Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan
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-...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
MDPI
2022
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/IC180100030 http://hdl.handle.net/20.500.11937/91005 |
| _version_ | 1848765483703074816 |
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| author | Zaitouny, A. Fragkou, A.D. Stemler, T. Walker, D.M. Sun, Y. Karakasidis, T. Nathanail, E. Small, Michael |
| author_facet | Zaitouny, A. Fragkou, A.D. Stemler, T. Walker, D.M. Sun, Y. Karakasidis, T. Nathanail, E. Small, Michael |
| author_sort | Zaitouny, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:35:58Z |
| format | Journal Article |
| id | curtin-20.500.11937-91005 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:58Z |
| publishDate | 2022 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-910052023-05-17T01:18:26Z Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan Zaitouny, A. Fragkou, A.D. Stemler, T. Walker, D.M. Sun, Y. Karakasidis, T. Nathanail, E. Small, Michael Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering traffic monitoring traffic management non-recurrent congestion major minor incident incident detection recurrence plots Quadrant Scan RECURRENCE QUANTIFICATION ANALYSIS PLOTS ANALYSIS TIME-SERIES Quadrant Scan incident detection major/minor incident non–recurrent congestion recurrence plots traffic management traffic monitoring Accidents, Traffic Algorithms Reproducibility of Results Time Factors Travel Reproducibility of Results Accidents, Traffic Algorithms Time Factors Travel 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. 2022 Journal Article http://hdl.handle.net/20.500.11937/91005 10.3390/s22082933 English http://purl.org/au-research/grants/arc/IC180100030 http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering traffic monitoring traffic management non-recurrent congestion major minor incident incident detection recurrence plots Quadrant Scan RECURRENCE QUANTIFICATION ANALYSIS PLOTS ANALYSIS TIME-SERIES Quadrant Scan incident detection major/minor incident non–recurrent congestion recurrence plots traffic management traffic monitoring Accidents, Traffic Algorithms Reproducibility of Results Time Factors Travel Reproducibility of Results Accidents, Traffic Algorithms Time Factors Travel Zaitouny, A. Fragkou, A.D. Stemler, T. Walker, D.M. Sun, Y. Karakasidis, T. Nathanail, E. Small, Michael Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan |
| title | Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan |
| title_full | Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan |
| title_fullStr | Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan |
| title_full_unstemmed | Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan |
| title_short | Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan |
| title_sort | multiple sensors data integration for traffic incident detection using the quadrant scan |
| topic | Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering traffic monitoring traffic management non-recurrent congestion major minor incident incident detection recurrence plots Quadrant Scan RECURRENCE QUANTIFICATION ANALYSIS PLOTS ANALYSIS TIME-SERIES Quadrant Scan incident detection major/minor incident non–recurrent congestion recurrence plots traffic management traffic monitoring Accidents, Traffic Algorithms Reproducibility of Results Time Factors Travel Reproducibility of Results Accidents, Traffic Algorithms Time Factors Travel |
| url | http://purl.org/au-research/grants/arc/IC180100030 http://hdl.handle.net/20.500.11937/91005 |