文章基本信息

标题：Optimization of Pavement Inspection Schedule with Traffic Demand Prediction
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作者：Soomin Woo ; Soomin Woo ; Hwasoo Yeo 等
期刊名称：Procedia - Social and Behavioral Sciences
印刷版ISSN：1877-0428
出版年度：2016
卷号：218
页码：95-103
DOI：10.1016/j.sbspro.2016.04.013
语种：English
出版社：Elsevier
摘要：AbstractIn order to improve the current pavement inspection systems, many studies have attempted to produce a flexible inspection schedule by minimizing the lifecycle cost of pavement management system. However, these methods do not incorporate the risk of untimely inspection nor adopt the state-of-art intelligent transportation resources, such as traffic data from various infrastructures, flow prediction models with high accuracy, and empirical-mechanistic models for pavement deterioration process. Therefore, this paper proposes a framework to optimize a flexible inspection schedule within a risk boundary defined by pavement state prediction with traffic flow data and a more mechanistic deterioration model. The results validate the outperformance of the optimized inspection over two conventional inspection schemes – 1-year and 2-year regular inspection. Also the optimized inspection is comparably more robust than the regular inspections with different traffic scenarios due to the uncertainty risk taken by regular inspections.
关键词：Pavement Inspection;Traffic Demand;Maintenance Optimization