Intelligent Parking Assist for Trucks with Prediction
Principal Investigator: Petros Ioannou
| University of Southern California
This project investigates the truck parking problem in the US, and in particular California, and develops an intelligent parking assist system for trucks with prediction capabilities by taking into account the availability of information technologies, and modeling and prediction techniques. The researchers will use stochastic techniques to model the availability of parking at different locations, and will use these models to develop a prediction algorithm that will predict the availability of parking at the time of arrival of the truck. This predictive capability will allow trucks to plan their trips and rest breaks more efficiently than using a rule of thumb and past experience. The models will be continuously updated as new data become available by using current and future information technologies. The benefits of such a system on freight mobility, environment and GHG emissions, fuel economy and health will be considerable and efforts will be made to quantify them. The emphasis will be on the development of modeling techniques and prediction algorithms for truck parking availability. The researchers plan to address the feasibility of commercializing such a system based on available sensor and information technologies, and by taking into account other efforts on freight advanced traveler information systems to which our algorithms may be useful.
Status: In Progress
Sponsors: US DOT