Researchers at the University of Southern California developed a truck routing model that minimizes fuel consumption and reduces emissions while explicitly accounting for parking availability and hours-of-service constraints. The researchers used the model to test various scenarios that reflect the practical constraints faced by drivers.
The seminar summarized a study that analyzed drivers’ search behavior for parking. The study reveals a reservation-based service can improve the performance of the parking system.
Automated vehicles have the potential to reduce the need for parking, but they also have the potential to increase congestion through picking up and dropping off passengers. This project studies such possible environmental effects.
The main objective of this project is to develop a centralized truck parking system that will balance parking utilization in time and space by using full information about supply and demand.
In this project, the researchers consider the issue of coordinating the parking decisions of a large number of long-haul trucks. More specifically, how to model the behavior of a region’s driver population and how it could be influenced.
This data stems from a project in which researchers used a microscopic road traffic model with local travel activity data to simulate vehicle travel in San Francisco’s downtown central business district to explore traffic flow, VMT, and GHG effects of AV scenarios.
This project’s objective was to study the truck parking problem and generate useful information and parking assist algorithms that could assist truck drivers in better planning their trips.
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.
