Effectiveness of VMS Using Empirical Loop Detector Data

This study employs traffic data and incident data from the Twin Cities of Minneapolis and St. Paul, Minnesota. The conclusion in this study provides guidance on making policy about investing in VMS systems. Few studies utilize empirical traffic data. They either use costly surveys or conduct traffic simulation, which are expensive and may not conform well to reality. This study uses empirical traffic flow and occupancy data on both mainline and ramps, collected every 30 seconds to estimate the effectiveness of VMS. The variation of diversion rate before and after warning messages is statistically tested. A discrete choice model is built to predict what proportion of the vehicles diverts to the alternative routes given the characteristics of different messages. So in this study, the effectiveness of VMS is evaluated in two ways: (1) Using a discrete choice model to estimate the response of drivers to messages provided by VMS; (2) Statistical analysis on the variation of diversion rate with and without VMS. A before and after study allows us to quantitatively evaluate the network wide benefit of VMS systems. We define and evaluate MOE before and after installation of VMS for selected corridors. The improvement (or worsening) of traffic conditions and system performance before and after installation of VMS can be measured by these MOE terms. This study will estimate the travel time saving, safety improvement, as well as other benefits.