Driving volatility helps us understand instantaneous driving decisions in a connected vehicle environment. The principal idea behind pursuing this NSF-funded project is to understand and reduce “driving volatility” in instantaneous driving decisions and increase driving and locational stability.
From a driver behavior perspective, an in-depth understanding of drivers’ policies related to instantaneous driving decisions made on different roadway functional classifications can help improve the effectiveness of transportation systems.
Overall, by studying driving volatility from different perspectives, the team is creating a new analytical framework to extract useful information from raw CAV data with the purpose of enhancing safety and fuel consumption efficiency. As the project progresses further, apart from the new knowledge generated regarding instantaneous driving volatility and key associated factors, newly developed algorithms will generate substantial new knowledge based on CAV data science.