ASCE has honored Yafeng Yin, Ph.D., M.ASCE, with the 2026 Frank M. Masters Transportation Engineering Award for his research informing congestion pricing policy, guiding electric vehicle infrastructure investment, shaping regulatory approaches to emerging mobility, and opening new directions for artificial intelligence-driven traffic modeling and control.
Yin’s work is distinguished not only by its analytic rigor but also by its deep practical relevance and its influence on transportation practice and policy. His research spans congestion pricing, EV infrastructure, ride-sourcing regulation, connected and automated vehicles (CAVs), and AI-enabled travel demand modeling, consistently bridging theory with real-world implementation. In his early work, he tackled the classical problem of congestion pricing by developing behaviorally consistent and Pareto-improving pricing schemes that are both more operationally effective and societally appealing. These frameworks have informed how transportation agencies approach managed lanes and dynamic tolling strategies, offering implementable tools for reducing congestion while enhancing public acceptability.
His contributions to EV infrastructure planning exemplify the same dual emphasis on modeling and application. By anticipating drivers’ charging behaviors and accounting for interactions between the transportation network and power grid, Yin’s models provide strategic guidance for governments and utilities making infrastructure investment decisions. His research on ride-sourcing and shared mobility has provided economic analyses and regulatory insights that have shaped how cities and states think about pricing, permitting, and managing these rapidly evolving services.
More recently, Yin has been at the forefront of integrating AI into transportation modeling. He has developed inverse learning frameworks to directly infer travel demand models from traffic observations, thereby reducing reliance on costly and time-consuming household surveys. This work has the potential to transform travel forecasting practice, enabling agencies to build more accurate and adaptive models with less data burden. His research on leveraging connected and automated vehicles as active control actuators in traffic streams offers a new paradigm for network management, with clear implications for real-time traffic operations and infrastructure planning.
The Frank M. Masters Transportation Engineering Award recognizes the best example of innovative or noteworthy planning, design, or construction of transportation facilities.
