trucks

This project established models to optimize the balance of freight demand across rail and truck modes.

This project studied truck parking problems and generated useful information and parking assist algorithms that could assist truck drivers in better planning their trips.

This research brief summarizes findings from the associated project, the objective of which was to generate parking assist algorithms that can help truck drivers better plan their trips.

This study conducted a review of the potential impacts of automated technologies in the freight sector, with a particular emphasis on driving occupations. This work also recommends policy strategies to aid workers impacted by automation.

This study highlights the potential of a coordinated routing system to enhance urban traffic efficiency by dynamically adjusting incentives based on drivers’ choice data and driver behavior.

The transportation sector is responsible for a significant part of the U.S.’s greenhouse emissions, with a considerable amount being generated by medium-and heavy-duty trucks. However, when it comes to the trucking industry, ‘green’ routing studies do not consider other critical practical factors, like working hours regulations and parking availability. This study addresses a variant of the shortest path and truck driver scheduling problem under parking availability constraints which focuses on optimizing fuel consumption and emissions by controlling the truck's travel speed and accounting for time-dependent traffic conditions.