Urban Traffic Intersection Incident Prediction Using AI Algorithm

Abstract

utomatic 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. Offline 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.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.2991/jcis.2006.302 under the license https://creativecommons.org/licenses/by

• https://download.atlantis-press.com/article/302.pdf

• https://dblp.uni-trier.de/db/conf/jcis/jcis2006.html#KongC06,

https://www.atlantis-press.com/proceedings/jcis2006/302,

https://academic.microsoft.com/#/detail/2019666429 under the license cc-by