A collaborative variable speed-limit control for continuous bottlenecks on freeways
Owing to restricted traffic capacities,highway bottlenecks typically result in traffic con-gestion,thus causing prolonged commutes,increased fuel consumption,and reduced driving com-fort.In areas with successive bottlenecks,the road network is susceptible to chain reactions,thereby increasing the risk of widespread accidents.Existing congestion-control approaches target isolated bottlenecks,where several individual strategies are deployed across multiple bottleneck areas.The control efficacy is hindered by inadequate coordination between traffic conditions and control param-eters,which exacerbates traffic congestion owing to misaligned strategy coordination.Leveraging the model-predictive-control method,this study proposes a cooperative variable speed-limit ap-proach to address continuous multi-bottleneck congestion.Initially,a cellular transmission model for ongoing multi-bottleneck scenarios is enhanced and multiple bottleneck formations are simulated to reduce traffic capacities,erratic traffic-flow fluctuations,and the effects of variable speed-limit con-trol.Second,the Lighthill-Whitham-Richards model,which is based on the Lagrangian coordinate system,is refined to precisely forecast the stochastic state of congested traffic flows.Third,a feed-back-driven variable speed-limit control strategy is formulated using the comprehensive traffic opera-tion status from multi-bottleneck segments as input.The control framework collaborates seamlessly with the variable speed-limit strategy customized for multi-bottleneck scenarios.Finally,several sim-ulation experiments are conducted to evaluate the efficiency of the proposed control strategy in ad-dressing congestion at successive bottlenecks by comparing its effects with those of other strategies.The results reveal that the proposed collaborative variable speed-limit strategy effectively mitigates ongoing multiple congestion scenarios and outperforms other strategies considerably.In general,it re-duces the vehicle travel time and delay time by 21.3%and 70.6%,respectively.
万方数据
维普
