Roadway safety can be significantly improved with real-time collection of traffic, roadway surface condition, and environmental condition data and the efficient broadcasting of that information to road users. The UW’s Smart Transportation Applications and Research Laboratory (UW STAR Lab) has developed the Mobile Unit for Sensing Traffic (MUST), which can be used for real-time traffic and environmental sensing, data collection, vehicle-to-everything (V2X) applications, roadway monitoring, and infrastructure security. MUST is able to collect real-time transportation-related data, such as travel times, speeds, traffic volumes, vehicle types, pedestrian flows, and roadway surface and weather conditions. Taking advantage of AI technology, MUST can detect moving objects promptly without the need for high bandwidth communication support. In addition, MUST can be configured as a road-side unit for connected vehicle and other smart transportation applications, This project will install MUST sensors along four roadways in Lynnwood and Bellevue, Washington, to demonstrate their data collection, condition assessment, and V2X application capabilities. This project will also investigate the most cost-effective way for MUST sensors to transmit their data to transportation agencies and to broadcast real-time information to road users through various channels. The goal is to establish a standard procedure for state transportation agencies to follow in deploying MUST sensors to improve roadway safety.

Principal Investigator: Yinhai Wang, Civil and Environmental Engineering, UW
Sponsor: WSDOT
WSDOT Technical Monitor: Matt Neeley
WSDOT Project Manager: Doug Brodin
Scheduled completion: December 2023

The Federal Highway Administration is looking to help transportation agencies shift their focus from simply measuring transportation system performance to directly using those performance measures within their project identification, project selection, and decision-making processes. A potential tool for accomplishing that is the Capability Maturity Model (CMM). The CMM describes the degree of formality and optimization (“maturity”) of an organization’s processes, from reactive, ad hoc practices; to formally defined steps; to managed consistency through measurement; to optimization and continual performance improvement. As a subcontractor to Cambridge Systematics, Inc., TRAC researchers will help refine the CMM to examine how state agencies manage roadway performance. TRAC and Cambridge Systematics will develop a primer that will discuss the difference between simply reporting operations performance measures and actively managing the roadway system using those measures. The primer will also present how using the CMM can help agencies assess the maturity of their existing roadway operations management processes and provide actions that agencies can take to more effectively use performance measures to select and apply the best roadway operations management strategies.

Principal Investigator: Mark E. Hallenbeck, Washington State Transportation Center, UW

Sponsors:
Cambridge Systematics, Inc.
FHWA

Scheduled completion: October 2020

Residents of smaller and low-density communities, as well as the elderly and disabled, have few alternatives to private car travel. While new on-demand mobility services, connected vehicle technologies, and smarter city initiatives are reshaping travel in cities, those in smaller towns and rural areas, those without smart phones and communication network access, and lower-income travelers lacking a variety of additional resources are at risk of being left behind. The goal of this outreach effort is to better understand and characterize under-served populations’ perceptions of mobility needs in urban and rural environments of the Pacific Northwest and to inform those communities about the opportunities for mobility improvement that a smart city could provide. This project will identify and work with representatives from different mobility under-served groups in Idaho, Oregon, Washington, and Alaska and will develop interactive materials to inform and educate the under-served groups about the potential improved mobility opportunities in connected-vehicle and smarter city environments. They will also collect data from the participants on their mobility challenges, perceptions, and experiences and map those data within a GIS database. They will then use the data to help identify smart city implications and potential solutions.

Principal Investigators:
Ahmed Abdel-Rahim, University of Idaho
David Hurwitz, Oregon State University
Eric Jessup, Civil and Environmental Engineering, WSU
Jeff Ban, Civil and Environmental Engineering, UW
Billy Connor, University of Alaska Fairbanks

Sponsor: PacTrans
Scheduled completion: March 2023

Connected and autonomous vehicle (CAV) technology and information may greatly help reduce congestion, especially in urban settings. 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. In addition, research is needed to explore the ability to communicate basic traffic signal controller information such as signal phase and timing (SPaT) to connected vehicles to allow them to directly respond to and coordinate with ongoing signal operations. Because WSDOT maintains 1,000 signalized intersections throughout the state, it is important for WSDOT to plan for this emerging and revolutionary technology and to develop ways to use the additional information that CAVs will provide to improve traffic operations. This project will help WSDOT to identify technological issues and requirements of integrating CV hardware in existing traffic signal systems.

Principal Investigator: Ali Hajbabaie, Civil and Environmental Engineering, WSU
Sponsor: WSDOT
WSDOT Technical Monitor: Ted Bailey
WSDOT Project Manager: Doug Brodin
Scheduled completion: December 2020