Matching the Model to the Decision: 3 Ways VisionEval Supports Long-Range Scenario Planning

Long-range transportation planning asks agencies to make decisions today about conditions that may not fully take shape for years or even decades. Population growth or decline, land use, economic conditions, technology, transportation pricing, travel behavior, and public policy can all change over the timeframe of most long-range transportation plans (LRTPs), creating uncertainty for planners.

That uncertainty does not make forecasting less important or negate the need to plan at all. On the contrary, the increasing need for planners to engage in decision-making under deep uncertainty makes choosing the right approach to forecasting even more important. Strategic models such as VisionEval can give planners a powerful tool to explore how different assumptions and policy choices could influence regional travel outcomes.

The Planning Decision Comes Before the Model

When beginning the process of developing a long-range plan, the starting point should not be a decision to use a particular model. Instead, it should be based on the questions an agency needs to answer and the tools already in its toolkit.

“When we work with clients who are looking to update their long-range plan, we first seek to understand what tools they have, including how they are using their current travel model before we even consider how VisionEval might fit in,” said Jonathan Slason, Managing Consultant at RSG. That is because the answer varies by model type and agency need.

For agencies with more detailed activity-based models, the primary benefit of VisionEval is its ability to explore many more scenarios more quickly using a similar set of inputs. Agencies can draw from common demographic, socioeconomic, and land-use data while exploring many more combinations of pricing, land use, transportation demand management (TDM), and other assumptions without requiring the longer run times associated with a full activity-based model. The nature of demand, sensitivity to pricing, and accessibility give the models common attributes so they can inform one another to maximize their individual utility.

The relationship is somewhat different with a trip-based model. In this case, VisionEval is a powerful complement because it is sensitive to pricing, land use density and diversity, travel demand management, and other factors that trip-based models often do not capture, such as changes in household makeup and income. That allows planners to use VisionEval to test questions their existing trip-based model may not be capable of addressing, then use those results to identify scenarios worth carrying forward into more detailed modeling.

The principle guiding each of these planning decisions is straightforward: understand the planning question first, then choose the modeling approach (or combination of approaches) that can best answer it.

Different Questions Require Different Levels of Analysis

Matching the model to the question also means considering the level of detail an agency actually needs. Network-based travel demand models provide the specificity agencies need to understand how travelers move across a transportation network, and they remain important for project-level, operational, and other analyses where that detail matters. VisionEval operates at a different scale, allowing planners to examine broader changes in travel behavior and regional outcomes without first specifying every element of a future transportation network.

That difference can be particularly useful when planners want to understand the implications of a major change without yet needing to know exactly how it will affect individual roadway segments. Consider, for instance, evaluating the regional implications of significant new development. Conducting that analysis in a network-based model could require much more time to develop the inputs and parameters needed to assess the change. Once a base VisionEval model is up and running, planners could potentially complete an analysis like this in a couple of hours, test various scenarios and assumptions about secondary land-use changes, and use the resulting range of outcomes to inform future network and transportation system improvements.

The same principle applies to policy questions. VisionEval is sensitive to changes in factors such as pricing, land use density and diversity, various transportation demand management policies, demographics such as household ages, employment, income, and other conditions that influence travel behavior. That makes it possible to examine a wider range of assumptions before committing resources to more detailed modeling.

Planners should not view this as a choice between a better model and a less performant one. Instead, they should select the tool that best matches the question and level of analysis required. A strategic model such as VisionEval can help an agency understand the direction and magnitude of potential change; a network model can then provide the additional specificity needed when the planning process moves toward individual corridors, projects, or operational decisions.

Three Ways VisionEval Can Fit into Long-Range Planning

Because agencies differ in their size, resources, existing modeling capabilities, and planning needs, there is no single way VisionEval needs to fit into the long-range planning process. In practice, its role can take several forms, some of which are outlined here.

1. As a strategic forecasting tool

For smaller MPOs, VisionEval can provide a comparatively lightweight way to add quantitative forecasting and scenario planning capabilities without relying on a full network-based travel demand model for every long-range question. There exists particular potential among MPOs serving smaller regions because VisionEval can give these agencies a more capable tool and provide a lightweight way to explore the future and support public conversations about possible changes to policy or planning.

Planners can supplement VisionEval with GIS, passively collected travel data, traffic counts, and other sources to understand regional travel behavior and potential future conditions. In some cases, VisionEval can meet many of the needs of a long-range plan when planners do not require detailed forecasts of operations at the individual roadway level.

2. As a long-term counterpart to a network model

For agencies that already maintain detailed network models, VisionEval can operate at a different level and time horizon. In this instance, it can be used to explore broader questions about policy, investment, land use, pricing, demographics, and travel behavior, while the network model remains available for the corridor-, project-, and operational-level questions for which it was designed.

This creates a two-scale approach to planning. Long-range plans are updated regularly even as they continue to look decades into the future. VisionEval can help agencies revisit strategic assumptions and policy direction as conditions on the ground change, while more detailed models can translate those directions into specific transportation investments and operational decisions.

3. As an exploratory front end to detailed modeling

VisionEval can also help agencies decide which scenarios are worth evaluating in greater detail. As a relatively lightweight tool, VisionEval can explore many more scenarios more quickly than most network-based models while drawing from a similar set of demographic, socioeconomic, and land-use inputs.

Rather than using a detailed model to test every possible combination of future conditions, planners can use VisionEval for scenario planning across a much broader range of assumptions. The results can then help narrow the field to the scenarios, policies, or uncertainties that warrant deeper analysis in an activity-based or trip-based model.

These approaches are not mutually exclusive. What matters to planners is giving VisionEval a role that reflects the agency’s planning questions, existing capabilities, and need for detail rather than assuming every organization should use the tool in exactly the same way.

From Hundreds of Futures to Defensible Decisions

Scenario planning using VisionEval is unique. It allows the user to combine trends across multiple dimensions to explore a wide range of possible outcomes. Because it is modeled, with specific output measures produced, it is a quantifiable way to conduct exploratory scenario planning (XSP). The outcomes from this process can be used to inform policy decisions and actions today that are more likely to move a region toward its goals under a range of possible conditions. The Southwest Washington Regional Transportation Council (SWRTC) provides one example.

Ahead of an update to its long-range transportation plan, SWRTC used VisionEval to explore potential strategies for reducing per capita vehicle miles traveled (VMT). The process began with stakeholders identifying policies and investments they wanted to better understand, including changes in land use, transit service, walking and biking infrastructure, transportation demand management, parking costs, road pricing, and telework.

VisionEval was then used to test different combinations and levels of those inputs. Ultimately, the analysis included 971 different possible futures. Rather than treating one of those futures as a prediction of what would happen, planners could compare the results to understand which combinations of policies consistently moved the region toward its VMT goals.

The analysis helped SWRTC and its stakeholders narrow a broad universe of possibilities to strategies worth carrying forward in the planning process, including more compact land use, increased transit service, active transportation and transportation demand management, and different approaches to transportation pricing. The results could also show how factors outside an MPO’s direct control, such as state policy or future telework rates, might affect regional outcomes.

Better Data-Driven Decisions for Communities

Ultimately, the value of any modeling tool can be assessed in whether the information it provides helps planners and decision-makers make better choices about the future of their communities.

That is especially important in long-range planning, where agencies are making decisions about investments and policies even though many of the conditions influencing those decisions continue to change. Scenario planning provides a way to make that uncertainty part of the analysis rather than obscuring it behind a single forecast. By examining how different assumptions about land use, pricing, transportation options, demographics, travel behavior, and other factors affect outcomes, planners can better understand which strategies remain valuable across a range of possible futures and which questions require additional analysis.

Long-range plans should evolve as conditions change. Strategic models such as VisionEval let agencies revisit assumptions, test new scenarios, and determine whether previously identified policy and investment directions still hold as they update their plans. The goal is to give planners the information they need to make informed decisions today while remaining prepared to adjust those decisions as uncertainty decreases and the future becomes clearer.

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Strategic transportation planning requires agencies to make consequential decisions even when future conditions remain uncertain. Scenario planning with VisionEval can help planners compare possible futures and identify strategies/policies aligned with goals. To learn more about how VisionEval can support your agency’s long-range planning process, explore RSG’s strategic transportation planning service and contact our team.