An analysis of the New York City traffic volume, vehicle collisions, and safety under COVID-19.

Introduction And Method: We use the arguably exogenous intensity of COVID-19 as an instrument in order to study the relationship between traffic volume and vehicle collisions in a large metropolitan area. We correlate data from multiple sources and consider a time interval ranging from about one year before to one year after the pandemic breakout, which allows to account for preexisting seasonal patterns as well as the disruption brought by the pandemic.

Results: We identify that increased traffic volume is associated with significantly more collisions with a robust elasticity varying between 1.

A Methodology for Validating Diversity in Synthetic Time Series Generation.

In order for researchers to deliver robust evaluations of time series models, it often requires high volumes of data to ensure the appropriate level of rigor in testing. However, for many researchers, the lack of time series presents a barrier to a deeper evaluation. While researchers have developed and used synthetic datasets, the development of this data requires a methodological approach to testing the entire dataset against a set of metrics which capture the diversity of the dataset.