Projects

The study evaluates mobility and safety concerns in Northfield, Vermont, with a focus on pedestrians and bicyclists, and examines the potential role of autonomous vehicles in long-term planning. Through local input, the project identifies short-term improvements and supports practical, forward-looking transportation approaches for rural communities.

This research examines the impact of non-reducible truck permit loads on transportation infrastructure and explores strategies for balancing freight mobility with infrastructure preservation. By analyzing the extent of permit usage and its effects, the project aims to recommend practical policies to reduce infrastructure damage while supporting safety and economic productivity.

This project investigates how Human-Machine Interface (HMI) designs can build passenger trust in SAE Level 4 automated vehicles (AVs) through effective intent communication. By using a driving simulator, it evaluates how interface features influence perceptions of trust, safety, and acceptance, aiming to advance AV adoption and improve user experience.

This project incorporates local input into Digital Twin (DT) technology to improve roadway safety, focusing on practical and locally informed transportation solutions. Using a collaborative research approach, it aims to model safety concerns and co-develop solutions that are adaptable, scalable, and responsive to community-identified needs.

This project aims to enhance motorcoach passenger safety by promoting seatbelt usage through a low-cost, scalable campaign targeting passengers on regularly scheduled and charter services in New England. The research includes testing and expanding a promotional action kit to increase awareness and usage of seatbelts, focusing on both passenger behavior and potential regulatory support.

This study uses an explanatory sequential mixed methods design, combining surveys and semi-structured interviews, to explore the impact of cell phone use while driving and the adoption of emerging technologies for behavior mitigation. The research aims to provide insights into patterns of distracted driving and factors influencing the effectiveness and acceptance of preventive technologies.

This project investigates how mode-specific network metrics, such as size and connectivity for roads, transit, cycling, and walking networks, influence crash outcomes across towns and cities in New England. By developing predictive models using crash data and community characteristics, the study aims to guide safer infrastructure design and transportation policies.

This project develops models to estimate ride-sourcing demand and supply using coarse public data, such as town-level ride data from Massachusetts. By leveraging land-use and transportation data along with community characteristics, it aims to provide high-resolution insights into trip patterns and driver availability to support planning and policy-making.

This research leverages advanced vehicle sensors, such as LiDAR and cameras in modern and autonomous vehicles, to continuously monitor and assess pavement conditions like potholes, cracking, and rutting. By developing algorithms to analyze real-time data, the study aims to enhance Pavement Management Systems (PMS), reduce maintenance costs, and improve road safety.