Greater Growth Scenarios


The Greater Growth scenarios center around specific narratives developed through discussions with regional stakeholders. The scenarios have distinctly different assumptions for future growth in jobs, based on key industry drivers in each scenario narrative. Likewise, patterns of population growth will vary across scenarios, depending on scenario assumptions. For all three Greater Growth scenarios, the forecasted growth in jobs and population will be the same. This forecasted growth is in addition to the 2045 baseline growth from the HRTPO Board-approved 2045 Socioeconomic Forecast.

Click here to watch a video from a recent open house about the Greater Growth scenarios.

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Key Drivers

Within each scenario are certain parameters—demographics and land use, economics, and technology—which have their own drivers. Drivers are external factors that can influence the future scenarios but are uncertain in the future. Through scenario planning, we are able to adjust the “settings” of these drivers to produce variable outcomes.

Scenario Narratives

The scenario narratives describe the key drivers and the intended travel patterns to be tested by each scenario (see graphic below). The RCS Steering Committee approved the initial scenario narratives for the Greater Growth Scenarios on July 9, 2019, and HRTPO Board on July 18, 2019.

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Land Use & Demographics

Examples of land use and demographic drivers include population, locations of population growth clusters, and the generational mix of future populations. The land use model uses regional place types and suitability factors to distribute growth differently in each scenario.


Examples of economic drivers include industry diversification, military activity, port activities, and tourism.


Examples of technology drivers include connected/autonomous vehicle implementation and shared mobility costs and usage.

Scenario Models

The Regional Connectors Study (RCS) used growth scenarios to inform recommendations for future transportation investments. The HRTPO examined each scenario through a land use, travel demand, and economic model to gather performance data and compare results.

Land Use Model

The HRTPO used a land use model to examine 2045 population and job increases within each Greater Growth scenario based on attractiveness, or suitability (shown in maps below). For example, Greater Growth on the Water scenario shows job suitability in areas close to the waterfront, but more widely dispersed population. The model then allocated the growth according to the suitability for each scenario, as shown in the allocation maps below.

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Land Use Performance Measures

Each scenario produced important differences in land use patterns. For example, Greater Growth in Urban Centers scenario shows the most jobs near transit stops, and Greater Suburban/Greenfield Growth scenario shows the most population growth on undeveloped land.









Land Use Modeling Conclusions

  • Scenario land use patterns and growth allocation are consistent with the drivers of future growth in each scenario narrative.
  • Strong differentiation between the Greater Growth scenarios and a plausible range of alternate futures for the region, lending to travel demand and resiliency testing.

Travel Demand Model

The RCS used HRTPO’s regional travel demand model to create a “baseline” for travel patterns and impacts in 2045. That model includes existing and planned facilities. Scenario data was calculated using the baseline information and the population and employment growth data collected from the land use model. Results for each scenario represent the total of all person trips and all travel on the roadway network (2045 baseline data + scenario data), whereas the land use model results only represent additional growth in each scenario. The results below highlight key differences in these travel patterns.

Travel Model Technology Assumptions

The 2045 Baseline scenario also included a set of assumptions about future transportation technologies, such as connected vehicles, autonomous vehicles, and mobility as a service. The illustration below shows some of these assumptions for each scenario. These assumptions reflect the features noted in each of the scenario narratives concerning technology.

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** Mobility as a Service: A platform that enables users to plan, book, and pay for multiple types of mobility services.

** Connected Vehicles and Autonomous Vehicles (AV): Vehicles with technology that communicates with other cars, infrastructure, and the environment. Autonomous vehicles do not require a driver. 

Transportation Performance Measures

The transportation performance results below show important differences between the Baseline and Greater Growth scenario results in the amount of travel, the amount of congestion, and the patterns of congestion. They also show differences in the amount of travel and delay on harbor crossings. The maps below also reflect markedly different locations and patterns of congestion across the scenarios.



Please note, in the transportation model results graphics, the bar heights and x-axis show the comparative quantities. The white bar represents the baseline and the colored bars represent the scenarios. The bar labels indicate the percent change of each scenario from the baseline.





Change in Hours of Delay Due to Congestion

*Compared with 2045 baseline

Travel Modeling Conclusions

  • Transportation performance measures for each of the Greater Growth Scenarios generally reflected the scenario narratives. For example, both land use and technology assumptions produce markedly less congestion in the Greater Growth in Urban Centers scenario, more concentrated congestion in the regional core in Greater Growth on the Water scenario, and more widespread congestion in the Greater Suburban/Greenfield Growth scenario.
  • There is substantially more congestion forecasted in the future, even in the Baseline condition, and the levels and patterns of congestion support the need for additional cross-harbor capacity.
  • The patterns of congestion and cross-harbor travel show effective differentiation between the Greater Growth scenarios. The transportation performance measures portray a range of alternate futures that will provide a good resilience test for candidate alternatives for regional crossings.

Economic Model

The economic model used information from the travel model to interpret how the scenarios affect regional trip efficiency and what the improvement or decline in efficiency might mean in terms of societal and economic costs.

The evaluation of societal costs relies on measures that reflect the time and expense of travel, such as congestion and reliability, and measures of safety and air quality that affect everyone. The model includes assumptions about how future technologies, such as electric and autonomous vehicles, will impact these measures.

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Economic Model Conclusions

The table below indicates that, compared to the 2045 baseline societal costs, the Greater Growth on the Water scenario raises costs related to crashes and congestion, and the Greater Growth in Suburban/Greenfield scenario raises both congestion and freight-related costs. In total, however, only the Greater Growth on the Water scenario yields a net increase in societal costs.

Both the Greater Growth in Urban Centers and Greater Suburban/Greenfield Growth in scenarios reduce societal costs compared to the Baseline. The summary shows meaningful differences between the scenarios, and the reasons behind the differences align with the scenario narratives. For example, the high implementation of autonomous vehicles (expected to be electric vehicles and to create safer travel conditions) reduces the societal costs of emissions and crashes in the Greater Suburban/Greenfield Growth scenario.

Similarly, improvements in travel speeds and reductions in congestion in the Greater Growth in Urban Centers scenario results in significant travel time and reliability savings.

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Economic Model Conclusions

  • The Greater Growth on the Water scenario is the most inefficient overall. In this scenario, overall societal costs of travel increase because of more crashes and congestion.
  • The Greater Growth in Urban Centers scenario has the most efficient travel patterns, resulting in the strongest societal cost reductions.
  • The Greater Suburban/Greenfield Growth scenario has mixed results with notable improvements in safety and emissions that offset its negative effects related to congestion and goods movement.

What's Next

In 2021, the RCS will develop concepts for regional crossings, called alternatives. The study will test each alternative within the Baseline and Greater Growth scenarios and report the results (reference graphic). Finally, an evaluation of the crossing alternatives will be conducted to see which ones are the most resilient under these scenarios – in other words, which alternatives perform best under a range of possible futures.

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Frequently Asked Questions

Will HRTPO select one preferred scenario of the future?
In exploratory scenario planning, the point of the exercise is not to select one preferred future scenario, but rather to be prepared for what could happen in the future by analyzing several plausible future scenarios. The scenarios are used to test alternative transportation investments under different possible futures – not to pick a preferred future. Therefore, HRTPO will not select a preferred scenario.
Why do all the scenarios include changes in technology – are these changes very likely?
In the timeframe of the planning horizon – the year 2045 – experts predict there will be significant changes in transportation technology and mobility choices. The rise of mobility as a service in the last decade and the advent of scooters and autonomous shuttles in just the last few years illustrate that technology changes are already coming. The exact details are not foreseeable at this time; therefore, the scenarios represent different areas of emphasis such as autonomous versus connected vehicle technology and purchase of rides vs vehicle ownership. This approach allows decisionmakers to foresee the issues and benefits of what may happen and make investment choices that maximize preparedness.
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RCS Public Meeting Presentation Recording – 2/10/2021
RCS Public Meeting Presentation Recording, Feb.10 - Mar.3, 2021 The Virtual Public Meeting Presentation shares data insights from Phase 2 scenario planning. This phase of the study compared each Gr…
Scenario Planning Webinar #8 – 1/23/20
Scenario Webinar #8, 01/23/2020 This webinar focused on the trend drivers to be incorporated in the travel demand model. These drivers include operational and behavioral impacts of transportation t…
Scenario Planning Webinar #7 – 6/27/19
Scenario Webinar #7, 6/27/19 This webinar focuses on the details of the land use model suitability factors, which are a key component of land use allocation. The land use team illustrated the way tha…
Scenario Planning Webinar #6 – 6/6/19
Scenario Webinar #6, 6/6/19 In this webinar, the study team reviewed the analysis and recommendations for the Greater Growth employment level and pros and cons of the choices under consideration. The…
Scenario Planning Webinar #5 – 5/2/19
  The focal points of this webinar were goals, objectives, and performance measures. The team reviewed the four draft goals—Economic Vitality, Sustainability (Equity, Community, and Environmen…
Scenario Planning Webinar #4 – 4/11/19
The land use team updated the working group on the progress of the place type development and allocation for the place types in both parallel tracks (2015/2045 and the greater growth scenarios). They …
Scenario Planning Webinar #3 – 3/27/19
The third webinar reviewed the questionnaire sent out to the region’s jurisdictions and organizations regarding drivers. The major focus of the webinar was economic. The team discussed the likelihood …
Scenario Planning Webinar #2 – 3/15/19
During the second update, the land use team went into more detail about how the development and allocation of the existing and (baseline) future place types is used in the land use model and subsequen…
Scenario Planning Webinar #1 – 2/14/19
The first webinar introduced how the model will take land use into account both in the present and in the future. The team explained how the land use data are used to build “place types”—profiles of d…