<p>The manuscript introduces a novel perspective and approach to tackling traffic control. Bypassing the need for computationally expensive constrained optimization of spatiotemporal cues describing the road traffic state, the system exploits the causal relation between traffic light green light timing and the flow of cars. This way the system can store traffic contexts as memories used to simply recall plausible green time timings matching the flow of cars. This behavior amounts to an autoassociative memory, efficiently implemented in spiking neural networks, that provides a good trade-off between execution time and accuracy. We believe the approach has very high potential in real-world deployments. Our initial results on four real datasets demonstrate the benefits that the approach has and, of course, the lightweight and efficient computation steps and learning paradigm. Our goal is to raise awareness in the traffic engineering community on how neural associative memories can be a suitable candidate for traffic control, a problem without straightforward solutions. The flexibility in representing traffic data through vectors, causal learning of memories, and fast recall provide outstanding benefits demonstrated through our experiments. We hope the work will contribute to both the neural network community, through a novel autoassociative memory system using high-dimensional vectors, and the traffic engineering community, through a novel solution to traffic control.</p>

<p>The manuscript introduces a novel perspective and approach to tackling traffic control. Bypassing the need for computationally expensive constrained optimization of spatiotemporal cues describing the road traffic state, the system exploits the causal relation between traffic light green light timing and the flow of cars. This way the system can store traffic contexts as memories used to simply recall plausible green time timings matching the flow of cars. This behavior amounts to an autoassociative memory, efficiently implemented in spiking neural networks, that provides a good trade-off between execution time and accuracy. We believe the approach has very high potential in real-world deployments. Our initial results on four real datasets demonstrate the benefits that the approach has and, of course, the lightweight and efficient computation steps and learning paradigm. Our goal is to raise awareness in the traffic engineering community on how neural associative memories can be a suitable candidate for traffic control, a problem without straightforward solutions. The flexibility in representing traffic data through vectors, causal learning of memories, and fast recall provide outstanding benefits demonstrated through our experiments. We hope the work will contribute to both the neural network community, through a novel autoassociative memory system using high-dimensional vectors, and the traffic engineering community, through a novel solution to traffic control.</p>