Evaluating Performance of a Proactive Optimal Variable Speed Limit Control Using Different Objective Functions

Abstract

Proactive optimal Variable Speed Limit (VSL) control is a promising solution to improve both the freeway capacity and the travel time. Despite this well-recognized fact, most of the previous studies found that the VSL control strategy was capable of improving only the freeway travel time. This finding hinders the overall mobility benefits from the computationally intensive proactive optimal VSL control and its credibility compared to the other control strategies those do not involve real-time traffic state prediction and optimization. To resolve this issue, in this paper, the authors adopt a previously developed novel VSL control strategy that explicitly considers traffic characteristics at active bottleneck, and its upstream-downstream links. Then, the performance of the control strategy has been evaluated within a predictive control framework, considering several objective functions (including stand-alone objective and combination of multiple objectives). Moreover, within the control framework, DynaTAM-VSL has been used as a prediction model. To find the optimal values of control variable at each control horizon, the authors adopt the multi-start Sequential Quadratic Programming, which starts a local solver from multiple start points. A sensitivity analysis is performed to see how the weight parameters of the partial objectives influence the VSL performance, which resolves existing paradoxical results of VSL mobility benefits. In addition, the model is simulated for a range of demand inputs, and analysis is carried out to see how efficient the VSL control is to improve the freeway mobility.