This project developed a flexible, scalable, and cost-effective camera-based system for automated FHWA 13-bin vehicle classification in real-world traffic environments. The system, which uses roadside video, edge computing, and deep learning, will enable the collection of more detailed and reliable data to support state and regional agencies’ transportation planning, infrastructure management, and safety analysis.

Because WSDOT maintains 1,000 signalized intersections throughout the state, it is important for WSDOT to plan for connected and autonomous vehicle (CAV) technology and the information it generates, which may greatly help reduce congestion. However, currently there is no real-time, reliable, and multimodal approach for controlling the timing of signalized intersections in a connected or semi-connected arterial or urban street network. This project was intended to help WSDOT identify the technological issues and requirements of integrating connected vehicle hardware in existing traffic signal systems.

Roadway safety can be significantly improved with the real-time collection of data on traffic, roadway surface, and environmental conditions and the efficient broadcasting of that information to road users. This project tested a mobile sensing unit and visibility detection system that would collect real-time traffic and environmental data to aid in object detection and tracking.

Connected vehicles, the internet of things, and smart infrastructure technologies support the exchange of real-time, highly granular traffic information among transportation network users, system operators, and the supporting infrastructure. This project worked to harness this emergent connectivity and to improve traffic mobility by optimizing the timing of signalized intersections.

Individuals provide substantial amounts of data about themselves to private transportation companies and others in connection with their requests for transportation services. This project developed recommended changes to the state’s Public Records Act that will enable the state’s public agencies to obtain and use a variety of new electronic data that describe transportation system use of individuals, vehicles, and companies, without releasing to the public detailed data that could compromise the privacy of individuals and the trade secrets of companies.

As traffic congestion increases on urban street networks, a network’s ability to process vehicles decreases because of queue spillovers and gridlock. Traffic metering can help regulate the flow of vehicles entering congested areas to allow the network to operate at its optimum level. This study sought to develop a traffic metering method for urban street networks and to study the effects of metering on traffic operations.

Pedestrians, bicyclists, and other non-motorized road users represent a large percentage of all traffic-related fatalities. This project focused on these especially vulnerable road users by developing a way to use information from connected vehicles to identify locations prone to conflicts between motorized and non-motorized users and improve traffic safety on multimodal roadway networks.