Truck drivers consistently rank parking availability as a top concern. When drivers cannot readily find parking, they are forced to park illegally or continue searching, often violating federal hours-of-service rules. To help commercial drivers plan their trips and maximize the use of available parking, WSDOT, in partnership with the STAR Lab at the University of Washington, is developing and installing a self-learning and optimizing Truck Parking Information and Management System (TPIMS). The UW researchers will support WSDOT in determining site viability and design to prepare for TPIMS development. They will build the server that will host all relevant data and UW analytics algorithms and will integrate it with WSDOT’s data management system. They will enhance the accuracy and reliability of the truck parking availability prediction algorithm based on the results and findings from a pilot project, including fusing real-time data, historical spatial-temporal data, and attributes information into the framework. Finally, the UW team will work with WSDOT to develop an application programming interface to provide third-party access to the resulting occupancy and prediction data. In addition, they will enhance the mobile app developed in the pilot project, such as allowing it to host more parking sites and making it more effective and user friendly to disseminate critical information to truck drivers.
Principal Investigator: Yinhai Wang, Civil and Environmental Engineering, UW
Sponsor: WSDOT
WSDOT Technical Monitor: Karthik Murthy
WSDOT Project Manager: Doug Brodin
Scheduled completion: June 2025