title: |
Urban Traffic Intersection Incident Prediction Using AI Algorithm |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
ISBN: |
978-90-78677-01-7 | |
ISSN: |
1951-6851 | |
DOI: |
doi:10.2991/jcis.2006.302 (how to use a DOI) | |
author(s): |
Yaguang Kong, Huakui Chen |
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corresponding author: |
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publication date: |
October 2006 |
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keywords: |
Incident Detection, Neural Network, Fuzzy Logic |
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abstract: |
Automatic incident detection and characterization is urgently require in the development of advanced technologies used for reducing non-recurrent traffic congestion on urban traffic. This paper presents a new method using data mining to identify automatically freeway incidents. As a component of a real-time traffic adaptive control system for signal control, the algorithm feeds an incident report to the system’s optimization manager, which uses the information to determine the appropriate signal control strategy. Off-line tests were conducted to substantiate the performance of the proposed incident detection algorithm based on simulated data. The test results indicate the feasibility of achieving real-time incident detection utilizing the proposed method. |
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |