For a number of years TfL have been providing open access to feeds from over 170 traffic cameras or ‘JamCams’ distributed at key locations across London’s road network. In addition to static images each camera also provides a five second video which is updated every 5 minutes. The feeds of these videos have now been incorporated into CASA’s ViLo platform.
I’d been fascinated by the videos for some time. Every morning when I arrive at CASA I check out CASA’s London CityDashboard which we have on in our reception area. The dashboard includes two static images from the cameras chose at random along with a looped video feed from another in the top right.
I was always struck by the sense of ground truth the cameras seemed to offer for a particular place. At the same time I was frustrated by the fact that I couldn’t get a sense of the wider context: What’s just out of shot? What’s the wider context in which each camera is situated? What’s going on over at the next nearest camera and the rest in the surrounding area Incorporating the feeds from those cameras into ViLo provides a spatialised sense of context in a way that the dashboard can’t. The 3D models also help users understand the orientation of each camera in a way that a map might not. Finally their incorporation in ViLo also facilitates comparison with other spatialised streams of data data.
By comparison with other real-time feeds like TfL’s real-time bus information the traffic cameras provide a much richer sense of what is happening in an area, at least within the five minute time scale provided by the video updates. Not only do we get a sense of the flow of traffic and any blockages, the information provided by the cameras also provides a wider situational awareness of factors like local weather conditions and pedestrian footfall. In this way the information they provide offers a degree of validation to other data sets that can be particularly useful when additional context is required for decision making by city officials and members of the public alike.