An open-source SUMO framework for adaptive traffic signal control is introduced, and experiments on a two-intersection network show Max-pressure outperforms deep reinforcement learning controllers.
Self-Organization in Traffic Lights: Evolution of Signal Control with Advances in Sensors and Communications
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abstract
Traffic signals are ubiquitous devices that first appeared in 1868. Recent advances in information and communications technology (ICT) have led to unprecedented improvements in such areas as mobile handheld devices (i.e., smartphones), the electric power industry (i.e., smart grids), transportation infrastructure, and vehicle area networks. Given the trend towards interconnectivity, it is only a matter of time before vehicles communicate with one another and with infrastructure. In fact, several pilots of such vehicle-to-vehicle and vehicle-to-infrastructure (e.g. traffic lights and parking spaces) communication systems are already operational. This survey of autonomous and self-organized traffic signaling control has been undertaken with these potential developments in mind. Our research results indicate that, while many sophisticated techniques have attempted to improve the scheduling of traffic signal control, either real-time sensing of traffic patterns or a priori knowledge of traffic flow is required to optimize traffic. Once this is achieved, communication between traffic signals will serve to vastly improve overall traffic efficiency.
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An Open-Source Framework for Adaptive Traffic Signal Control
An open-source SUMO framework for adaptive traffic signal control is introduced, and experiments on a two-intersection network show Max-pressure outperforms deep reinforcement learning controllers.