Conference Proceedings

Urban Sensing for Anomalous Event Detection: Distinguishing Between Legitimate Traffic Changes and Abnormal Traffic Variability

Masoomeh Zameni, Mengyi He, Masud Moshtaghi, Zahra Ghafoori, Christopher Leckie, James C Bezdek, Kotagiri Ramamohanarao, U Brefeld (ed.), E Curry (ed.), E Daly (ed.), B MacNamee (ed.), A Marascu (ed.), F Pinelli (ed.), M Berlingerio (ed.), N Hurley (ed.)

MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2018, PT III | SPRINGER INTERNATIONAL PUBLISHING AG | Published : 2019

Abstract

Sensors deployed in different parts of a city continuously record traffic data, such as vehicle flows and pedestrian counts. We define an unexpected change in the traffic counts as an anomalous local event. Reliable discovery of such events is very important in real-world applications such as real-time crash detection or traffic congestion detection. One of the main challenges to detecting anomalous local events is to distinguish them from legitimate global traffic changes, which happen due to seasonal effects, weather and holidays. Existing anomaly detection techniques often raise many false alarms for these legitimate traffic changes, making such techniques less reliable. To address this i..

View full abstract