PacTrans News
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May 16, 2025
Strong Turnout for Intro & Intermediate Data Courses — Get Ready for the Advanced Session in Fall 2025!
In May, Dr. Ryan Avery, Deputy Director of the Washington State Transportation Center (TRAC) at the University of Washington, taught both Introduction and Intermediate Transportation Data Analysis Courses for the PacTrans Workforce Development Institute (WDI).
For the Introduction to Transportation Data and Analysis Tools course, ten students actively participated in discussions on data, geographic information systems, and data visualization techniques. Practical topics covered issues surrounding gathering and organizing data, skills required for the management and analysis of increasingly large data sets, transportation dataset trends and limitations, capacity limits of desktop tools to process data for projects, and creating data reports for others to demonstrate system performance or justify project investments. Students also participated in a hands-on Excel exercise. Dr. Avery provided an introduction to GIS, an ArcGIS demonstration, presented map projections, and gave GIS examples. The course wrapped up with a discussion about continuing education opportunities in these areas.
For the Intermediate Transportation Data and Analysis Tool course, students learned about database design and organization in relation to the importance of maintaining the integrity of the data and in facilitating a data query. Dr. Avery provided an overview of Structured Query Language along with nuances of running queries through demonstrations and hands-on practice opportunities for participants. Dr. Avery also reviewed potential issues to consider when applying machine learning to transportation datasets and presented an overview on programming languages. The course wrapped up with resources for continuing professional development in data science. Many participants were affiliated with Trimet and WSDOT.
Save the date for our Advanced Transportation Data and Analysis Tool course coming in the Fall of 2025 on September 9-10, 2025!
View all our courses at pactranswdi.org.
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May 1, 2025
PacTrans Hosts Second Offering of “AI 101” for Transportation Professionals
In response to the increasing demand for knowledge and practical skills in artificial intelligence (AI) among transportation professionals, the PacTrans Workforce Development Institute (WDI) launched a short course titled “AI 101: Introduction to Artificial Intelligence for Transportation Professionals.” Following the overwhelming response to the first session held in February— which quickly reached full capacity—PacTrans organized a second offering of the course.
The second session took place on Tuesday, April 29, 2025, and was led by PacTrans Director Dr. Yinhai Wang and PI Dr. Muhammad Karim. The course aimed to provide transportation professionals with a foundational understanding of AI and its potential in transportation.
Transportation is likely to become one of the first public-facing domains where AI will be engaged and tested. From autonomous and connected vehicles to smart infrastructure, AI technologies are already shaping how we move and how our transportation systems are managed. This four-hour course introduced key AI concepts, including Machine Learning and Deep Learning, and explored their applications within the transportation field.
They also covered real-world case studies demonstrating the tangible benefits of AI, such as improved traffic flow, reduced emissions, and enhanced public transportation services. The course concluded with discussions about the current limitations of AI, as well as future opportunities for its integration into transportation systems.
Attendees represented a diverse mix of professionals from both the public and private sectors, including the Washington State Department of Transportation (WSDOT), Remington & Vernick Engineers, and DKS Associates.
As AI continues to evolve, PacTrans remains committed to equipping transportation professionals with the knowledge and tools they need to navigate and lead in this rapidly changing landscape.
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April 30, 2025
Mapping Our Way to Accessible Cities via AI-Powered Crowdsourcing: A Look Inside Project Sidewalk
Figure 1. Project Sidewalk’s mission is to map and assess every sidewalk in the world via crowdsourcing, artificial intelligence (AI), and online map imagery. Above, a Project Sidewalk user labels a “sidewalk uplift” as a surface problem, which poses a significant safety and accessibility hazard.
“Using Project Sidewalk gave me a new perspective that I can use to help change the world.” – Girl Scout, Age 11
“It is amazing to think that in just a few hours, users can label so many problematic sidewalks and validate others’ work” – Wheelchair user
Sidewalks are more than just concrete paths; they are vital arteries of our communities—connecting us to work, school, shops, transit, and parks. They are spaces for strolling, playing, dining, and commerce. For many, especially individuals with mobility challenges, accessible sidewalks aren’t just a convenience – they are lifelines, enabling independence, physical activity, and access to key services. Despite decades of civil rights legislation, however, many city streets and sidewalks remain inaccessible. As the United Nations notes, “[there is a] widespread lack of accessibility in built environments, from roads and housing to public buildings and spaces.”
The Hidden Problem: Missing Data
The challenge isn’t just fixing broken sidewalks; it’s also that we often don’t even know where the problems are. Reliable data on sidewalk existence, condition, and accessibility features are surprisingly scarce. For example, in a sample of 178 US cities, Deitz et al. found that only 36 (20%) published sidewalk data, 18 (10%) had curb ramp locations, and even fewer included detailed accessibility information like sidewalk condition, obstructions, and crossing controls.
This lack of data fundamentally limits how sidewalks can be studied in cities, the ability for communities, disability advocacy groups, and local governments to understand, transparently discuss, and make informed urban planning decisions, and how sidewalks and accessibility are incorporated into interactive map, navigation, and GIS tools. Imagine, for example, loading up Google Maps and being provided personalized pedestrian-based routing directions that avoids obstacles and meets your mobility needs.
Enter Project Sidewalk: A Virtual Solution with Real-World Impact
This is where Project Sidewalk comes in! Developed by Professor Jon E. Froehlich and his lab at the University of Washington, with support from PacTrans, UW CREATE, and the National Science Foundation, this innovative tool is revolutionizing how we map and understand sidewalk accessibility.
How? By harnessing the power of crowdsourcing, artificial intelligence (AI), and online map imagery. Project Sidewalk allows anyone to become a virtual sidewalk explorer. You virtually explore cities via immersive imagery similar to a first-person video game, labeling sidewalks and identifying accessibility features or problems like missing curb ramps, uneven surfaces, or obstructions. Other missions allow users to validate pre-existing labels to ensure high data quality. See Figures 1 and 2.
For each identified sidewalk feature or obstacle, users assign a severity score, add descriptive tags, and can even leave detailed notes. This data is then used for:
- Creating powerful new interactive visual analytics of urban accessibility.
- Providing concrete data to inform government policy and funding decisions.
Training AI models to automatically detect accessibility issues, helping to scale the auditing process even further.
Figure 2. (top) A screenshot of a user virtually auditing sidewalks in Mexico showing labels for a curb ramp (green), missing curb ramp (red) and crosswalks (yellow). (bottom) A user validating an obstacle-in-path label in Amsterdam.
Making a Difference, Street by Street, City by City
Figure 3. The PacTrans-supported Project Sidewalk is now deployed in 35 cities across 8 countries including the US, Canada, Mexico, Ecuador, Netherlands, Switzerland, and New Zealand. Project Sidewalk users have analyzed over 21,380 km of city streets contributing over 1.1 million labels and 935k validations.
Working with NGOs, disability advocates, community groups, and local governments, Project Sidewalk is now deployed in 35 cities across 8 countries including the US, Canada, Mexico, Ecuador, The Netherlands, Switzerland, and New Zealand (Figure 3). Incredibly, Project Sidewalk volunteers have contributed over 1.1 million sidewalk accessibility labels, covering more than 21,350 km of city streets (over 13,200 miles). To our knowledge, this is the largest open sidewalk accessibility dataset ever collected.
And it’s leading to tangible change:
- In Newberg, Oregon: Community members used Project Sidewalk to meticulously map local sidewalks, collecting over 17,000 labels. This data fueled successful advocacy efforts, resulting in two new sidewalk repair programs.
- In Chicago, Illinois: The city used Project Sidewalk data to guide infrastructure spending equitably, ensuring sidewalk improvements targeted high-priority areas across different wards.
- In Oradell, New Jersey: Project Sidewalk became an educational tool. Local Girl Scouts mapped their entire town, learning about urban design, and disability advocacy. They then presented their findings directly to the Oradell City Council – empowering youth to shape their community! This project, in collaboration with the Bergen County Community Council of the National MS Society and the Hackensack Meridian School of Medicine, showcases the tool’s power to unite diverse groups. See Figures 4 and 5.
Furthering its educational reach, Project Sidewalk is also a featured project in SciStarter, a leading community science platform connecting volunteers to scientific research.
A Global Tool for a Universal Challenge
Because Project Sidewalk leverages existing Google Street View imagery, it doesn’t require expensive or time-consuming physical audits. This makes it easy to deploy almost anywhere in the world–100+ countries currently have Street View coverage with over 220 billion street view images.
- In San Pedro, Mexico, the local government said: “Project Sidewalk provides us with data that is essential to improving San Pedro’s urban accessibility. With Project Sidewalk, we know the main problems to be solved, how many problems there are, and their location… The results will be used to inform a new Pedestrian Master Plan for our municipality.”
- In Zurich, Switzerland Project Sidewalk was used to support a community and government partnership called ZuriACT (Zurich Accessible CiTy) to transform the city’s pedestrian accessibility, leading to a new open data portal on sidewalk assessments.
- In Burnaby, Canada, Project Sidewalk was used to support community-engaged sidewalk auditing of over 220 miles in collaboration with Simon Fraser University and the local Burnaby government
Figure 4. In Oradell, NJ, we are working with the Girl Scouts and local organizations to map and assess sidewalk accessibility as a service-learning project. The girls and other community members completed 35.9 miles of virtual assessments and collected 11k labels and 26k validations. The figure above shows color-coded circles of found problems—most commonly, surface problems (orange) and missing sidewalks (purple).
Figure 5. Example sidewalk problems found in Oradell, NJ—surface problems are orange labels and obstacles are blue.
Get Involved: Help Build More Accessible Communities
Sidewalk accessibility affects everyone. Whether you push a stroller, use a wheelchair or walker, rely on transit, or simply enjoy walking, safe and accessible sidewalks make our communities better.
You can be part of the solution! Visit https://projectsidewalk.org to learn more, explore the maps, and try your hand at virtual auditing. Just a few minutes of your time can contribute valuable data that empowers change. If you are a data or machine learning scientist, we just released a public dataset of annotated sidewalk features/problems on Hugging Face (Figure 6) to help others build AI models of automatic sidewalk assessment.
Together, we can transform our cities into places that are more walkable, rollable, and livable for all!
Figure 6. To help others build on their work, Project Sidewalk just released a public dataset of labeled sidewalk images and trained AI models on Hugging Face: https://huggingface.co/projectsidewalk. Some examples from the dataset are shown above.
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April 15, 2025
Join us for the next PacTrans Regional Seminar: “Playing the Cat-and-Mouse Game Between Traffic Routing and Signal Control with Three ‘Consoles’: Results and Implications” with Michael Zhang, UC Davis
View the Full Flyer Here or Add to Calendar!
📌 HUB 250, UW or watch online at here.
⏲️ Monday, May 9 from 10:30 AM – 12:30 AM PT
💼 This event is free & open to the public!
☕ Complimentary coffee and snacks provided. -
March 27, 2025
PI Ji Yun Lee works to improve wildfire evacuation planning
Article reposted from WSU Insider, written by
Computer models that take into account how people act might someday be able to predict the ensuing evacuation challenges and traffic problems that occur during wildfire emergencies.
PacTrans researchers from Washington State University (WSU) used predictive machine-learning models to simulate how people behaved during the Tick wildfire in the Santa Clarita area of California in 2019. The research, published in Fire Technology, could improve evacuation policies and help emergency managers in fire-prone areas.
