Workshops

Workshop 1: Securing Safe Journeys—Enhancing Transportation Safety Through a Gender Lens

Presenters

Alyssa Ryan, University of Arizona, Moderator

Floridea Di Ciommo, cambiaMO

Nikhil Menon, Pennsylvania State University, Harrisburg

Lori Porreca, FHWA

Co-Presenters

Sushmita Bhandari, University of Arizona

Ideas Moving Forward

• Understanding perceptions: The workshop explored the distinction between attitudes and perceptions of safety and how experiences differ based on gender, race, ethnicity, age, and geographic location.
• Intersectional data analysis: Some participants discussed the lack of intersectional and gender-disaggregated data in transportation safety research, highlighting the importance of expanding datasets to better inform policy.
• Global contexts: Presenters compared safety challenges in developed and developing countries, emphasizing the need for context-specific solutions that consider cultural values, social structures, and infrastructural differences.
• Infrastructure and reporting: Persistent issues such as inadequate lighting and low confidence in justice systems were identified as barriers to improving transit safety, especially for women and other historically marginalized groups.
• Collaborative solutions: Some presenters discussed gender-transformative business cases, prevention measures, and care-based approaches to create safer and more welcoming transit environments.

Summary

This workshop, moderated by Alyssa Ryan of the University of Arizona, examined the gendered dimensions of transportation safety and security. Presenters Alyssa Ryan, Floridea Di Ciommo, Nikhil Menon, and Lori Porreca explored safety perceptions, infrastructure gaps, over-designing concerns, and cultural barriers that affect diverse populations.

The session began by distinguishing between “safety” and “security” in transit contexts, emphasizing their different policy and design implications. Safety was discussed in terms of minimizing risks of unintentional harm, such as crashes or system failures, while security addressed risks associated with intentional harm, such as vandalism or assaults. Presenters shared research illustrating how men and women perceive transit safety differently, noting that these perceptions further vary across age, race, ethnicity, geographical location, and socioeconomic status.

Discussions also highlighted global disparities in safety infrastructure and the availability of gender-disaggregated data, with developed countries having more advanced safety systems than their developing counterparts. Common concerns, such as insufficient lighting and a lack of trust in government systems, were noted as ongoing challenges to ensuring safe transit environments.

The session concluded with an interactive group exercise, led by Ryan and Menon, that engaged participants to explore practical strategies for improving mobility safety through a gender lens. Attendees worked in small groups to analyze real-world scenarios, applying equity-focused frameworks to identify challenges and propose solutions. Facilitators guided discussions on topics such as the role of technology in promoting safety, the impact of inclusive infrastructure design, and the use of storytelling to complement data-driven approaches.

Participants left the workshop with practical insights and a deeper understanding of how intersectional considerations can be integrated into transportation safety planning. The session emphasized collaboration, ethical frameworks, and the importance of disaggregated data in shaping transportation systems that are safe, accessible, and equitable for all.

Workshop 2: Empowering the Future—Gender Equity and AI in Transportation

Presenters

Mehri Mohebbi, University of Florida Transportation Institute, Moderator

Karen Philbrick, Mineta Transportation Institute, San Jose State University

Patricia Hu, U.S. Bureau of Transportation Statistics, U.S. DOT

Renee Autumn Ray, Hayden AI

Activity Co-Moderators/Organizations

Alyssa Ryan, University of Arizona

Nikhil Menon, Pennsylvania State University, Harrisburg

Key Takeaways

• Mitigating bias: Discussions emphasized the importance of addressing bias in datasets and algorithms by promoting team diversity, increasing transparency, and employing decision-support tools to identify gaps in lived experiences.
• Expanding the equity lens: Transportation planning benefits from considering intersectional disparities, including gender, disability, and socioeconomic status. For example, women with disabilities often experience compounded barriers, such as inaccessible transit vehicles or stations, inadequate sidewalk infrastructure, and the absence of audible or tactile signage.
• Promoting trust in AI: Public acceptance of AI in transportation, particularly autonomous vehicle systems, depends on clear communication, ethical design, and demonstrable safety improvements. Initiatives like the U.S. DOT’s “PANDA” (Pathway to Advancing Novel Data Analytics) program illustrate how data analytics can contribute to safety and reliability.
• Workforce development: Addressing workforce shortages and fostering gender-inclusive training in AI and transportation is crucial. Efforts could focus on early education, practical training programs, and raising awareness about technological innovations to attract diverse talent.
• Ethical frameworks: Discussions highlighted the need for ethical considerations at every stage of AI development. Questions of who benefits from AI-driven transportation systems and who might be excluded were noted as central to ensuring equitable implementation.
• Call for continued collaboration: Participants explored partnerships between academia, industry, and public agencies as key to advancing equitable and inclusive AI research and deployment. The role of data-driven decision-making across the AI lifecycle was emphasized as an essential component of sustainable progress.

Summary

The workshop, moderated by Mehri Mohebbi of the University of Florida Transportation Institute, brought together experts from academia, government, and industry to explore how AI is shaping transportation and its implications for equity and accessibility. Panelists Karen Philbrick, Patricia Hu, and Renee Autumn Ray provided perspectives on leveraging AI to improve transportation while addressing challenges relating to data equity, bias, and workforce development.

Ray opened the discussion by outlining AI’s potential to enhance transportation systems through tools such as predictive analytics and autonomous vehicle technologies. While these innovations hold transformative potential, Ray pointed to inherent biases in AI models, which stem from the data used to develop them. She underscored the importance of responsible AI development, transparency, and the creation of inclusive tools that account for the needs of historically underrepresented populations.

Following Ray’s presentation, Hu discussed the role of public trust in AI systems. Using examples such as FHWA’s “PANDA” initiative, she illustrated how flawed or incomplete datasets can reinforce existing inequities in transportation. Hu emphasized the value of harmonized datasets and carefully designed regulations to ensure that AI-powered solutions prioritize safety, privacy, and inclusivity.

Philbrick addressed workforce challenges in AI implementation, stressing the need for gender-inclusive development and training initiatives. She discussed the role of interdisciplinary approaches in broadening transportation equity beyond traditional engineering and planning fields. Philbrick also shared personal experiences related to trust-building in AI-driven transportation systems and explored societal solutions to increase confidence in these technologies.

In the second half of the workshop, participants engaged in an interactive group activity co-led by Alyssa Ryan of the University of Arizona and Nikhil Menon of Pennsylvania State University. This hands-on exercise examined real-world scenarios in which AI intersects with transportation equity. Participants worked in small groups to identify and examine challenges such as algorithmic bias or barriers to data accessibility that impact historically overburdened communities.

Facilitators guided discussions on applying equity-focused frameworks to these challenges, encouraging collaborative problem-solving. Building on these conversations, Ryan and Menon led participants through a structured exercise to propose strategies for mitigating AI-related disparities in transportation. Each group presented its findings and received feedback from facilitators and fellow attendees, reinforcing the value of participatory approaches in shaping inclusive, technology-driven transportation solutions.

The session concluded by emphasizing the transformative potential of AI when applied responsibly and inclusively in transportation. Discussions highlighted practical approaches for creating equitable AI-driven systems, including the development of inclusive design standards, trust-building mechanisms within communities, and workforce training programs that engage a diverse range of professionals.