Understanding the relationship between pavement raveling and traffic characteristics is important to pavement management and maintenance planning. In this work, we propose a framework to empirically quantify this relationship. It consists of an alignment method to tackle the inconsistent spatial-temporal scales of the raveling and traffic measurements and we propose spatial-temporal maps to qualitatively analyze and compare the data. A non-parametric correlation is done on the aligned raveling and traffic flow data. This framework is applied to five study areas in the Dutch highway network. The correlation analysis of the study areas provides empirical evidence to a commonly held theory that traffic flow has effects on raveling. Categorizing the correlation by lanes indicates that the raveling is homogeneous in the through or auxiliary lanes, and the severe raveled sections are parallel to the road discontinuity, suggesting the potential effect of mandatory lane changing on raveling development. The proposed framework can be employed in empirical raveling models that predict raveling based on traffic and other factors.
|Title of host publication||Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)|
|Number of pages||8|
|Publication status||Published - 2022|
|Event||2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) - Macau, China|
Duration: 8 Oct 2022 → 12 Oct 2022
Conference number: 25th
|Conference||2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)|
|Period||8/10/22 → 12/10/22|
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