New York City Department of Transportation:

The New York City Department of Transportation (NYC DOT) manages one of the world’s most complex urban transportation networks, overseeing 6,300 miles of streets and highways with the dual goals of safety and efficiency for its population of 8.8 million people.

Vision Zero, the citywide initiative aimed at reducing and ultimately eliminating all traffic-related deaths and injuries, is integrated into all aspects of NYC DOT operations. Much of the implementation for Vision Zero in the department rests with the Office of Research, Implementation & Safety (RIS). Based on analyses of injury, speed and other data, this office leads street redesign projects, installs traffic-calming interventions, conducts safety research and makes safety policy recommendations.

NYC DOT and Geotab have a longstanding partnership, with the Geotab telematics solution installed across its fleet of over 11,000 vehicles. Telematics is one of the leading data capture and analytics platforms empowering the City to make informed decisions on road safety.

Recently, when RIS was seeking a more data-driven process for applying and analyzing traffic-calming strategies, NYC DOT took its partnership with Geotab to the next level by adopting the new Geotab Intelligent Transportation Systems (ITS) Altitude platform. Altitude is an easy-to-use, easy-to-integrate transportation analytics platform powered by deep and scalable insights for more informed decision-making. In the first stage of implementation, RIS adopted Roads, one of three products within Altitude (alongside Origin & Destination and Stop Analytics). Roads lets RIS tap into speed and travel-time metrics across all road segments in the region, enabling the office to better identify problems and plan and monitor improvements.

RIS devotes considerable time and resources to finding out which areas of the City have the worst speeding problems. The office has a minimum threshold for a street to become a candidate for traffic-calming treatments: when 15% of vehicles are traveling at 5 or more mph above the speed limit. It sounds simple, but gathering that data and ensuring it’s reliable is not.

“We needed a more comprehensive dataset to better determine where we should direct our resources,” says Rob Viola, Director of Safety Policy & Research at RIS. “We get thousands of comments and complaints about road safety every year from community members. Faced with limited budgets, we were looking for a quick and accurate way to assess the validity of these claims and make evidence-based investments.”

Establishing which streets should receive traffic-calming treatments – ranging from lowered speed limits to speed humps and speed cushions (humps modified to accommodate trucks and buses) – is a complex calculation involving multiple factors. There were several key areas where RIS needed more consistent, high-quality data:

• Verifying whether roads meet the qualifying criteria
  for traffic-calming interventions
• Before/after evaluations of the effectiveness
  of traffic-calming interventions
• Vetting of community requests and complaints

The “Priority Geographies” identified by Vision Zero – those with a high incidence of pedestrian fatalities and injuries – go to the top of the list, followed by school zones. “There are thousands of schools in the City, so we’re always trying to find new and better ways to allocate our resources,” says Viola.

Beyond these top two areas, there are still many spots in the City that require the office’s attention. While roads must meet detailed criteria (traffic volume, types of vehicles, road geometry and more) to qualify for traffic-calming interventions, the primary determinants are speeding history and crash history.

“Our office receives a live feed of all the City’s injury crashes, which goes into a database,” says Viola. “For historical speeds, though, we were using the time-consuming, manual approach of sending people out with radar guns to gather information.” Other methods such as pulling speed data from mobile devices can be problematic due to difficulties distinguishing between slow moving vehicles, bus riders and bicyclists. “The bottom line is we needed easier access to better data.”