Abstract
SummaryThis study presents two models for proactive variable speed limit (VSL) control on a recurrently congested freeway segment. The proposed model uses embedded traffic flow relations to predict the evolution of congestion patterns over the projected time horizon, and then computes the time‐varying optimal speed limit to smooth traffic flows. To contend with the uncertainties associated with drivers' responses to VSL control, this study has also proposed an advanced model that further adopts Kalman Filter to enhance the traffic state estimation. Both models have been investigated with two control objectives—travel time minimization and speed variance minimization. Our extensive simulation analysis with a VISSIM simulator, calibrated with field data from our previous VSL field demonstration, has revealed the benefits of the proposed VSL control models. Also, the experimental results indicated that the proposed advanced models with both control objectives can significantly reduce the travel time over the recurrent bottleneck locations. With respect to several selected measure of effectiveness (MOEs), such as average number of stops and average travel time, the research results confirm that the control models with the objective of minimizing speed variance can offer the promising properties for field implementation. Copyright © 2014 John Wiley & Sons, Ltd.
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