Cooperative Control Strategy for Variable Speed Limit and Dynamic Hard Shoulder Running of Highway On-Ramp Merging Area

<div class="section abstract"><div class="htmlview paragraph">To meet the traffic control demands of highway merging areas and address the accuracy error of traffic flow prediction models, a cooperative control strategy based on adaptive prediction horizon Model Predictive Control (MPC) has been proposed for variable speed limits (VSL) and dynamic hard shoulder running (HSR). Firstly, the METANET model was improved based on the characteristics of merging areas and the impact of cooperative control strategy. Secondly, to mitigate the negative impact of the METANET prediction errors on control effectiveness, a fuzzy rule-based adaptive prediction horizon controller is designed. Thirdly, a cooperative control strategy for VSL and dynamic HSR is formulated under the MPC framework, aiming to optimize Total Time Spent(TTS)and Total Travel Distance (TTD), using genetic algorithms equipped with sliding time windows for resolution. Finally, using actual traffic flow data from Changtai Highway, simulation experiments are conducted, involving four scenarios: HSR-VSL control, VSL-only control, HSR-only control, and no control. In the cooperative control scenario, both adaptive and fixed prediction horizon approaches are considered. Results show that the proposed HSR-VSL control strategy with fixed prediction horizon reduces the total travel time and mainline density by 20.02% and 10.78% respectively, outperforming single strategies (only HSR or VSL). Compared to a fixed prediction horizon, the VSL-HSR with adaptive prediction horizon delivers even better results, reducing total travel time and mainline density by 24.53% and 12.94% respectively, proving the effectiveness of the cooperative control strategy and the adaptive prediction horizon controller.</div></div>