Repurposing 1980s traffic systems for artificial intelligence

Video monitoring systems for traffic have been in use since the mid- 1960s, initially pioneered by Israel and the Netherlands in order to capture motorists violating traffic light regulations. But they would have been very differently-designed if computer evaluation, rather than human monitoring, had been anticipated – a problem which researchers from Carnegie Mellon are currently tackling. The sheer scale and maturity of urban traffic-monitoring systems, many of which hail back to the 1980s, are a tantalising prospect for AI in terms of generating usable data about traffic flow, but are predicated on'common sense' – currently an elusive goal for machine learning, even in terms of widely-agreed definition. In the paper Understanding Traffic Density from Large-Scale Web Camera Data four researchers consider what Fully Convolutional Networks (FCNs) might be able to achieve in terms of leveraging all this extant, live data without the need to invest in expensive and experimental new monitoring systems. City-wide traffic camera networks were originally developed as tools for municipal traffic authorities to make rough estimations of congestion, and to be apprised of serious blockages such as road accidents; any traffic flow data emerging from their use would be purely anecdotal – impossibly expensive or complicated to rationalise and analyse scientifically.