Anomaly Detection in Spatiotemporal Patterns
The paper Anomaly Detection in Spatiotemporal Patterns: A Case Study of Brazilian Traffic Accidents During the COVID-19 Lockdown just got accepted for publication at the 35th Brazilian Conference on Intelligent Systems (BRACIS). The paper is authored by Gabriel Cesar Silveira, Benjamin G. Moreira1, Diogo Nardelli Siebert, and Ricardo J. Pfitscher1.
The paper investigates how traffic accident profile changed in Brazil due to the lockdown of the COVID-19 pandemic, analyzing different categories of occurrences separately. The method uses mainly public data on highway incidents and applies spatial correlation and linear regression models. The combined indicator of dissimilarity and spatiotemporal autocorrelation identifies regional anomalies when regressed with data before and during the pandemic. The methodology revealed that the lockdown affected accident characteristics differently across states, leading to changes in state-level and regional rates that could be overlooked in exploratory analyses or when neglecting spatial relationships. This case study can inform public policies on accident prevention and guide future research on the impacts of lockdowns on human behavior.
As detailed in the conference website: “BRACIS is one of the most important events in Brazil for researchers aiming to publish significant and novel results in the fields of Artificial Intelligence (AI) and Computational Intelligence (CI). It was established through the merger of the two most prominent scientific events in Brazil dedicated to AI (SBIA) and CI (SBRN).”
As soon as the paper is out, it will be linked in the publications page.