The trend of increasing traffic demand is causing congestion on existing urban roads, including urban motorways, resulting in a decrease in Level of Service (LoS) and safety, and an increase in fuel consumption. Lack of space and non-compliance with cities’ sustainable urban plans prevent the expansion of new transport infrastructure in some urban areas. To alleviate the aforementioned problems, appropriate solutions come from the domain of Intelligent Transportation Systems by implementing traffic control services. Those services include Variable Speed Limit (VSL) and Ramp Metering (RM) for urban motorways. VSL reduces the speed of incoming vehicles to a bottleneck area, and RM limits the inflow through on-ramps. In addition, with the increasing development of Autonomous Vehicles (AVs) and Connected AVs (CAVs), new opportunities for traffic control are emerging. VSL and RM can reduce traffic congestion on urban motorways, especially so in the case of mixed traffic flows where AVs and CAVs can fully comply with the control system output. Currently, there is no existing overview of control algorithms and applications for VSL and RM in mixed traffic flows. Therefore, we present a comprehensive survey of VSL and RM control algorithms including the most recent reinforcement learning-based approaches. Best practices for mixed traffic flow control are summarized and new viewpoints and future research directions are presented, including an overview of the currently open research questions.

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Variable Speed Limit (VSL) control systems are widely studied as solutions for improving safety and throughput on urban motorways. Machine learning techniques, specifically Reinforcement Learning (RL) methods, are a promising alternative for setting up VSL since they can learn and react to different traffic situations without knowing the explicit model of the motorway dynamics. However, the efficiency of combined RL-VSL is highly related to the class of the used RL algorithm, and description of the managed motorway section in which the RL-VSL agent sets the appropriate speed limits. Currently, there is no existing overview of RL algorithm applications in the domain of VSL. Therefore, a comprehensive survey on the state of the art of RL-VSL is presented. Best practices are summarized, and new viewpoints and future research directions, including an overview of current open research questions are presented.

Variable speed limit control (VSLC) is one of the services from the domain of intelligent transport systems applied to alleviate or prevent congestion on urban motorways. The main idea of VSLC is to change the speed limit according to current traffic or weather in order to improve the traffic situation. Improvement of the traffic situation on urban motorways depends on the applied controller and its parameters. For this reason, the controllers chosen for implementation have to be tested in simulations using a realistic urban motorway segment, placement of sensors and traffic scenario, and a relevant set of control parameters. An analysis of two VSLC controllers, including the influence of their parameters on controller performance with respect to traffic and environmental aspects, is presented in this paper. To this end, an appropriate microscopic simulation framework was developed, the fundamental diagram of a simulated synthetic urban motorway segment was measured and traffic parameters, with vehicle emissions values, were obtained. The effectiveness of the two analysed controllers was compared based on the obtained simulation results. The results revealed that appropriate settings of the controller are crucial to ensure optimal use of urban motorways with minimum travel times and vehicle emissions.