Arash Tavakoli, PhD
Assistant Professor, Civil and Environmental Engineering | Villanova University
Villanova, PA, UNITED STATES
Prof. Arash Tavakoli, Ph.D., leads the Human Centered Cities Lab, focusing on human factors, human-centered design, and transport safety.
Spotlight
Areas of Expertise (7)
Human Factors in Transportation Engineering
Transportation Safety
Human Centered Design
Human Sensing
Naturalistic Driving
Driving Simulator Research
Well-Being
Biography
Arash is an Assistant Professor in Transportation Engineering at Villanova University. His research is focused on humanizing smart cities. He is interested in building systems that can understand, adapt, and communicate with humans. Before joining Villanova University, he was a postdoctoral scholar at Stanford University. He earned his Ph.D. from the University of Virginia.
Education (3)
University of Virginia: PhD, Civil and Environmental Engineering
Virginia Tech: MSc, Civil and Environmental Engineering
Sharif University of Technology: BSc, Civil and Environmental Engineering
Select Accomplishments (4)
Graduate Student Proposal Award, Commonwealth Cyber Initiative (professional)
2020
Service Graduate Student Award, Link Lab (professional)
2020
Environmental Resilience Institute Graduate Fellowship (professional)
2018
CMAA NCC Scholarship, Construction Management Association of America (professional)
2018
Links (2)
Select Media Appearances (1)
After a Horrific Crash Killed a Cyclist, Advocates are Again Pushing for Barriers Between Bike Lanes and Cars
The Philadelphia Inquirer online
2024-07-19
Any kind of bike lane can be an improvement, but those with hardened barriers protect cyclists better and deter motorists from driving in them because they don’t want to damage their cars, said Arash Tavakoli, assistant professor of civil and environmental engineering at Villanova University. His research focuses on how to make cities safer for pedestrians and cyclists, as well as the psychological effects of urban infrastructure. Ideally, he said, strong bike lanes would be paired with some stepped-up enforcement against speeding as well as design elements that slow vehicles in densely packed areas, such as “road diets,” narrowing the traffic lanes, and the use of traffic-calming devices like speed tables.
Research Grants (1)
From Data to Design: Enhancing Pedestrian Infrastructure for Well-Being through Mobile Sensing and Experience Sampling in the Wild
National Science Foundation $199,000
June 2024 In his project, “From Data to Design: Enhancing Pedestrian Infrastructure for Well-Being through Mobile Sensing and Experience Sampling in the Wild,” Tavakoli and his team of researchers will work with focus groups in a suburban setting to analyze how various walkways, even safe ones, affect well-being metrics such as stress levels. Based on their findings and with direct collaboration with the community, the team will develop and test virtual sidewalk models designed to improve the walking experience. The researchers aim to pinpoint exactly what environmental factors contribute to lowering well-being metrics and how modifications can mitigate these adverse effects.
Select Academic Articles (6)
Unveiling the Impact of Cognitive Distraction on Cyclists Psycho-Behavioral Responses in an Immersive Virtual Environment
IEEE Transactions on Intelligent Transportation Systems2024 The National Highway Traffic Safety Administration reported that the number of bicyclist fatalities has increased by more than 35% since 2010. One of the main reasons associated with cyclists’ crashes is the adverse effect of drivers’ cognitive distractions. However, very limited studies have evaluated the impact of cyclists’ cognitive distraction on their behaviors and safety. This study leverages an Immersive Virtual Environment (IVE) simulation environment to explore the effect of secondary tasks on cyclists’ cognitive distraction by evaluating their behavioral and physiological responses. Specifically, by recruiting 75 participants, this study explores the effect of listening to music versus talking on the phone as standardized secondary tasks on participants’ behavior (i.e., speed, lane position, input power, head movement) as well as, physiological responses including participants’ heart rate variability and skin conductance metrics.
How are drivers’ stress levels and emotions associated with the driving context? A naturalistic study
Journal of Transport & Health2023 Understanding and mitigating drivers' negative emotions, and stress levels, is of high importance for enhancing road safety, and human well-being. While detecting drivers' stress and negative emotions can significantly help with this goal, understanding what might be associated with increases in drivers' negative emotions and high stress level, might better help with planning interventions, which has not been explored in detail. Methods: In this study, by using a naturalistic driving study database, we analyze the changes in drivers' heart rate and facial expressions with respect to the changes in the driving scene, including road objects and the dynamical relationship between the ego vehicle and the lead vehicle. Results: Our results indicate that different road objects might be associated with varying levels of increase in drivers' HR as well as different proportions of negative facial emotions detected through computer vision. Larger vehicles on the road, such as trucks and buses, are associated with the highest amount of increase in drivers' HR as well as negative emotions.
Psycho-physiological measures on a bicycle simulator in immersive virtual environments: how protected/curbside bike lanes may improve perceived safety
Transportation Research Part F: Traffic Psychology and Behaviour2023 As a healthier and more sustainable way of mobility, cycling has been advocated by literature and policy. However, current trends in bicyclist crash fatalities suggest deficiencies in current roadway design in protecting these vulnerable road users. The lack of cycling data is a common challenge for studying bicyclists’ safety, behavior, and comfort levels under different design contexts. To understand bicyclists’ behavioral and physiological responses in an efficient and safe way, this study uses a bicycle simulator within an immersive virtual environment (IVE). Off-the-shelf sensors are utilized to evaluate bicyclists’ cycling performance (speed and lane position) and physiological responses (eye tracking and heart rate). Participants bike in a simulated virtual environment modeled to scale from a real-world street with a shared bike lane (sharrows) to evaluate how the introduction of a curbside bike lane and a protected bike lane with flexible delineators may impact perceptions of safety, as well as behavioral and psycho-physiological responses.
Occupant privacy perception, awareness, and preferences in smart office environments
Scientific Reports2023 Building management systems tout numerous benefits, such as energy efficiency and occupant comfort but rely on vast amounts of data from various sensors. Advancements in machine learning algorithms make it possible to extract personal information about occupants and their activities beyond the intended design of a non-intrusive sensor. However, occupants are not informed of data collection and possess different privacy preferences and thresholds for privacy loss. While privacy perceptions and preferences are most understood in smart homes, limited studies have evaluated these factors in smart office buildings, where there are more users and different privacy risks. To better understand occupants’ perceptions and privacy preferences, we conducted twenty-four semi-structured interviews between April 2022 and May 2022 on occupants of a smart office building. We found that data modality features and personal features contribute to people’s privacy preferences. The features of the collected modality define data modality features – spatial, security, and temporal context.
Multimodal driver state modeling through unsupervised learning
Accident Analysis & Prevention2022 Naturalistic driving data (NDD) can help understand drivers’ reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver’s state and behavioral patterns. Unsupervised analysis of NDD can be used to automatically detect different patterns from the driver and vehicle data. In this paper, we propose a methodology to understand changes in driver’s physiological responses within different driving patterns. Our methodology first decomposes a driving scenario by using a Bayesian Change Point detection model. We then apply the Latent Dirichlet Allocation method on both driver state and behavior data to detect patterns. We present two case studies in which vehicles were equipped to collect exterior, interior, and driver behavioral data. Four patterns of driving behaviors (i.e., harsh brake, normal brake, curved driving, and highway driving), as well as two patterns of driver’s heart rate (HR) (i.e., normal vs. abnormal high HR), and gaze entropy (i.e., low versus high), were detected in these two case studies.
HARMONY: A Human-Centered Multimodal Driving Study in the Wild
IEEE Access2021 The National Highway Traffic Safety Administration reported that the number of bicyclist fatalities has increased by more than 35% since 2010. One of the main reasons associated with cyclists’ crashes is the adverse effect of drivers’ cognitive distractions. However, very limited studies have evaluated the impact of cyclists’ cognitive distraction on their behaviors and safety. This study leverages an Immersive Virtual Environment (IVE) simulation environment to explore the effect of secondary tasks on cyclists’ cognitive distraction by evaluating their behavioral and physiological responses. Specifically, by recruiting 75 participants, this study explores the effect of listening to music versus talking on the phone as standardized secondary tasks on participants’ behavior (i.e., speed, lane position, input power, head movement) as well as, physiological responses including participants’ heart rate variability and skin conductance metrics.
Social