# Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
###### Abstract
In this case, VSL usually serves as a complement to RM, when a stand-alone RM system is unable to achieve predefined traffic parameter values (usually vehicle density or flow) within desired limits on a controlled segment of an urban motorway [7]. Studies [2; 7; 8] have shown that the VSL system can prevent or reduce the effect of capacity drop caused by increased traffic demand on the controlled urban motorway segment. By reducing the speed limit posted on the VMS placed in front of the bottleneck area, the effect of reducing the vehicle inflow to the bottleneck area is achieved. Today's traffic control systems increasingly rely on intelligent data processing. For example, the City Brain system is an urban traffic control platform that includes various traffic control systems alongside a large number of sensors for traffic data collection. It is based on the elastic computation and large-scale data processing platform of Alibaba Cloud, integrated with the capabilities of interdisciplinary fields such as machine vision, large-scale topological network computation, and traffic flow analysis [9]. It is capable of collecting massive multi-source data, real-time processing, and intelligent computing. It incorporates extremely large-scale and multi-source data processing, Machine Learning (ML), and formulating global optimal traffic control strategy. Emerging technologies in Autonomous Vehicles (AVs) and Connected and Autonomous Vehicles (CAVs) are likely to improve the way VSL and RM will operate in the coming years. CAVs can be incorporated with VSL so they automatically comply with the posted speed limits. In the initial phase of deployment, AVs and CAVs will cohabit with Human-Driven Vehicles (HDVs) in mixed traffic flows. The term mixed traffic flows thus refers to traffic flows that contain conventional HDVs, AVs and CAVs with different penetration rates. AVs represent vehicles that can receive traffic information from different proprioceptive sensing technologies integrated within the vehicle such as cameras, sonars, navigation, radar, and lidar. They are characterized by high compliance with traffic laws, shorter time and space headway, and lower gap acceptance. CAVs are similar to AVs with additional features that enable communication with other vehicles (Vehicle-to-Vehicle (V2V)), roadside infrastructure (Vehicle-to-Roadside (V2R) communication) such as traffic signal controllers, infrastructure (Vehicle-to-Infrastructure (V2I) communication), and the entire environment (Vehicle-to-Everything (V2X) communication) [10; 11]. RM strategies in mixed traffic flows disregard the classical RM approaches, as traffic lights become obsolete for signaling the entry of vehicles onto the mainline flow. Instead of the classical RM control, which uses a traffic light with real-time traffic data to determine the rate at which vehicles should enter the motorway, merging control approaches are proposed to resolve interactions and conflicts between vehicles at merge areas. Thus, we refer to RM for HDV traffic flows and merging control approaches for mixed traffic flows. Therefore, for mixed traffic flows, merging control optimization is developed instead of RM. This paper describes the VSL problem and the merging problem on urban motorways with a focus on mixed traffic flow scenarios. To the best of our knowledge, there is no comprehensive systematic review on applications of control strategies for VSL and merging approaches to mixed traffic flows. A brief overview of VSL and RM control strategies for HDV traffic flows is also mentioned to explain the current knowledge base for possible control strategies that can be applied to mixed traffic flows. A more comprehensive review of VSL strategies for HDV flows has been analyzed in [12; 13; 14; 15; 16], while in [17] the focus has been on Reinforcement Learning (RL) strategies for VSL control. RM strategies in HDV flows were analyzed in papers [18; 19; 20; 21; 22; 23; 24; 25; 26]. RL strategies for RM control were analyzed in [25].