Lead Presenter Biography

Co-Authors

Presentation Description

To plan for the future, wide ranging and uncertain as it may be, the Texas Department of Transportation (TxDOT) Houston District undertook an innovative planning study for a sub-region in Houston’s southwest area. This study, termed Sustainable Mobility Alternatives for Regional Transportation (SMART), takes a data-driven, performance-based scenario planning approach to assess long-term transportation needs and disruptors (major drivers) that may impact the sub-region. This effort was one of the first to quantitatively define, model, and explore impacts of various uncertainties through a performance-based, scalable, and repeatable process integrating land use, travel demand, and mesoscopic models to improve agency preparedness and bring robust risk-based planning for future transportation projects.

The first step of this innovative planning study focused on identifying major drivers that can influence the sub-region and the region. Major drivers can be described as factors or trends most likely to influence travel demand, transportation network supply, and travelers’ behavior. Twenty-one major drivers were identified and qualitative impacts to the region were summarized. Through stakeholder discussion and feedback, the twenty-one drivers were narrowed to the following nine major drivers for further detailed analysis and to formulate scenarios: automated vehicles, connected vehicles, shared mobility, micromobility, transit expansion, urbanization & transit-oriented development, westward expansion (or sprawl), growth / energy sector, telecommuting and e-commerce/freight.

The study included a scenario planning approach within a Robust Decision Making (RDM) framework to bring valuable insights on uncertainties, disruptive technologies, and emerging trends. Robust Decision Making (RDM) requires exploring the full potential range of the major drivers’ effect on the transportation system which implies the need for many points of analysis – ideally thousands of different year 2045 future year scenarios. FHWA’s TMIP-EMAT tool was first utilized in conjunction with a travel demand model to quantitatively evaluate the transportation system under the entire range of potential futures. Utilizing TMIP-EMAT’s visualizations and algorithms, the interactions between the uncertainty variables and the performance metric outputs were assessed.

TMIP-EMAT was then utilized with both the travel demand model and mesoscopic traffic simulation model to assess the viability of a number of build projects and policies. The study focused on forming and answering questions to evaluate which individual and combination of projects and policies (e.g., congestion mitigation strategies) are most successful in reaching the overall goal (e.g., congestion reduction) under various conditions, such as:
• Which policies or projects cause the greatest change to a specific performance metric or most number of performance metrics?
• What conditions (i.e., level of policies and uncertainty factors) must be in place for a specified performance metric to have a high confidence of always being above or below a designated threshold (e.g., travel time less than 15 minutes).
• What is the set of policies and projects and corresponding levels of the policies or projects that perform well across all performance metrics?

The end product of this complex modeling process was a pipeline of long-range planning projects that would improve the sub-regions’ mobility, reliability, and resiliency across a wide range of uncertainties. 

Presentation File