New York has started using sensors that track movements of up to nine modes of movement at various intersections.
Most of us have at one time or another driven over one of those black tubes pulled across the road. These pneumatic tubes count vehicles and are often used to gather data on traffic flows, but they aren’t very accurate, and they don’t distinguish various kinds of vehicles, let alone pedestrians or cyclists. And yet, as the article points out, “understanding how people use streets is crucial information for transportation planners to recommend adaptive changes”. In other words, if a city planner is going to recommend a wider bike lane here, an extra turn lane there, or a barrier between this or that mode of transport, they need data to make the recommendation.
Viva, a London-based start-up is working with New York transportation commissioner Ydanis Rodriguez in a pilot of their tech for measuring up to nine modes of movement, all with the aim of keeping people safe and improving the quality of data that city planners can use. There is a dozen of these cameras, which are installed on street-lights, installed in New York, but many more in London.
The cameras pickup on movement, translating it into trajectory paths and transportation modes. The nine modes are the following: “pedestrian, bike, e-scooter, motorcycle, car, van, light truck, semi-truck, and bus. The data gathered, which is being made both open-source and anonymized, shows flows relative to time of day as well as the distinct modes spatially relative to one another. The data is fine grained enough to pick up on so called ‘desire lines’, which capture unpredictable movements of, say pedestrians, around something blocking their way, or cyclists around a parked vehicle. The hope and expectation is that this data will show where touch points, or near touch points, are between the various modes, and therefore help pre-empt accidents.
As Stuart Weitzman from the University of Pennsylvania points out this pilot is an attempt at “closing the gap between what we, as individuals, use to plan our safe routs and how the city is doing its planning”, which represents an attempt at being proactive rather than reactive. This is undoubtedly a good thing as prevention is often much less costly and more effective than waiting for tragedy at a particular crossing before installing a barrier or expanding a bike-lane.
Gaining higher-resolution pictures that improve our understanding of our streets, how they are used and where pinch points might be, is excellent. It also appears to be a growing market with actors such as Viscando and Univrses also active in this area. This competition should lead to more reliable and more detailed data sets should help reduce the waste of building infrastructure in the wrong spot and depending on accidents to tell us where to build.
However, there is a note of caution, which is also mentioned in the article. There is a distance between gathering data and turning that into built infrastructure. That distance is only covered by political will and finances. There have already been 11 cyclist deaths in New York this year, and this has happened while the city drags its proverbial feet to implement standards already in place. Better data from actors like Viva might suggest better placement for new infrastructure and how it can be built to improve safety or efficiency. But no matter how good the suggestions are they remain just that until someone acts. Good suggestions are impotent without the political will and resources to bring them about.
Written by Joshua Bronson,
RISE Mobility & Systems