Abstract
Dynamic message sign (DMS) systems aim to provide realistic, reliable, and real-time traffic information to roadway users. This study investigated the performance of a DMS present at a work zone that warns drivers of any imminent congestion. A work zone in Davenport, Iowa, was used as the test site to evaluate the performance and the proposed algorithm improvements. A typical automated DMS uses a dedicated sensor measuring speed or occupancy and simple thresholds to post messages warning drivers of the congestion by using a set of predefined messages such as “Traffic Delays Possible,” “Slow Traffic Ahead,” and “Stopped Traffic Ahead.” On the basis of field observation, it was found that these simplistic algorithms lead to a significant number of very short messages, erroneous messages during nighttime, and groups of messages that continuously alternate between different displays. This study first developed performance metrics to report the issues with the existing DMS automated programming logic and then proposed a machine learning–based real-time algorithm for improved operations.
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