Accurate travel demand forecasting provides essential insights into demand for infrastructure and helps users understand the effects of transportation policies, investments, and trends.
Travel demand modeling helps transportation agencies and other metropolitan planning organizations make informed planning decisions and roadway improvements. By aggregating and analyzing data on specifics like traffic volume or vehicle miles traveled (VMT), mode choice, transit ridership, and other travel patterns, experts can forecast future demand. RSG works with clients to develop travel demand models that accurately predict travel behavior and demand within a specified time frame.
For decades, RSG has pioneered advancements in travel model development. We apply our expert knowledge and understanding of these tools, many of which we developed, to help clients select the appropriate travel demand model for their needs. Models we work with include activity-based models, trip-based models (also called four-step models), and strategic models.
To help our clients innovate and stay ahead of trends, we can also incorporate passively collected data (“big data”) sources into the model development, calibration, and validation process. Through advanced and innovative techniques, our clients have used models we have developed to explore the effects of ride-hailing services, autonomous vehicles (AVs), electric vehicles (EVs), and other emerging technologies.
We are also active in federal transportation research related to activity-based modeling, including:
By combining the best theoretical approaches with real-world experience, our team delivers models that produce realistic, defensible forecasts of future travel demand. These forecasts are critical to helping clients guide long-range infrastructure investments and planning efforts.
Activity-based models (e.g., ActivitySim, DaySim, or CT-RAMP) offer greater functionality and fidelity than standard trip-based models by forecasting people’s daily activity patterns, including:
Our advanced activity-based modeling solutions offer a high resolution of spatial and temporal travel decisions to transportation agencies, academics, and other professionals. By creating microsimulations of individuals, including activity schedules and daily travel diaries, our activity-based models allow users to forecast not just trips—but the people and choices behind them.
Trip-based models are a common and traditional approach to forecasting travel at the local, regional, and statewide levels. Often less data intensive and capable of fulfilling many forecasting needs, these models employ a sequential, four-step modeling process:
Trip-based models can forecast origin-destination flows, simulate transportation network congestion, and support many transportation and land use planning needs, but lack some of the finer forecasting capabilities of activity-based models. Our trip-based models can optionally leverage big datasets and include many innovations to better forecast emerging trends in transportation.
Strategic models (e.g., VisionEval-State and Region models and EERPAT) occupy a middle ground between sketch planning tools and integrated or network-based travel demand models. These models can test a wider range of policies/scenarios, often more quickly than traditional travel demand models. They also offer the ability to test policies and pricing strategies on a disaggregate synthetic population. For both reasons, strategic models can be a powerful complement to other travel demand models. They also require less precise data while easily accommodating new policies and features. Strategic travel models can help transportation agencies address questions and concerns around:
RSG offers comprehensive transportation planning, analysis, and travel demand modeling techniques all under one roof. Our advanced travel model development capabilities enable your organization to develop highly detailed forecasts of everything from VMT to travel behavior across a range of scenarios. Join us as we shape the future of travel demand modeling.
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