We are thrilled to showcase our work through three presentations at TRB 2025. These sessions highlight how Steer leverages cutting-edge methodologies to address complex transit challenges.
Large-scale transit projects require efficient processes to test multiple alternatives early in planning. Many existing regional activity- and trip-based models are unsuitable due to their time and resource demands. Strategic modeling frameworks can support early transit project development by improving run-time efficiency, flexibility, and competitiveness between auto and transit modes.
The Link21 program in Northern California aims to transform the region’s passenger rail network, including a new crossing beneath San Francisco Bay. Evaluating multiple alternatives is essential to select the best options for maximizing project benefits. The Link21 Team implemented a strategic modeling framework, led by Steer, that streamlined model development, reduced run times, and assessed detailed rail network impacts while simplifying auto and transit assignments. The model was customized to focus on the morning rush hour and optimized for efficient comparisons of alternatives.
The framework uses the Regional Dynamic Model (RDM), a proprietary travel demand model with fast run times and customizability, built on Gravity and Nested Mode Choice mechanisms in Vensim simulation software. RDM outperformed a local activity-based model during calibration and validation, providing practical, rapid comparisons of transit alternatives and supporting decision-making.
The Link21 framework’s flexibility allows it to be applied to other projects, including intercity rail and public policy assessments. Its ability to test various alternatives enhances early decision-making in transportation planning across North America.
Bus Priority Interventions are small-scale adjustments that improve bus speed and reliability, including changes to street geometry, bus stops, and traffic signals. Automated Vehicle Location-Automated Passenger Counting (AVL-APC) data provides insights into bus location, speed, and passenger volumes, which can help transit agencies identify improvements.
This project demonstrated an end-to-end methodology for using AVL-APC data to design bus priority interventions for a route in Portland, Oregon. The mixed-methods approach paired quantitative data analysis with site visits to identify what was causing delays on the route and recommend targeted interventions. Scenario analysis of historical trip data was employed to predict the impact of different interventions. Historical trips that fell into two different scenarios were compared: a delay scenario (where a specific delay-inducing event occurred, like a red light) and a non-delay scenario (where that event did not occur).
This end-to-end methodology allows transit agencies and transportation planners to quickly assess different corridors and interventions, diagnose problems, and determine which projects would create the most significant customer and financial benefits. This approach could help planners prioritize time and resources to pursue the highest-impact projects.
The research offers an evidence-based resource for public transportation agencies and partners, focusing on the health benefits of public transportation. It highlights the best available research on the health benefits across urban, suburban, and rural areas, addresses equity considerations, and identifies improvements that can enhance health benefits while mitigating adverse effects.
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