Smart and Cooperative Truck Parking Monitoring and Calibration System Empowered by Machine Learning 

Current systems that determine parking lot occupancy to inform drivers of available spaces have issues related to calibration and accuracy. In response, this research is creating a truck parking monitoring and calibration system powered by machine learning. Rather than relying on manual calibration, which is labor intensive, inefficient, and difficult to scale up, this research will monitor and calibrate the installed sensing system by creating a data pipe­line that includes deep learning and cooperative AI methods. For the developed system, counting data will be obtained by sensors installed at the entrance and exit of a truck parking lot. A separate video surveillance system will collect ground-truth data. Then real-time parking occupancy will be calculated, and error status will be identified by comparing the results with ground-truth records. The cooperative AI calibration component will generate a calibrated occupancy result, confidence rate, sensing system status, and calibration recommendations. The project will also create a live website to show the status of the installed truck parking sensing system and calibration recommendations. 

Principal Investigator: Yinhai Wang, Civil and Environmental Engineering, UW
Sponsor: WSDOT
WSDOT Technical Monitor: Karthik Murthy
WSDOT Project Manager: Doug Brodin
Scheduled completion: December 2024

Truck Parking Information and Management System (TPIMS)

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

Data-Driven Simulation Tool for Dynamic Curb Planning and Management

Curbs are a critical layer at which people and goods join and leave the transportation network. Traditionally, curb spaces have been statically supplied, priced, and zoned for specific uses, such as commercial or passenger loading, or bus stops. However, in response to the growing demand for curb space, some cities are being more intentional about defining curb usage. This heightened demand and changing expectations for finite curb resources requires the implementation of real-time curb management capabilities to improve occupancy and throughput and decrease traffic disruption caused by cruising for parking and space maneuvering. The Department of Energy’s Vehicle Technologies Office has funded the Pacific Northwest National Laboratory to develop a city-scale, dynamic curb use simulation tool and an open-source curb management platform. The simulation and management capabilities will include dynamically and concurrently controlling price, number of spaces, allowed parking duration, time of sale or reservation, and curb space use type. A microscale curb simulator will simulate the activities of individual vehicles transferring goods and people at the curb at the city block-face level. This project will examine new methods for dynamically reallocating curb space throughout the day and will provide this capability to city and commercial partners through a demonstration.

Principal Investigator: Andisheh Ranjbari, Civil and Environmental Engineering, UW
Sponsor: Pacific Northwest National Laboratory
Scheduled completion: September 2023

Freight Policy Transportation Institute at WSU

The purpose of the Freight Policy Transportation Institute at WSU is to undertake research on a variety of topics and issues that will improve our understanding of the importance of efficient and effective freight transportation, both to the national economy and to regions, states, and international trade.  Research topics address the need for improved intermodal freight transportation policies and implementable actions that would increase the effectiveness of intermodal transportation in lowering operating costs while also increasing the safety and decreasing the environmental impacts of freight transportation nationwide. Distributing the benefits of improved freight transportation performance to specific industries and sectors of the economy are important objectives of the Institute. The continuing focus of research projects falls generally under five themes: infrastructure investment and alternative financing/pricing, transportation security and freight efficiency, transportation and economic development, alternative energy sourcing and transportation systems, and freight transportation and international trade.

Principal Investigators:
Eric Jessup, School of Economic Sciences, WSU

Sponsor: FHWA
Scheduled completion: September 2021