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This project focuses on assessing cybersecurity readiness in state-level transportation systems through exercises using the DECIDE® platform and the development of the SECURE concept, aiming to enhance protection against cyber threats in intelligent transportation systems across New England states.
This project evaluates the performance of battery electric vehicles (BEVs) and their safety systems in mountainous regions and cold climates, focusing on the impact of terrain and weather on vehicle range and automated driving features, with implications for optimizing charging infrastructure and vehicle charger distribution.
The project focuses on evaluating the impact of GRIDSMART technology on improving intersection safety by analyzing turning movement and traffic data to predict and mitigate vehicle crashes.
This research investigates the spatiotemporal trends in pedestrian crashes, focusing on socioeconomic, demographic, infrastructure, and COVID-19 conditions to enhance pedestrian safety in New England.
This research aims to evaluate the effectiveness of different bicycle infrastructure treatments in urban settings using drone-collected trajectories, to understand their impact on the safety of bicyclists and motorists and inform future infrastructure placement and transportation safety programs.
This project aims to improve the safety, health, and efficiency of the gig transportation workforce by designing and testing a decision support system (DSS) prototype to assist gig drivers/riders in Massachusetts in making well-informed work schedule decisions.
This project focuses on developing a spatial crash typology representation to analyze multimodal road safety in New England, incorporating a range of factors from topology to traffic behavior.
This project aims to evaluate the safety and public health impacts of microtransit services, focusing on their effectiveness in reducing pedestrian safety risks and improving access to critical destinations, thereby enhancing overall public health.
This project seeks to holistically identify complex roadways using computer vision and machine learning models, and analyze their impact on driver behavior and fatal crash occurrences.


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