Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan

Ayham Zaitouny, Athanasios D. Fragkou, Thomas Stemler, David M. Walker, Yuchao Sun, Theodoros Karakasidis, Eftihia Nathanail, Michael Small

Research output: Contribution to journalArticlepeer-review

Abstract

Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.

Original languageEnglish
Article number2933
JournalSensors
Volume22
Issue number8
DOIs
Publication statusPublished - 1 Apr 2022

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