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StreetLight Data can now help cities measure bicycle and foot traffic

One of the big by-products of the digital revolution has been data — large banks of information garnered from billions of connected devices and software. Making sense of all this data is a challenge, but it’s one that San Francisco-based StreetLight Data has been setting out to tackle by drawing meaningful insights from trillions of location-based data points collected from smartphone apps.

Founded in 2012, StreetLight Data works with an aggregator called Cuebiq, which offers app makers an SDK that enables them to request anonymized access to their users’ location data. On an individual user basis, this doesn’t reveal that much, but tracking the location of people through weather apps, dating apps, and around 300 other apps on 70 million devices in North America can serve up valuable insights into how people move around.

StreetLight Data applies its algorithms to this data to figure out things like where people travel to and from, at what times and on which days. This can be useful for city authorities, engineers, and urban planners when working on new infrastructure projects. The data can also reveal how new modes of transport are impacting the way people travel — for example, has a new train line helped cut down traffic jams in a particular area?

So far, StreetLight Data has focused largely on tracking vehicular traffic — cars, buses, motorcycles, and so on. Moving forward, the company will be expanding the scope of its flagship StreetLight Insight product to cover pedestrians and bicycles, too.

The platform has, of course, already been tracking pedestrians and bicycles, but it hasn’t been able to provide breakdowns for these modes of transport specifically. StreetLight Data said it has developed a new proprietary machine learning toolset that can differentiate bikes and foot traffic from motorized vehicles.

This makes it possible to dig deeper into how people traverse cities — if its data shows that more than three-quarters of bike traffic is heading downtown, this could be used to justify more cycle lanes, for example.

Above: StreetLight Data

So anyone who subscribes to StreetLight Data can now log into their digital dashboard and view exact times, dates, and trip types (car, bike, foot). From this, they can see things like average travel times and distances per mode of transport during rush hour in a specific street, neighborhood, or any other pre-defined “zone.”

Above: StreetLight Data: Insights

Smart cities

Cities are increasingly turning to tech-infused mobility platforms for access to their data. Uber, for example, has partnered with cities to help planners manage urban growth, and last year it joined SharedStreets, a data-sharing project from the National Association of City Transportation Officials (NACTO).

Elsewhere, Palo Alto-based startup Waycare is collecting historical and real-time data from multiple sources, including connected car platforms, telematics services, road cameras, construction projects, fleet management platforms, weather services, and public transit to help cities build a more complete picture of their roads.

However, being able to drill down into a specific mode of transport — be it cars, bikes, or pedestrians — arms cities with even more detailed information.

“My hope is that by being able to measure bikes and pedestrians, we can manage toward a future where these modes of travel are more prevalent,” said StreetLight Data CEO Laura Schewel.

StreetLight Data recently raised $10 million in funding, taking its total amount raised to around $30 million, with a promise to accelerate its expansion and “invest in new product innovations.” Today marks the first step in this expansion.

“StreetLight Data has long led the industry with in-depth analytics that describe the mobility patterns of vehicle traffic,” Schewel added. “It is critical to develop a granular analysis of bike and pedestrian traffic to better see a complete picture of today’s complex mobility landscape. Our industry has been in the dark for years on bike and pedestrian movement because of the cost and difficulty of data collection. Now we can shed light on these mobility trends, giving planners the ability to make smart decisions about policy and infrastructure investment.”

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