LAUSR

Transportation jurisdictions should monitor mobility and reliability of roadway systems in order to adequately invest capacity expansion and deployment of ITS technologies to alleviate congestion effectively and efficiently. In recent years, several link-based bottleneck identification schemes have estimated bottleneck impact factors on freeways based on the characteristics of congestion. However, those have used congestion data with no attention to distinguishing recurrent level of at the same “bottleneck” location. Most existing studies that distinguish between recurrent and non-recurrent congestion have focused on separating non-recurrent congestion from recurrent congestion only for intensity of congestion using parameters for the speed distribution in a time of day in a segment or point. As such, this study introduced a data-driven procedure for quantifying spatiotemporal “recurrent” congestion impact. In addition, this study used spatiotemporally historic congestion information and generated stochastic spatiotemporal congestion distributions in terms of congestion types. Using the relationship between the distributions of recurrent and non-recurrent congestion occurring at bottlenecks, the bottleneck impacts were estimated by capturing spatial and temporal impact of recurrent bottleneck from that of non-recurrent congestion occurring at recurrent bottleneck. The proposed approach represents a significant improvement in the understanding and monitoring of mobility on freeways. This can be directly applied to evaluate and rank bottlenecks.