Projects

The study assesses the mobility and safety challenges faced by Northfield, Vermont, focusing on pedestrians and bicyclists, and explores integrating autonomous vehicles into long-term solutions. Utilizing community engagement, the project identifies immediate improvements while promoting equitable and innovative transportation strategies for rural settings.

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 equitable and sustainable policies to mitigate infrastructure damage while enhancing 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 integrates community input into Digital Twin (DT) technology to improve roadway safety, focusing on equitable and community-driven transportation solutions. Using Community-Based Participatory Research (CBPR), it aims to model safety concerns and co-design solutions that are transportable, scalable, and informed by local 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 socioeconomic factors, 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 socioeconomic, land-use, and transportation data, 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.