Research

Our overarching goal is to explore and understand the complex interactions between pedestrians and traffic systems, particularly focusing on psychology, behavior, and safety of road users. To achieve this, we employ and develop cutting-edge virtual reality (VR) and data analysis tools to visualize and analyze the relevant behavioral and psychological degrees of freedom.

Our mission is to construct instruments and methodologies that empower us to delve into the questions we find most intriguing. This endeavor is facilitated by our team’s diverse expertise in various research themes and technologies. We have team members who have honed their skills in VR and traffic psychology, drawing from experiences in leading labs and bringing a wealth of knowledge to our collaborative projects. We further enhance our capabilities by integrating group members with different backgrounds and interests, working in unison on the physics of human behavior and the instrumentation needed to study it.

Here are some themes and techniques that we currently work on:

Virtual Reality Pedestrian Simulation. We have developed a novel VR-based platform that allows us to simulate real-world traffic scenarios. This tool enables us to safely and effectively study pedestrian reactions without risking actual harm. We have used this instrument to uncover critical insights into how pedestrians navigate traffic systems, especially in contexts where driving rules differ from their home country.

Habitual Behavior and Maladaptation. Our research delves into the habitual behaviors of pedestrians and how these habits can lead to maladaptation in unfamiliar traffic systems. We have used structural equation modeling to understand the complex relationships between hometown driving rules, length of stay, and pedestrian adaptation to new traffic environments.

Alcohol Influence on Pedestrian Behavior. We explore how alcohol consumption affects pedestrian crossing behaviors, particularly under unfamiliar traffic rules. Our studies highlight the negative impact of alcohol on perceptual-motor response and the importance of understanding these effects to mitigate risks for foreign travelers.

Inverse Reinforcement Learning for Pedestrian Motivations. We harness inverse reinforcement learning to uncover the safety and efficiency motivations that drive pedestrian crossing behaviors, especially under the influence of alcohol. This approach allows us to model and understand the complex interplay between observed behaviors and unobservable motivations.

… and more.

Through these research themes and techniques, we aim to advance our understanding of pedestrian behavior in traffic systems and contribute to the development of safer and more efficient urban environments. Our work is at the intersection of transportation science, behavioral psychology, and cutting-edge technology, pushing the boundaries of how we study and improve pedestrian safety.