market factor to calculate capacity. Rather, the current approach reduces growth rates as developable land decreases. One example of an LQA for the City of Spokane 7 describes the methods and results from estimating the amount of land available and the capacity of that land to support residential growth. 3. Accounting for recent and planned development that has occurred, has been approved, or is in process. SRTC obtains any changes from local jurisdiction staff prior to distributing growth to TAZs. 4. Distributing population growth to TAZ using a logistic growth model. This is a departure from the current approach, where population growth was allocated to TAZs by local jurisdictions. This logistic growth model reduces growth rates as the population approaches capacity. This is done by identifying the theoretical unconstrained growth rate (r–max) of the population (P) and reducing it as capacity (K) decreases. R–max is determined by fitting the logistic growth equation to the geography’s historical growth. The following formula is used to determine a given geography’s growth rate: ℎ = − max(1 − ) Employment Forecasting. The employment forecast also has four steps: 1. Establishing the countywide employment control total to follow the population forecast. This is calculated to maintain the current population-to-employment ratio. 7https://static.spokanecity.org/documents/projects/mayors-housing-quality-task-force/additional- materials/2015-land-quantity-analysis-result-and-methodology.pdf 8
DATA Project Summary and Recommendations 2. Determining employment sector growth allocated for eight sectors based on the ESD’s long-term growth projections for Spokane County. Each sector is allocated a share of growth based on the ESD’s long-term occupational projections for Spokane County. These forecasts are 10-year projections. 3. Accounting for recent and planned development that has occurred, has been approved, or is in process. SRTC obtains any changes from local jurisdiction staff prior to distributing growth to TAZs. 4. Distributing employment growth to TAZs by applying Transportation Analysis District (TAD) (see Figure 1) historical growth rates by sector. These growth rates will be derived from the Census Bureau’s LEHD Origin-Destination Employment Statistics (LODES) dataset from 2002 to 2017. After the growth rates are applied, employment will be adjusted to fit countywide control totals. 2.2.1.3 Strengths and Weaknesses of Current Process In general, the current process appears to meet federal requirements and is in harmony with practices at similarly sized metropolitan planning organizations across the country. The process seems to be transparent and individuals wishing to question aspects of the forecast could readily be shown the data, assumptions, estimates, projections, and forecasts that undergird all points of the process. In short, it appears to be a defensible process that is in keeping with the state-of-the-practice for most small to medium sized MPOs. The current method gives SRTC control, or influence, over every step in developing land use inputs for the travel demand model. The method is well understood and relatively easy to explain to concerned stakeholders. Documenting the current method can be accomplished in a concise document such as Appendix B: Horizon 2040 Land Use Documentation and Planning Assumptions of the Horizon 2040 Spokane Metropolitan Transportation Plan. The current method facilitates input from various levels of government, concerned citizens, and other stakeholders who can contribute at multiple stages and multiple levels of the forecast. This type of process often yields a high degree of support from affected agencies and stakeholders. The final advantage of the current method is cost. Given that much of the labor to produce the forecast is performed by SRTC staff, rather than consultants or outside experts, costs are comparatively low. The current process has several points of weakness. First, is objectivity. It appears that the current process requires many points of professional judgement on the part of agency and local staff. While a heavy reliance upon professional judgement is not necessarily a weakness, it can generate concern that there is a subjective component to the forecast. Nearly all forecasting processes have a subjective component. Therefore, it is the size of the subjective component and the overall influence on the process that determine to what degree it is or is not a weakness. The current process appears to be a data supported, expert driven approach in which professional judgement seems to play a significant role throughout. 9
Spokane Regional Transportation Council FIGURE 1. SRTC TRANSPORTATION ANALYSIS DISTRICTS 10
DATA Project Summary and Recommendations Second, in the current model there is no accounting of land use capacity. It is very difficult to determine how much future development to assign to each TAZ without some accounting for physical capacity. On the fringe areas where land is plentiful this may be less of a concern. However, infill development is often capacity constrained, and it is difficult to understand how much the TAZ level forecast is affected by a lack of consideration for capacity. This weakness has been addressed in the proposed and adopted methodology. Third, there is no accounting for real estate price. Urban land is subject to market conditions. Market conditions reflect themselves via price. Land price affects where various land uses can and will locate across time and is often a more powerful determinant than is zoning. Developers determine the feasibility of development via a pro-forma calculation in which land price has the greatest variability across time and space. Fourth, there is no consideration for redevelopment. Low value land uses, or aging land uses may have a higher and better use that is obtainable given evolving market and regulatory conditions. If zoning density increases, for example, it may open a location for redevelopment. Fifth, the land use forecast does not change with the analysis of differing transportation-related capital improvements. The purpose of the region’s travel demand model, of which the land use forecast is a part, is to evaluate differing capital improvement investment strategies to determine which set of alternatives bring about the best transportation system performance at the lowest cost. Differing transportation investment strategies impact regional land use as transportation accessibility is a key component of real estate price. A static land use forecast feeding into a dynamic travel demand forecast can create validity issues. Finally, it is difficult for processes such as this to undergo rigorous external evaluation and validation due to the mental model problem. One professional’s mental model of real estate development may be different from another’s and both are likely internally inconsistent. This makes evaluation and validation difficult. 2.2.2 Travel Demand Model 2.2.2.1 Travel Model Description The SRTC travel demand model is a four-step model, implemented in the VISUM software package. A four-step travel model consists of trip generation, trip distribution, mode choice, and assignment. The model was updated and calibrated in 2008 and again in 2010 based on the 2005 household travel survey, as well as some borrowed parameters from other regions. The model steps are described in more detail below. Trip Generation. Cross-classification models are used to estimate trip productions for the following purposes: Home-based Work, Home-Based Retail, Home-Based School, Home-Based College, Home-Based Other, and Non-Home-Based. Trip production models are stratified by household size, household income, and number of workers (number of students is used in place of workers for Home-Based School purpose). Trip attractions are a linear-in-parameters equation whose independent variables include dwelling units, employment by type, and 11
Spokane Regional Transportation Council enrollment. Trip rates are specified for hotel rooms to take visitor travel into account. There are 519 internal transportation analysis zones (TAZs), ranging in size from 0.014 to 187.21 square miles. In addition, there are 12 park-and-ride zones and 34 external zones. TAZ resolution is shown in Figure 2. FIGURE 2: TRAVEL DEMAND MODEL TRANSPORTATION ANALYSIS ZONE (TAZ) MAP 12
DATA Project Summary and Recommendations Note that accessibility does not influence trip generation (except central business district employment is broken out as a separate employment type), and the magnitude or location of non-home-based trips are not influenced by the magnitude or location of home-based trip attractions. The calibration report notes that total trips decreased from 2008 to 2010, possibly due to the update of the worker per household distribution to 2006-2010 American Community Survey data which includes recessionary years starting in 2008. Trip distribution. Gravity models are used for trip distribution, with friction factors specified as continuous non-linear functions that convert auto time to impedance. Peak time is used for Home-Based Work and Home-Based College, and off-peak time is used for all other purposes. Non-motorized travel time and transit time are not considered. All purposes are doubly constrained (match both productions and attractions at the TAZ level). Home-based Work model is segmented by household income groups (4). Mode Choice. Mode choice is implemented as a two-level nested logit model with five modes: drive-alone, shared-ride, walk/bike, walk-transit, and bike-transit. Auto modes are in one nest and transit modes are in another nest; a combined walk/bike mode competes at the multinomial level. Mode choice model parameters are asserted, and the model was calibrated to household survey data. Currently bus is the only transit option in the SRTC region; however, there is a placeholder for light rail transit in the model. It would compete at a nested level with bus under each mode of access nest. Alternative-specific constants and cost parameters for Home-based Work, Retail, and Other models are segmented by household income group. The model relies on traditional assumptions regarding peak period skims: Home-Based Work and Home-Based College models use peak level-of-service and all other purposes rely on off-peak levels of service. For drive-transit, the model uses matrix convolutions to determine the best parking lot for each origin and destination and builds the auto utility from the auto network and the transit utility from the transit network. Assignment. Trip tables are converted from production-attraction format to origin-destination format and factored into four time periods: AM Peak (6am to 9am), Midday (9am to 3pm), PM Peak (3pm to 6pm) and Evening (6pm to 6am). Additional, AM Peak Hour (7am to 8am) and PM Peak Hour (5pm to 6pm) trip tables are prepared and assigned. Auto trip tables are assigned in a multi-class equilibrium assignment using a network balancing based algorithm and conical link volume-delay functions plus TModel node volume-delay functions and/or turn volume-delay functions at select nodes. Transit trips are assigned using a headway-based strategic pathfinder. Non-motorized (walk/bike) trips are not assigned. The model uses method of successive averages for feedback; AM Peak period and Midday period skims are fed back to trip distribution and mode choice and the model is iterated until convergence. Commercial Vehicles and External Trips. There is a commercial vehicle trip purpose in the model system (trip generation, distribution, and assignment). External trips are produced at external stations and attracted to internal zones. WSDOT traffic count data was used for target 13
Spokane Regional Transportation Council trips at external stations. It is not clear from existing documentation what data was used to calibrate these model components. Model Validation. Auto assignment was validated to traffic counts for 22 screenlines. Goodness of fit varies across each screenline. Approximately 10 screenlines are within 10% of observed traffic counts, 6 are within 10-20% of observed traffic and the rest are over 20% off. Percent root mean square error is not reported, nor are comparisons provided by facility type or volume group. Estimated transit ridership is validated against route group by service and sector (shuttles, north, south, east, west). Estimated ridership matches sector boardings well but route level boardings are not reported. Model Outputs. The travel model automatically produces the following outputs and reports: • Auto volumes, congested speeds, and volume-to-capacity ratios by time period on each link • Transit volumes and passenger miles on each route by period (peak and off-peak) • Transit boardings and alightings at each stop by Transport System and period (peak and off-peak) • Auto and transit skim matrices Travel demand model outputs are used to inform many of the key activities undertaken by SRTC, including: • Transportation Air Quality Conformity (Maintenance Plans for CO and PM10) • Metropolitan Transportation Plan (MTP)/Regional Transportation Plan (RTP). • MAP-21 Performance Measures and Targets • Congestion Management Process • Transportation Improvement Program (TIP) • Subarea and corridor studies/plans • Evaluation of member jurisdiction projects (e.g. Interchange Justification Reports, regionally significant road projects, bike/pedestrian mode share, major transit service changes and projects, etc.) 2.2.2.2 Travel Model Peer Review SRTC held a peer review of their travel model in November 2015. The review was sponsored by the Travel Model Improvement Program (TMIP) and culminated in a report that documents the review process and findings/recommendations 8. The panel had the following recommendations: 8Spokane Regional Transportation Council (SRTC) Peer Review, Prepared for U.S. Department of Transportation, Federal Highway Administration, February 2016. 14
DATA Project Summary and Recommendations Land Use Model. The panel did not recommend that SRTC develop a land-use or economic model. However, the panel did recommend that SRTC develop consistent procedures across jurisdictions for developing economic, land-use, and socioeconomic forecasts. SRTC should seek out expert opinion to ensure that the developed procedures are valid. The panelists encouraged SRTC to make use of the State of Washington’s purchased REMI data, but cautioned SRTC to make sure the data units are consistent with what is needed for the SRTC model. SRTC should regularly review local jurisdiction comprehensive plans to ensure that SRTC’s land-use forecasts match-up to local land-use forecasts. SRTC should improve its methodology for developing economic forecasts. Travel Survey and Traffic Count Data. The panel recommended that SRTC move forward with collection of new household travel survey data using a reputable and experienced consultant. They suggested that SRTC should collaborate with local universities on data collection to ensure that university travel is appropriately surveyed. The panel encouraged continued collection of transit on-board survey data and automated passenger counting (APC), as those data sets are an essential resource for model validation. They also suggested that periodic tablet-based surveys should be incorporated into SRTC’s data collection plan. The panel assumed that the current observed traffic count data collection was adequate but stressed the importance of these counts for model validation. The panel suggested obtaining Bluetooth data. Model Updates. The peer review panel made several recommendations to SRTC to improve their travel models: • Trip generation rates should be adjusted intelligently to match observed data and enhanced by the incorporation of special generators such as the airport, university campuses, open spaces, and parkland. • The gravity model should continue to be used for trip distribution. Transit impedances should not be included in trip distribution utilities since adding transit impedances would add additional and unnecessary complexity to the model considering relatively low transit mode share. • Continue use of a nested logit model for mode choice application. They recommended that bike and walk should be split into separate modes, but there is no need to assign these trips. They recommended removal of the light rail placeholder. • Explore traffic assignment algorithms that allow multi-threading capability to reduce computation time and pursuing a tighter convergence threshold. They recommended considering peak one-hour assignments. They recommended improving delay estimation by moving toward a more complete representation of traffic delay, including intersection delay, but retaining the method of successive averages (MSA) as the assignment method. They suggest pursuing Dynamic Traffic Assignment only for addressing non-recurring congestion and episodic railway crossings. The panel 15
Spokane Regional Transportation Council suggested considering a targeted investigation of the region’s bus schedules to see where a headway-based model may fail to adequately measure transfer time. • Implement a modest truck-based freight component that follows the Quick Response Freight Manual (QRFM v2). • Closely relate performance measures to plan objectives and closely relate model outputs (i.e. tangible data) to performance measures. • Review NCHRP 765: Analytical Travel Forecasting Approaches for Project-Level Planning and Design for guidance on model refinement. • Pursue model updates via a reputable consultant who in addition to updating the model should provide training to SRTC, produce detailed documentation on model estimation, calibration, and validation, and develop a sufficient user guide. 2.2.3 Scenario Planning SRTC does not currently have a formal scenario planning tool. Scenario planning was performed as part of the Horizon 2040 regional transportation plan update, but it was implemented as an ad-hoc process where decision-makers and the public were presented with several hypothetical scenarios and asked to discuss them and evaluate them in terms of likely effects. The scenario planning process included two scenarios; the first scenario considered how development could occur and the second considered the potential for operation and maintenance funding. 2.2.4 Current Performance Measures and Ability of Current Tools to Address Them The following section lists various planning efforts undertaken by SRTC and associated performance measures evaluated in each plan. 2.2.4.1 Regional Transportation Plan A key MPO responsibility is the development and update of a metropolitan transportation plan (MTP). The MTP is a long-term blueprint for a region's transportation system. The plan identifies and analyzes transportation needs of the metropolitan region and creates a framework for project priorities. During development of the MTP, planners use a variety of tools to quantify project and policy benefits. SRTC's current MTP is called Horizon 2040 9. Under the FAST Act, MPOs are required to coordinate with state and public transportation providers to establish targets that address federal performance measures. Horizon 2040 lists a number of performance measures (Table 2); some are based on federal requirements while others are based on regional importance. 9Horizon 2040 documents can be found at https://www.srtc.org/horizon-2040/ (accessed on August 25, 2020) 16
DATA Project Summary and Recommendations The regional travel demand model currently produces estimates for a number of the performance measures . SRTC's regional vehicular level-of-service (LOS) is evaluated based on the regional travel demand model. For transit LOS, SRTC evaluates systemwide ridership and for nonmotorized LOS, mode share is analyzed. TABLE 2: FEDERAL AND REGIONAL PERFORMANCE MEASURES GUIDING FEDERAL REGIONAL AVAILABLE PERFORMANCE MEASURE PRINCIPLE REQUIREMENT IMPORTANCE TO USE Truck Travel Time Reliability Index X X Economic Economic Impact on Activity Centers X Vitality Jobs within 30-Minute Commute by Mode X CO2 Emission Reductions X Emission Reductions from Congestion Mitigation Air Quality (CMAQ) Funded Stewardship X X Projects (includes Carbon Monoxide & Particulate Matter-10) Reduction in Vehicle Miles Traveled X X Commute by Mode X X Transit Ridership X X Cost of Housing/Transportation X Quality of Life Multi-Modal Level of Service X % of Population Access to Trails, Parks & X Recreation % of Interstate Pavement in Good/Poor X X Condition % of National Highway System Pavement X X in Good/Poor condition % Bridges on the National Highway System X X System in Good/Poor Condition Operations, Maintenance & % of Person-Miles Traveled on the X X Performance Interstate System that are Reliable % of Person-Miles Traveled on the Non- Interstate National Highway System that X X are Reliable % of Expenditures on Preservation & X Maintenance Total Fatalities and Serious Injuries X X Safety and Fatality & Serious Injury Rates X X Security Bicycle and Pedestrian Fatalities & Serious X X Injuries (Source: Horizon 2040, P 2-18) 17
Spokane Regional Transportation Council Table 2 shows that some performance measures are not currently available to SRTC. Based on these performance measures, SRTC used a project evaluation method to help the SRTC Board select projects to include in the RTP. The evaluation criteria used to link performance and regional decision‐making is shown in Figure 3. Criteria that represents current information is in blue and data that SRTC forecasts is shown in green. The plan document notes that many of the evaluation criteria shown in Figure 3 are placeholders until better data and/or tools become available. FIGURE 3: HORIZON 2040 EVALUATION TOOL CRITERIA (Source: Horizon 2040, P 2-18) 2.2.4.2 Congestion Management Planning Process SRTC also performs a critical role in developing and implementing a congestion management planning process for the Spokane region. The Congestion Management Process (CMP) identifies Spokane’s most congested roadways, develops strategies to reduce congestion or keep it from getting worse and for tracks progress toward those efforts. This helps the region to prioritize transportation investments in order to manage congestion. SRTC's board adopted the CMP in 2014 10. The process designated sixteen congested corridors whose performance is monitored annually. The CMP identified fourteen performance measures to track progress of the CMP corridor’s system performance and the effectiveness of CMP strategies. 10See Congestion Management Process, adopted by SRTC Policy Board December 11, 2014, Spokane Regional Transportation Council. Accessed via web on September 1, 2020 (https://www.srtc.org/wp-content/uploads/2016/11/CMP_Final_12-14.pdf) 18
DATA Project Summary and Recommendations Performance measures are summarized from the CMP report Appendix B in Table 3. A more complete description of each measure as well as the results for the corridors selected for congestion monitoring can be found in the CMP report. TABLE 3: CONGESTION MANAGEMENT PROCESS (CMP) CORRIDOR PERFORMANCE MEASURES GUIDING PERFORMANCE SOURCE METHODOLOGY PRINCIPLE MEASURE Transportation plus H+T Affordability Index by Join spreadsheet manually housing costs as a CNT (Center for acquired from CNT for 2007‐ percentage of median Neighborhood Technology) 2011 from their website in GIS. income in CMP Using 2000 Census Block Provide average along corridors Groups corridor. Highways ‐ WSDOT 2012 data Economic Vitality downloaded 1/16/14 (most WSDOT 2011 Traffic current available). Late 2013, Freight tonnage in CMP Volume Shapefile and SV data provided by City of corridors FGTS tonnage 2013 Spokane Valley for WSDOT Updates for FGTS FGTS updates. No data provided by City of Spokane Determine land value based Assessor's current parcel on Assessor's tax valuation Assessed land value in database for Spokane and including a percentage of CMP corridors County based on 1/2-mile the valuation for parcels split buffer of corridor within buffer area. and Leadership Sign‐in sheets, public Inform and determine the Cooperation Attendance at CMP meeting, meetings with process as to how the CMP meetings, committee, individual stakeholders, corridors were identified. and public meetings presentations to SRTC Review of specific corridors at Board, TTC and TAC each meeting. Expenditures from Review projects that meet Stewardship SRTC call for projects CMP strategies in TIP and SRTC Transportation for CMP projects vs. all review selected corridors Improvement Program expenditures for SRTC relating directly to the CMP call for projects listings 19
Spokane Regional Transportation Council GUIDING PERFORMANCE SOURCE METHODOLOGY PRINCIPLE MEASURE STA provided bus frequency Transit performance on and access along each Spokane Transit Authority corridors corridor during Peak Hours (6‐ 8 AM, 4‐6 PM) Derived from INRIX Traffic Analytics Historic Probe TTI for each corridor was System Operations, Maintenance & Preservation Data Explorer Tool, Travel determined by using data from Travel Time Index (TTI) Time Index (TTI) represents April 2012. AM TTI data was averages and peaks on actual travel time as a taken between the hours of corridors percentage of the ideal 07:00‐09:00 and PM TTI was (free flow) travel time between the hours of 16:00‐ (Travel Time/Free‐flow 18:00 PM. Travel Time) 11 Derived from INRIX Traffic Analytics Historic Probe PTI data is exactly the same Data Explorer Tool, as the TTI above. The Cost of Cost of Planning Time Index (PTI) Project will be determined by Project/Planning Time represents the near‐ worst the Transportation Index (PTI) case travel time as a Improvement Program (TIP) improvement percentage of ideal (free on a year by year basis flow) travel time (95% provided the TIP project has Travel Time/Free‐flow been constructed Travel Time)11 Reliability Transit Spokane Transit Authority Provided by STA Bus Route Factor (reliability based, (STA) Scheduler travel‐ time TBD) 11Note that this information is now provided by Regional Integrated Transportation Information System (RITIS, see ritis.org for more information) 20
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