TITLE:
Real-Time Road Traffic Anomaly Detection
AUTHORS:
Jamal Raiyn, Tomer Toledo
KEYWORDS:
Anomaly Traffic, Detection Scheme, Moving Average, Intelligent Transportation System
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.4 No.3,
July
31,
2014
ABSTRACT:
Many
modeling approaches have been proposed to help forecast and detect incidents. Accident
has received the most attention from researchers due to its impacts
economically. The traffic congestion costs billions of dollars to economy. The
main reasons of major percentage of traffic congestion are the incidents. Road
accidents continue to increase in digital age. There are many reasons for road
accidents. This paper will discuss and introduce new algorithm for road
accident detection. Various forecast schemes have been proposed to manage the
traffic data. In this paper we will introduce road accident detection scheme
based on improved exponential moving average. The proposed traffic incident
detection algorithm is based on the automatic exponential moving average
scheme. The detection algorithm is based on analyzing the collected traffic flow
parameters. The detection algorithm is based on analyzing the collected traffic
flow parameters. In addition a real-time accident forecast model was developed
based on short-term variation of traffic flow characteristics.