Advanced Fuzzy-Logic-Based Traffic Incident Detection Algorithm
This study demonstrates an incident detection algorithm that uses the meteorological and traffic parameters for improving the poor performance of the automatic incident detection (AID) algorithms under extreme weather conditions and for efficiently using the meteorological devices on advanced freewa...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2021/8471683 |
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| _version_ | 1832567902912380928 |
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| author | Changhong Zhu Zhenjun Guo Jie Ke |
| author_facet | Changhong Zhu Zhenjun Guo Jie Ke |
| author_sort | Changhong Zhu |
| collection | DOAJ |
| description | This study demonstrates an incident detection algorithm that uses the meteorological and traffic parameters for improving the poor performance of the automatic incident detection (AID) algorithms under extreme weather conditions and for efficiently using the meteorological devices on advanced freeways. This algorithm comprises an incident detection module that is based on learning vector quantization (LVQ) and a meteorological influencing factor module. Field data are obtained from the Yuwu freeway in Chongqing, China, to verify the algorithm. Further, the performance of this algorithm is evaluated using commonly used criteria such as mean time to detection (MTTD), false alarm rate (FAR), and detection rate (DR). Initially, an experiment is conducted for selecting the algorithm architecture that yields the optimal detection performance. Additionally, a comparative experiment is performed using the California algorithm, exponential smoothing algorithm, standard normal deviation algorithm, and McMaster algorithm. The experimental results demonstrate that the algorithm proposed in this study is characterized by high DR, low FAR, and considerable suitability for applications in AID. |
| format | Article |
| id | doaj-art-e2920fb689694c55a9242da40c1bb808 |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-e2920fb689694c55a9242da40c1bb8082025-02-03T01:00:15ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2021-01-01202110.1155/2021/84716838471683Advanced Fuzzy-Logic-Based Traffic Incident Detection AlgorithmChanghong Zhu0Zhenjun Guo1Jie Ke2School of Computer Science and Engineering, Guilin University of Aerospace Technology, No. 2 Jinji Road, Guilin, Guangxi 541004, ChinaSchool of Computer Science and Engineering, Guilin University of Aerospace Technology, No. 2 Jinji Road, Guilin, Guangxi 541004, ChinaSchool of Computer Science and Engineering, Guilin University of Aerospace Technology, No. 2 Jinji Road, Guilin, Guangxi 541004, ChinaThis study demonstrates an incident detection algorithm that uses the meteorological and traffic parameters for improving the poor performance of the automatic incident detection (AID) algorithms under extreme weather conditions and for efficiently using the meteorological devices on advanced freeways. This algorithm comprises an incident detection module that is based on learning vector quantization (LVQ) and a meteorological influencing factor module. Field data are obtained from the Yuwu freeway in Chongqing, China, to verify the algorithm. Further, the performance of this algorithm is evaluated using commonly used criteria such as mean time to detection (MTTD), false alarm rate (FAR), and detection rate (DR). Initially, an experiment is conducted for selecting the algorithm architecture that yields the optimal detection performance. Additionally, a comparative experiment is performed using the California algorithm, exponential smoothing algorithm, standard normal deviation algorithm, and McMaster algorithm. The experimental results demonstrate that the algorithm proposed in this study is characterized by high DR, low FAR, and considerable suitability for applications in AID.http://dx.doi.org/10.1155/2021/8471683 |
| spellingShingle | Changhong Zhu Zhenjun Guo Jie Ke Advanced Fuzzy-Logic-Based Traffic Incident Detection Algorithm Advances in Fuzzy Systems |
| title | Advanced Fuzzy-Logic-Based Traffic Incident Detection Algorithm |
| title_full | Advanced Fuzzy-Logic-Based Traffic Incident Detection Algorithm |
| title_fullStr | Advanced Fuzzy-Logic-Based Traffic Incident Detection Algorithm |
| title_full_unstemmed | Advanced Fuzzy-Logic-Based Traffic Incident Detection Algorithm |
| title_short | Advanced Fuzzy-Logic-Based Traffic Incident Detection Algorithm |
| title_sort | advanced fuzzy logic based traffic incident detection algorithm |
| url | http://dx.doi.org/10.1155/2021/8471683 |
| work_keys_str_mv | AT changhongzhu advancedfuzzylogicbasedtrafficincidentdetectionalgorithm AT zhenjunguo advancedfuzzylogicbasedtrafficincidentdetectionalgorithm AT jieke advancedfuzzylogicbasedtrafficincidentdetectionalgorithm |