, Professor, UMD-Civil Engineering
Understanding the behavior of weaving flows and estimating the effects of time-variant traffic conditions on the capacity of weaving areas is critical for developing effective operational and design strategies for freeway systems that can maximize existing capacity. This project built on previous work that identified and classified major weaving areas in the Twin Cities freeway network. In this study, the researchers specifically addressed capacity issues at multiple weaving areas, where more than two weaving sections are sequentially located. An adaptive procedure was developed to estimate the time-variant capacity at consecutive weaving areas in real time. The proposed procedure uses the volume/occupancy data commonly available from single loop detectors and estimates the maximum total volume that can enter a given freeway weaving segment through time. The behavior at several weaving sites with consecutive weaving segments were analyzed, using loop and video data as well as visual observation. The online identification process using Kalman filter techniques reduces estimation errors by continuously updating the parameters of the underlying models with the most recently measured data. The test results with real data showed that the proposed procedure can estimate the upper limit values of the mainline flow approaching given weaving segments with reasonable accuracy. This procedure addresses the effects of entrance ramp flows, which can be controlled through ramp metering, on the maximum possible mainline volume approaching weave areas. The procedure may be directly applicable in improving ramp metering operations, and in the development of better design of freeway weaving segments.