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Current Projects

The Future of Mobility Data Visualization: Applying Human-Centered Design to Transportation System Health Performance Intelligence

WSDOT produces technically rigorous performance analysis of its major programs. However, current reporting formats emphasize analytical completeness over the ability of users to comprehend the information and may limit legislative receptivity for investments such as preservation and maintenance. Washington State Executive Order 25-06 directs state agencies to modernize communication and improve customer experience. To aid WSDOT in meeting that goal, this project will pilot a research-based, human-centered design (HCD) framework for reporting on the state’s transportation system health (TSH) that will improve information clarity, accessibility, and decision impact while preserving analytical rigor. To explore how TSH performance reporting can be communicated more effectively through HCD principles, the researchers will interview end users and collaborate with subject matter experts to understand how different audiences—such as legislators, WSDOT executives, stakeholders, and the public—interpret and use performance information. Based on collected insights, the project will develop and test visualization and reporting approaches that present TSH information in formats that resonate with various audiences and is more useful for decision-making while also maintaining analytical rigor.

Principal Investigators:
Ceclia Aragon, Human Centered Design and Engineering, UW
Bart Treece, Mobility Innovation Center, UW

Sponsor: WSDOT

WSDOT Technical Contacts:
Takahide Aso
Sreenath Gangula

WSDOT Project Coordinator: Shervin Jahangirnejad
Scheduled Completion: June 2027

SMART Road Sticker: Enhancing Emergency Response and Roadway Safety through Intelligent V2X Communication

WSDOT crash data indicate that more than 17 percent of traffic casualties result from drivers’ inability to respond in time to roadway hazards. To address this problem, the University of Washington STAR Lab has developed the Smart Road Sticker. This innovative device leverages V2X (vehicle-to-everything) technology to communicate in real time with surrounding road infrastructure to gather and deliver roadway hazard information. Based on that information it can provide real-time emergency alerts, thereby improving driver response times and reducing the risk of accidents. The objectives of this project include finalizing the Road Sticker’s design and development by optimizing the hardware for durability, visibility, and modularity; conducting extensive field testing under various weather and traffic conditions; and integrating mobile and localized control systems for seamless operation. A pilot program will be deployed in high-priority locations such as accident-prone areas, highway ramps, and construction zones to test inter-device communication and gather feedback from traffic safety agencies and local governments. By incorporating this cutting-edge technology into Washington state’s transportation network, the project will improve emergency response efficiency on state roadways and reduce accident rates in critical zones.

Principal Investigator: Yinhai Wang, Civil and Environmental Engineering, UW
Sponsor: WSDOT
WSDOT Technical Monitor: Justin Belk
WSDOT Project Coordinator: Shervin Jahangirnejad
Scheduled completion: June 2027

Right Sizing Low Impact Development (LID) Best Management Practices (BMPs) to Aid in Reducing 6PPD in Stormwater Runoff

Low impact development (LID) best management practices (BMPs) may aim to treat roadway runoff near its source through dispersion and infiltration. These BMPs are crucial for reducing chemicals such as 6PPD and 6PPD-q, which derive from tire preservatives and have been linked to high mortality rates in coho salmon. Unfortunately, WSDOT’s current methods for estimating runoff infiltration rates is believed to be overly conservative, rendering the LID BMPs infeasible and substantially increasing runoff mitigation costs. Runoff infiltration rate is the product of saturated hydraulic conductivity and the hydraulic gradient. The goal of this research is to refine the method WSDOT uses to estimate hydraulic gradients, enabling it to produce more accurate and less conservative estimates of infiltration rates. These refinements should help improve the feasibility, cost-efficiency, and environmental effectiveness of LID BMPs used in WSDOT stormwater designs. This in turn should encourage broader adoption of LID BMPs and help reduce 6PPD-related environmental impacts, particularly in fish-sensitive areas.

Principal Investigators:
Brett Mauer
Mike Gomez
Pedro Arduino
Civil and Environmental Engineering, UW

Sponsor: WSDOT
WSDOT Technical Monitor: Rani Jaafar
WSDOT Project Coordinator: Mustafa Mohamedali
Scheduled completion: May 2027

Laser Scanner Data Collection to Refine Chip Seal Construction Specification

Approximately 7,000 miles of Washington state roadway are paved with chip seals. Chip seals cost less than hot mix asphalt projects and can extend the life of asphalt pavements. The chip embedment depth, or the percentage of chips embedded in the asphalt, plays a crucial role in determining the seal’s performance. Improper chip embedment can lead to raveling, flushing, and bleeding. Unfortunately, there are no specifications governing the required percentage of embedded chips, nor are there data-driven methods available to measure chip embedment depth during construction. In a previous project, the researchers demonstrated the successful use of a laser texture scanner to measure chip embedment depth, and they developed a draft specification for it. This project is collecting field performance data for chip seals that were constructed in 2022 and measured as part of that previous study. With those data the researchers will refine the specification and acceptance criteria for chip seals based on percentage of embedment to be included in the WSDOT Construction Specifications. The resulting specification and measurement method should allow WSDOT to apply chip seals more successfully and cost effectively.

Principle Investigator: Haifang Wen, Civil and Environmental Engineering, UW
Sponsor: WSDOT
WSDOT Technical Monitor: Riley Bender
WSDOT Project Coordinator: Shervin Jahangirnejad
Scheduled completion: June 2027

Collaborative Research: Fostering Collective Rationality Among Self-Interested Agents to Improve the Design and Efficiency of Mixed Autonomy Networks and Infrastructure Systems

This Early-Concept Grants for Exploratory Research (EAGER) project is investigating the emergence, mechanisms, and applications of collective rationality among self-interested vehicle agents in the design of mixed autonomy transportation networks and infrastructure systems. Collective rationality refers to the ability of self-interested members of a group to make consistent, logical decisions that maximize overall, shared benefits. This project will explore and rigorously define the concept of collective rationality in the context of mixed traffic and will explore its application to designing the strategic behaviors of autonomous driving agents in mixed autonomy transportation systems. The core hypothesis is that collective rationality can emerge in broad scenarios even if the involved agents are self-interested. The researchers are leveraging game theory and reinforcement learning to verify this hypothesis theoretically and computationally. The results may help reduce travel cost, uncertainties, and fuel emissions, as well as enhance equity among all road users. Broader applications may include autonomous vehicle behavior design and emergency evacuation.

Principle Investigators:
Jia Li, Civil and Environmental Engineering, WSU
Michael Zhang, Civil and Environmental Engineering, UC Davis

Sponsor: National Science Foundation
Scheduled completion: August 2026

Bridging the Gap: Identifying and Addressing Active Transportation Disparities in Underserved Rural Residential Clusters 

Residents of rural residential clusters (RRCs) in Washington often face systemic barriers to safe, affordable, and reliable transportation, resulting in disproportionate hardship and limited access to essential services. The goal of this study is to advance transportation equity by identifying, analyzing, and addressing active transportation (AT) disparities in transportation-disadvantaged RRCs. For this research, an RRC is defined as a small, unincorporated cluster of adjacent homes located along a state highway or major county road. The study area encompasses nine counties in western Washington state, extending from Lewis County northward to the Canadian border. For this project, the researchers are developing a geospatial method for identifying and mapping state and major county roads that prevent residents of RRCs from using AT. They are assessing the specific AT needs, challenges, and perceptions of residents in these communities and are identifying the kinds of infrastructure improvements that would be responsive to local priorities and experiences. They are also developing resources and tools—including a geospatial database, prioritization methodology, and reproducible workflow—to support targeted interventions that WSDOT can implement to improve connectivity, safety, and equitable access for residents of RRCs across the state.

Principal Investigators:
Angela Kitali
Jeff Walters
School of Engineering and Technology, UW Tacoma

Sponsor: WSDOT

WSDOT Technical Monitors:
Grace Young
Brian Wood

WSDOT Project Coordinator: David Strich
Scheduled completion: February 2027

Shore Power: Partnerships to Provide eMobility Options at the Bremerton Ferry Terminal for a Greener Future

Washington State Ferries (WSF) has prioritized electrification as a core strategy to reduce greenhouse gas emissions, improve accessibility, and alleviate congestion at key terminals. This project aims to develop a replicable interagency planning framework for integrating electrified mobility options at ferry terminals. Integrating electric mobility (eMobility) solutions requires a comprehensive approach that includes expanding access to transit, enhancing multimodal travel options, and deploying electrification infrastructure. The Bremerton Ferry Terminal provides a unique opportunity for electrification, given its role as a regional transportation hub with direct connections to Seattle. For this project, researchers are analyzing current mobility challenges, evaluating eMobility and electrification solutions, performing cost analysis and energy demand modeling, creating a scalable planning framework, and engaging stakeholders and facilitating knowledge transfer among WSF, Kitsap Transit, utility providers, and local agencies. The results will support Washington state’s transition to a more resilient, efficient, and equitable transportation system.

Principal Investigators:
Hyun Woo “Chris” Lee, Construction Management, UW
Rachel Berney, Urban Design and Planning, UW
Lingzi Wu, Construction Management, UW
Bart Treece, Mobility Innovation Center

Sponsor: WSDOT
WSDOT Technical Monitor: Kevin Bartoy
WSDOT Project Manager: Mustafa Mohamedali
Scheduled completion: June 2027

Seismic Collapse Prevention for WSDOT Bridges

Earthquakes threaten the functionality and safety of the highway transportation system in Washington state. Given the state’s funding constraints, WSDOT needs to focus scarce resources on the bridge failure mode that is most likely to lead to bridge collapse, namely the shear failure of reinforced concrete columns, and the bridges that are most likely to suffer from column shear failures. This project is developing a new methodology, or modifying an existing one, to evaluate the seismic-shear vulnerability of WSDOT bridges with reinforced-concrete columns.  This will allow WSDOT to account for the amount of shear-strength reduction that will result from repeated cycling during long-duration and shorter-duration earthquakes. The researchers will also provide WSDOT with the practical tools and training needed to implement the developed methodology. The results should enable more reliable characterization of post-earthquake transportation functionality, which will support improved emergency planning. 

Principal Investigators:
Marc Eberhard, Civil and Environmental Engineering, UW
Jeffrey Berman, Civil and Environmental Engineering, UW

Sponsor: WSDOT
WSDOT Technical Monitor: Amy Leland
WSDOT Project Manager: Mustafa Mohamedali
Scheduled completion: October 2027

Sound Mitigation Study of the SR 520 Bridge Modular Expansion Joints, Phase 3

While expansion joints are a necessary component of bridges, they also contribute to noise pollution. Previous studies have investigated the design and feasibility of strategies to mitigate noise caused by modular expansion joints on bridges in Washington state. This study is Phase 3 of an effort to develop a highly durable sound attenuation system in which researchers will perform laboratory-based testing of noise mitigation prototypes. The prototypes will be injected with mixtures of fiber-reinforced, natural, and synthetic rubber with protective additives. The researchers will use a selection of low-density foam to fill gaps for increased durability and protection against roadway debris. Finally, they will use laboratory equipment to evaluate the prototype systems for hydraulic compression, cyclic fatigue, weathering of the system, adhesive durability, abrasion between the prototype and the seal, and susceptibility to debris.

Principal Investigator: Per Reinhall, Mechanical Engineering, UW
Sponsor: WSDOT
WSDOT Technical Monitor: Omar Jepperson
WSDOT Project Manager: Mustafa Mohamedali
Scheduled completion: November 2026

Cost-Effective Traffic and Roadway Data Collection Using an Edge-Based, Comprehensive Sensing System: A Machine Learning-Based Approach

High-quality traffic data are crucial for infrastructure planning, system operations and performance measurement, safety considerations, maintenance activities, and informed analysis and decision-making. That’s why state departments of transportation need comprehensive and cost-effective traffic sensing and data collection systems. The primary goal of this research is to develop machine-learning-based detection algorithms, software, and a mobile hardware system that can utilize existing surveillance video cameras to accurately collect critical traffic information that traditional traffic sensors often cannot capture. This information includes vehicle volumes based on FHWA’s 13-bin classification system, speeds, and road surface conditions. The machine learning process will allow the unit to be trained with real data. The major advantage of this new system will be its ability to collect short-duration count data where geometry and volumes pose safety risks to field staff.

Principal Investigator: Yinhai Wang, Department of Civil and Environmental Engineering, UW
Sponsor: WSDOT
WSDOT Technical Monitor: Natarajan Janarthanan
WSDOT Project Manager: Doug Brodin
Scheduled completion: December 2025

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