Special Offer: Get 50% off your first 2 months when you do one of the following
Personalized offer codes will be given in each session

(ICCC 2020) Traffic Incident Detection from Massive Multivariate Time-Series Data

About This Webinar

Abstract: Smart-city infrastructure has the potential to improve the lives of anyone who finds themselves in an urbant environment. However, Vehicle Tranffic Man- agement (VTM) which ameleorates traffic congestion to improve productivity and reduce commuter stress is hampered by traffic incidents which introduce unexpected and chaotic variability into the traffic network. Automatic Incident Detection (AID) mechanisms aim to quickly and reliably identify vehicle traffic incidents to reduce the effect of traffic incidents on VTM. This paper shows that widely available magnetic traffic sensor data can be used with the AdaBoost Ma- chine Learning (ML) model to produce a reliable, light-weight AID mechanism to assist with VTM. A comparison with other ML models is also presented as well as ideas for future work.

Authors: Nicholas A Sterling and John Miller (University of Georgia, USA)

Email: nickbk@uga.edu, jam@cs.uga.edu

Who can view: Everyone
Webinar Price: Free
Featured Presenters
Hosted By
Services Society webinar platform hosts (ICCC 2020) Traffic Incident Detection from Massive Multivariate Time-Series Data
Services Society's Channel