Research Issue

Urban transport ecosystems are designed to meet human needs related to mobility and accessibility. Humans interact with urban transport systems in several ways across diverse spatiotemporal settings. While social network based systematic differences exist, humans are intrinsically unpredictable and this uncertainty will continue to characterize the future urban mobility systems. Driven by new technological and methodological advancements, my research broadly focuses on gaining an objectively-driven understanding of human behaviors required for the planning, design, and analysis of dynamic urban transport networks. Relevant in this regard is the role of intelligent transportation systems (ITS), especially how connected and automated vehicles (CAVs), digital technology, new ambient sensing technologies, and big data can enhance our understanding of extraordinarily complex human behaviors.

Research Approach

Considering cities as real-time ecosystems, my research harnesses methodological advancements, big data, and cyber-physical systems (CPS) to gain a deeper understanding of human behaviors. Through seamless integration of computation and physical components, CPS provides an innovative way to address major societal challenges including road safety, smart mobility, and health. 
With ambient-sensing and communication technologies at hand, the demand for transportation innovation across critical application domains is driving the exigency to accelerate context-specific fundamental transportation research. The dynamism  of urban systems complicates the design & analysis of transport systems – warranting development of methods that can extract reliable and accurate information from not only traditional and bigger data, but also from data systems characterized by the mix of the two. Along these lines, my research methodologically focuses on development and application of methods to gain new inferential and predictive insights into the role of human behavior in surface transportation systems.

Research Contributions

With a big data-driven perspective, my past and ongoing research focuses on obtaining a better, spatially-referenced, understanding of behavioral elements in existing/future surface transportation to proactively tackle safety, smart mobility, and health challenges. 

Proactive Safety

Traditional and emerging sensor-based data streams are integrated to capture information on a broad spectrum of roadway, driver, vehicle, and environmental factors. Using the hugely expanded data infrastructure to develop new surrogate safety measures from kinematics, health biometrics, GPS traces, CAV based BSMs, and processed video information – ultimately pieced together in advanced econometric & predictive AI models to paint a fuller picture of existing and future crash risk involving motorized and vulnerable road users.

Connected, Automated, Shared,
& Electric (CASE) Mobility

Understanding behaviors, preferences, and attitudes of modern populations towards a CASE-driven smart urban mobility system. Using stated/revealed preference population surveys and large-scale ambient sensing-based mobility traces to develop new methodological and behavioral frameworks to examine and forecast the adoption of CASE systems.

Transportation & Health

Informed by CPS and sensing technologies, we examine the interconnections between transportation planning and design, technology, active transportation, and health. Empowered by a systems approach,  the focus is on the entire pathway between upstream factors including transportation/built environment and downstream factors including chronic and infectious diseases – through the intermediate exposures and mobility behaviors induced by physical and social networks.