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MIT Researchers Use Movable Sensors to Measure Traffic, Weather, and Air Pollution

Imagine a scenario where Manhattan has just 10 taxis. If so, what part of the borough’s streets would they cover in a normal day?

Just 10 taxis typically cover one-third of Manhattan’s streets in a day. (Image credit: MIT)

Before answering that question, it would be better to explore why it is useful to be aware of this fact. There are plenty of things in cities that need measuring—for example, road quality, traffic patterns, weather, air pollution, and much more. Although instruments fixed to buildings can determine some of these things, researchers can also attach low-cost sensors to taxis and record measurements over a bigger part of a city. So, what is the number of taxis it would take to cover a specific amount of ground?

To find this out, a team of researchers from MIT examined traffic data of nine major cities from three continents, and eventually came up with a number of latest findings. Surprisingly, it was found that while a few numbers of taxis can cover a considerable amount of ground, it takes many more taxis to cover a city more expansively than that. Interestingly, this pattern appears to simulate itself in metro areas worldwide.

More particularly, only 10 taxis usually cover one-third of the streets in Manhattan in a day. Additionally, it takes approximately 30 taxis to cover half of Manhattan in a single day. However, since taxis are likely to have convergent routes, more than 1,000 taxis are needed to cover 85% of Manhattan in a single day.

The sensing power of taxis is unexpectedly large.

Kevin O’Keeffe, Study Co-Author and Postdoctoral Fellow, Senseable City Lab, MIT

The recently published paper describes the results of the study.

Conversely, O’Keeffe observed that “there is a law of diminishing returns” at play as well. “You get the first one-third of streets almost free, with 10 random taxis. But … then it gets progressively harder.”

An analogous numerical association occurs in San Francisco, Chicago, San Francisco, Singapore, Shanghai, Beijing, Vienna, and some other major cities across the world.

Our results were showing that the sensing power of taxis in each city was very similar. We repeated the analysis, and lo, and behold, all the curves [plotting taxi coverage] were the same shape.

Kevin O’Keeffe, Study Co-Author and Postdoctoral Fellow, Senseable City Lab, MIT

The paper titled, “Quantifying the sensing power of vehicle fleets,” will soon be published in Proceedings of the National Academy of Sciences.

Apart from O’Keeffe, who is the corresponding author, the study co-authors are Amin Anjomshoaa, a researcher at the Senseable City Lab; Paolo Santi, a research scientist at the Senseable City Lab and the Institute of Informatics and Telematics of CNR in Pisa, Italy; Steven Strogatz, a professor of mathematics at Cornell University; and Carlo Ratti, director of the Senseable City Lab and professor of the practice in MIT’s Department of Urban Studies and Planning (DUSP).

For years, the Senseable City Lab members have been analyzing cities based on the information obtained from sensors. While doing so, the team noted that some standard deployments of sensors come with certain tradeoffs. For instance, sensors on buildings can deliver consistent data on a daily basis, but their reach is rather restricted.

They’re good in time, but not space,” stated O’Keeffe of fixed-location sensors. “Airborne sensors have inverse properties. They’re good in space but not time. A satellite can take a photo of an entire city—but only when it is passing over the city, which is a relatively short time interval. We asked the question, ‘Is there something that combines the strengths of the two approaches, that explores this city well in both space and time?

Integrating the vehicles with sensors is one option, but which types of vehicles will be suitable? Buses have only fixed routes and they can cover only a limited amount of ground.

The Senseable City Lab members have also attached sensors to garbage trucks in Cambridge, Massachusetts, among other things, but in spite of this, they were not able to gather as much information as taxis might.

That analysis helped in leading to the present study, which employs data from a wide range of municipalities as well as private-sector research efforts to figure out taxi-coverage patterns in a much better way.

Manhattan was the first place to be examined by the researchers, who split the city into approximately 8,000 street segments, and acquired their initial results.

Nevertheless, Manhattan has some evident features— for instance, a typically regular street grid—and there was no way to make sure that the metrics it generated would be comparable in other places. However, in city after city, the researchers observed that the same kind of phenomenon emerged—a few taxis can cover more than one-third of a city in a single day, and a somewhat larger number can reach half of the city, but after that, a relatively bigger fleet will be required.

It’s a very strong result and I’m surprised to see it, both from a practical point of view and a theoretical point of view,” stated O’Keeffe.

The study's practical side is that policymakers and city planners, among others, now possibly have a more tangible idea about the investment required for specific levels of mobile sensing, and also the extent of the results they would probably achieve. For example, an air pollution study could be drawn up keeping this type of data in mind.

Urban environmental sensing is crucial for human health. Until today, sensing has been performed primarily with a small number of fixed and expensive monitoring stations. … However, a comprehensive framework to understand the power of mobile sensing is still missing and is the motivation for our research. Results have been incredibly surprising, in terms of how well we can cover a large city with just a few moving probes.

Carlo Ratti, Director, Senseable City Lab

As O’Keeffe readily admitted, one sensible method to build a mobile-sensing project would be to place sensors on taxis, and subsequently deploy a comparatively small number of vehicles (similar to how Google does for mapping projects) to reach streets that are almost never ventured by taxis.

You bias, almost by definition, popular areas. And you’re potentially underserving deprived areas. The way to get around that is with a hybrid approach. [If] you put sensors on taxis, then you augment it with a few dedicated vehicles.

Kevin O’Keeffe, Study Co-Author and Postdoctoral Fellow, Senseable City Lab, MIT

According to O’Keeffe, a physicist by training, the result bodes well for the sustained use of mobile sensors in major urban studies worldwide.

There is a science to how cities work, and we can use it to make things better,” concluded O’Keeffe.

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