Boston traffic (Heidi Besen/

Boston takes on traffic by building better data, not more roads

Cities prone to traffic congestion are scrambling for technologies that will help them get their residents to work on time and boost their local economies. But the best performers will likely be those places with the best data, not the best roads.

That’s the situation some cities find themselves in as they look for ways to generate and acquire transportation data from  public transit sources, commercial sellers and ride-sharing firms like Uber and Lyft.

“If you’re doing transportation planning today, you don’t have great data about where people are going and when they’re going and why they’re going there,” said Boston CIO Jascha Franklin-Hodge. “So what we’re trying to do is figure out how to wrap our arms around as many of these datasets as we can and start to do useful analysis that helps us make decisions.”

Those decisions might range from where the city wants to re-time traffic signals, whether to write different curb-space rules to make pick-ups and drop-offs easier or how to get more people moved faster by creating priorities for buses on a given roadway.

The city is also testing approaches to leveraging data it already owns as well as data being used in other segments of the transportation industry. “We’re making educated guesses by trying to analyze a broad variety of datasets, probably none of which will tell us exactly what we need to know,” Franklin-Hodge said.

Some data is close at hand, including data from train schedules and arrival times. Other public data is available from low-temperature light detectors, devices that provide the city “some sense of vehicle volume through intersections.”

Boston is also accessing traffic congestion data -- some available for free on a limited basis from various partnerships and some purchased outright from companies like INRIX, a software-as-a-service company that sells real-time traffic information and statewide traffic analytics.

In that case, data is abstracted, making it only occasionally useful, Franklin-Hodge said. “It doesn’t tell us who’s going where -- it just shows us a point in time on a specific location, what the conditions are -- but it is potentially valuable in planning.”

In addition to these datasets, information is now also being offered by ride-sharing giants Uber and Lyft and in some cases by shuttle bus fleets. But while the city is starting to develop a data sharing partnership with Uber, “we don’t have great visibility into where those vehicles are,” Franklin-Hodge said.

“Our data-sharing partnership with Uber does provide us some of that, but not at a high degree of geographic precision,” he added. “It lets us more than anything understand the overall volume of these vehicles and the trips that they generate, but not precisely where they are or how we might plan around them at a road level.”

The next step in improving data sharing between the city and Uber could be the arrival of “Movement,” a so-far free offering from the ride-sharing firm that provides travel time information between origin and destination pairs in different parts of the city.

The tool lets planners look at any given moment -- in historical, not real time -- and determine on a particular day how long it typically takes to go from one zone to another in the city.  “It’s a brand new tool designed to give transportation planners a broad view into the impact of congestion and shifts in the roadway system,” Franklin-Hodge said.

While a potentially useful tool to gather insights about travel times, it is very specific to that application, he added. “What it doesn’t tell us about is demand or the transportation mode choices people are making -- so it’s a useful but limited dataset.”

“It’s also brand new, and so I don't think we or any other city have quite figured out how to integrate it into our planning efforts.”

An early idea for Movement was to use it to measure the impact of changes the city might want to make in its transportation system.

“Let’s say we decided to create a bus rapid-transit lane,” Franklin-Hodge said. “It won’t tell us how much that improves the bus travel times, [but] it could tell us if it’s making things worse for other vehicles.”

The tool also gives the city insights into trends over time as parts of the city develop. “We, like many cities, are in the middle of a development boom,” he said. “As housing and new businesses go in, we can see how that’s affecting congestion [and] think about a different approach to our transportation planning for this part of the city.”

Looking ahead, transportation planners are developing digital adaptive systems capable of testing the impact of signal timing on traffic at different times of the day, said Franklin-Hodge, who cited the city of Los Angeles as a forerunner in using the technology.

“They’ll tell you that they’ve been able to reduce congestion by 15 percent by having a system that essentially learns how to adjust signal timing in real time to sense pockets of congestion and … dynamically make changes from one light cycle to the next that help minimize imbalanced traffic across the road network.”

Managing complexity is one of the greatest challenges of planning a transportation system, he added. “It requires a ton of technology, sophisticated analytics and real-time control systems. But layer onto that a public transit system, then add on to that people who make decisions based on increasingly real-time information sources -- and that’s really hard.”

Ultimately the success of the projects will depend on improving teamwork between the city and the ride-sharing industry, Franklin-Hodge said.

“We try to approach this in the spirit of cooperation, to say, ‘Look, you’re part of the system,’” he said. “They’re not independent of the curb space and traffic signals and road space that is paid for by the public and is intended to support mobility for all of the public.

“So I think we’re working toward a future of cooperation, but it’s going to require a continued  push from government and a collaborative mindset from companies that have historically not always been the best collaborators.”

About the Author

Paul McCloskey is senior editor of GCN. A former editor-in-chief of both GCN and FCW, McCloskey was part of Federal Computer Week's founding editorial staff.


  • senior center (vuqarali/

    Bmore Responsive: Home-grown emergency response coordination 

    Working with the local Code for America brigade, Baltimore’s Health Department built a new contact management system that saves hundreds of hours when checking in on senior care centers during emergencies.

  • man checking phone in the dark (Maridav/

    AI-based ‘listening’ helps VA monitor vets’ mental health

    To better monitor veterans’ mental health, especially during the pandemic, the Department of Veterans Affairs is relying on data and artificial intelligence-based analytics.

Stay Connected