Farid Peiravian, Ph.D.

Currently, I am not accepting any new students.

I hold a Ph.D. in Transportation Planning and Engineering, with expertise in applying geospatial technologies, including Geospatial Artificial Intelligence (GeoAI), to Civil and Transportation Engineering. With academic training in Civil Engineering at the B.Sc., M.Sc., and Ph.D. levels, combined with practical experience in GIS, my work focuses on spatial analysis, sustainability, resiliency, safety, and accessibility in transportation and urban systems.

My research interests center on the application of GeoAI to urban resiliency, active transportation, public transit, and rail transportation. I bring a strong combination of practical experience, technical proficiency, and analytical expertise to my work as a researcher, educator, and practitioner.

Professional Unified Vision for Sustainable & Resilient Mobility

My work, research, and teaching are driven by a singular mission: to design intelligent multimodal transportation systems that prioritize sustainability, resiliency, accessibility, and safety. By combining the human-scale mechanics of Active Transportation, the high-capacity efficiency of Public and Rail Transportation, and the predictive power of Geospatial Artificial Intelligence (GeoAI), my work seeks to transform how we model, build, and experience urban mobility networks.

I believe that a truly robust urban landscape requires a holistic, spine-and-spur transportation network built to withstand the challenges of the 21st century. High-capacity Public Transportation and Rail Transportation systems serve as the sustainable backbone of this network, essential for lowering carbon emissions, reducing regional congestion, and shaping dense, vibrant Transit-Oriented Developments (TOD).

To ensure long-term resiliency, my approach treats these networks as adaptive systems. A resilient transit network must be able to absorb disruptions, whether from extreme events, infrastructure failures, or shifting demographic demands, by dynamically rerouting transit assets and maintaining continuous service.

Crucially, this transit spine is only as viable as its first- and last-mile connections. This is where Active Transportation (walking, cycling, and rolling) plays its vital, complementary role. By building additional, flexible networks of active infrastructure, we create a more resilient urban ecosystem where communities have reliable, low-carbon alternatives to personal vehicular travel.

Elevating Accessibility and Safety through GeoAI

To optimize this interconnected ecosystem, my research leverages GeoAI, combining spatial data science, machine learning, and advanced geographic information systems (GIS). Modern transportation networks require dynamic, intelligent solutions to protect vulnerable road users and guarantee equitable access for all. My research applies GeoAI across two critical pillars:

• Universal Accessibility: Universal mobility means designing networks that serve everyone, including individuals with disabilities or mobility challenges. My work uses computer vision, satellite imagery, and street-view data to automate condition assessments of sidewalks, rail corridors, and transit stops. By digitally mapping infrastructure gaps, we can proactively eliminate physical barriers and ensure seamless, ADA-compliant accessibility across the entire transit network.
• Proactive Transportation Safety: True systemic safety requires shifting from a reactive approach to a predictive one. I utilize spatial deep learning and big geospatial data (such as micromobility feeds and GPS trajectories) to analyze the complex intersections where active transportation meets heavy transit infrastructure. By modeling conflict zones and passenger flows, we can predict and mitigate safety hazards before accidents occur, ensuring a secure environment for pedestrians, cyclists, and transit passengers alike.

Educating the Next Generation of Infrastructure Leaders

I am deeply committed to translating this cutting-edge research directly into the classroom. For that, I am constantly pioneering the development of brand-new, dedicated, relevant courses within my department.

This curriculum moves beyond traditional engineering silos, challenging students to view rail, transit, and active mobility not merely as isolated geometric or pavement calculations, but as a living, tech-forward ecosystem. By dismantling academic silos, my goal is to equip my students, the future engineers, planners, and civic leaders, with the spatial data literacy, technical skills, and community-first mindset needed to build the safe, accessible, sustainable, and resilient cities of tomorrow.

Areas of Interest:

• GeoAI (Applications of AI in GeoSpatial Analysis)
• Active Transportation & Complete Streets
• Rail Transportation
• Public Transportation
• Transportation Safety & Accessibility

GeoAI Lab is an interdisciplinary research hub advancing the integration of artificial intelligence with geospatial science to solve complex spatial problems. It develops intelligent methods for engineering surveying, remote sensing, GPS, drones, and GIS analytics, enabling precise data capture, modeling, and visualization. The lab supports civil engineering and transportation planning through predictive modeling, infrastructure monitoring, and smart mobility systems. It enhances disaster resilience by mapping hazards, forecasting impacts, and optimizing emergency response. By combining data-driven algorithms with spatial design, the lab fosters innovative, sustainable, and resilient solutions for built and natural environments while supporting education, collaboration, and policy innovation.

ActiveTransport Lab is a research and design hub dedicated to advancing safe, accessible, and sustainable walking and bicycling systems through a people-centered approach. It focuses on modeling travel behavior, evaluating infrastructure performance, and designing networks that prioritize people over vehicles. Guided by Complete Streets principles, the lab develops inclusive solutions that serve users of all ages and abilities, integrating sidewalks, bike lanes, crossings, and traffic calming measures. Using data-driven tools and community engagement, it supports equitable mobility, public health, and environmental goals. The lab collaborates with planners and engineers to create vibrant, connected streets that encourage active transportation and enhance quality of life.

RailTransport Lab is a research and design hub dedicated to advancing safe, reliable, and sustainable rail transport systems through an integrated engineering approach. It focuses on modeling track alignment mechanics, evaluating infrastructure performance, and optimizing fixed-plant networks that prioritize operational efficiency and structural integrity. Guided by industry-recommended practical standards, the lab develops resilient solutions that support modern railway systems management, integrating track design, scheduling, planning, and environmental considerations. Using data-driven diagnostics, such as machine vision inspection and predictive safety modeling, it supports derailment prevention, corridor modernization, and environmental compliance goals. The lab collaborates with industry professionals and academic researchers to cultivate next-generation railway engineers and enhance long-distance mobility networks.

Ph.D. in Transportation Planning
M.Sc. in Transportation Engineering
B.Sc. in Civil Engineering