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.