APACS: a system for the automatic analysis and classification of conceptual patterns
Computational Intelligence
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In both at-grade intersections of highway and the area nearby, mixed traffic condition leads to severe traffic conflict, and causes traffic accident frequently happening. There are many factors with complicated interrelationships that affect at-grade intersection's safety, and many uncertainties exist between traffic safety countermeasures and their expected effectiveness. In the process of developing and implementing countermeasures of highway traffic accident prevention, it is an essential task of current traffic safety management both to deal with the above-mentioned uncertainties effectively and to make scientific evaluation and selection of the countermeasures with an aim of allocating social resource reasonably. Upon a systematic analysis of both the characteristics of the traffic accidents and highway safety countermeasures in at-grade intersection, this paper proposes the at-grade intersection safety countermeasure system. With CRF (Crash Reduction Factor) borrowed from USA applied, the effectiveness analysis of highway safety countermeasures in at-grade intersection is implemented. Based on the above, and with help of Inductive Learning Approach, the intellectualized evaluation model (IEM) for highway safety countermeasure of at-grade intersection is established.