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.

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 Regional Connectors Study (RCS) Steering (Policy) Committee approved the initial scenario narratives for the Greater Growth Scenarios in July 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 (CAV) implementation and shared mobility costs and usage.

Scenario Models

The 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, the 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, the Greater Growth in Urban Centers scenario shows the most jobs near transit stops, and the Greater Suburban/Greenfield Growth scenario shows the most population growth on undeveloped land.

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Land Use Model 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, led to further exploration, including 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 ,whereas the land use model results only represent additional growth in each scenario. The results below highlight key differences in these travel patterns.

Travel Demand Model Technology Assumptions

The 2045 Baseline scenario also included a set of assumptions about future transportation technologies such as CAVs 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.

Travel Demand Performance Measures

The travel demand 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.

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Please note, in the travel demand 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.

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Change in Hours of Delay Due to Congestion

*Compared with 2045 baseline

Travel Demand Model Conclusions

  • Travel demand 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 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, 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.

Economic Model

The economic model used information from the travel demand 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 vehicles and CAVs, 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, only the Greater Growth on the Water scenario yields a net increase in societal costs

The Greater Growth in Urban Centers and Greater Suburban/Greenfield Growth scenarios reduce societal costs compared to the Baseline scenario. 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 CAVs (expected to be electric and 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?

The next phase of the study (Phase 3) will evaluate the mandatory segments on the basis of cost and construction complexity, permitting challenges, project readiness, and congestion relief in order to provide a tiering of the segments for further study and consideration. Tier 1 segments will be analyzed with the Greater Growth Scenarios to provide insight for their consideration in the HRTPO 2050 Long Range Transportation Plan.

Tier 1 and Tier 2 graphic.

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.