GC-462 Traffic Pattern Analysis and Anomaly Detection Using Large-scale Trajectory Data
Location
https://ccse.kennesaw.edu/computing-showcase/cday-programs/fall23program.php
Document Type
Event
Start Date
30-11-2023 4:00 PM
Description
With the advancement of IoT and improved computing capabilities, real-time vehicle and road user trajectories are easily accessible through advanced traffic sensing, replacing time-consuming manual checks. This study employs machine learning to analyze extensive trajectory data, focusing on anomaly detection in traffic patterns. It investigates efficient techniques for processing time-series data using an open-source dataset (InD dataset). The procedure involves data preprocessing, feature extraction, machine learning model training, and anomaly detection at 4 intersections. Irregular paths reveal abnormal driving behavior like U-turns and unexpected stops. The study highlights their impact on traffic management and safety and discusses potential applications in vehicle-to-infrastructure alert systems.
GC-462 Traffic Pattern Analysis and Anomaly Detection Using Large-scale Trajectory Data
https://ccse.kennesaw.edu/computing-showcase/cday-programs/fall23program.php
With the advancement of IoT and improved computing capabilities, real-time vehicle and road user trajectories are easily accessible through advanced traffic sensing, replacing time-consuming manual checks. This study employs machine learning to analyze extensive trajectory data, focusing on anomaly detection in traffic patterns. It investigates efficient techniques for processing time-series data using an open-source dataset (InD dataset). The procedure involves data preprocessing, feature extraction, machine learning model training, and anomaly detection at 4 intersections. Irregular paths reveal abnormal driving behavior like U-turns and unexpected stops. The study highlights their impact on traffic management and safety and discusses potential applications in vehicle-to-infrastructure alert systems.