How COVID-19 Changed Willingness to Adopt Autonomous Vehicles
August 1, 2021: New paper on “Accelerating the Adoption of Disruptive Technologies: The Impact of COVID-19 on Intention to Use Self-Driving Vehicles“ with Maher Said and Prof. Amanda Stathopoulos on ArXiv. We find that willingness to adopt autonomous vehicles increased during COVID, especially for individuals who are younger, liberal and frequent users of shared modes. Read more on ArXiv.
Improving Traffic Congestion with Autonomous Vehicles: An Agent-Based Model
Traffic congestion is a major concern of present societies and has plagued cities around the world for millennia. Historical sources mention cases of traffic congestion within ancient cities like Rome, Pompeii and Xanten. Today, despite the advancements in infrastructure and technology, congestion remains one of the leading concerns of urban societies and is a rising problem in metropolitan areas across the globe. In the United States alone, congestion results in an annual economic loss of $305 billion, not to mention the environmental costs as fuel consumption and vehicle emissions increase when traffic is present. Contrary to popular belief, traffic jams on highways are not necessarily due to outside factors such as accidents or construction, but to the phenomenon of phantom traffic jams. A tool for congestion mitigation, which is still in its infancy, is the use of autonomous vehicles. Autonomous vehicles have been shown theoretically and experimentally to have the ability to dissipate traffic waves under restricted conditions. These videos using agent-based simulations in NetLogo show how autonomous vehicles can help dissipate traffic jams not only in one-lane, but multi-lane highway environments. NetLogo is a software created by Prof. Uri Wilensky that can be used for agent-based modeling.
Download files for the February 10, 2022 Transportation Club workshop on “Introduction to Agent-Based Modeling with NetLogo”