Recently I wrote about an interesting project that was aiming to utilize big data to make the taxi network smarter in our cities.  A recent paper from the University of Arizona is along similar lines, albeit the target in this instance is the bus network.

The researchers took on the grueling task of improving public transport in the hustling Brazilian city of Fortaleza.  The team developed an online dashboard for city planners to access huge amounts of data about the bus network, such as where bottlenecks exist, in the hope that they will be better placed to correct them.

“This is something that makes an impact; we’re not just simulating data but working with a real-world problem and real datasets,” the researchers say.

Smarter cities

The researchers have digested around three years worth of bus data from across the city.  The network is hugely popular with citizens, who rack up around 30 million trips every month.

They were looking at not only the number of visits undertaken, but the actual journeys themselves, including the time as well as the location of them.  By combining this with GPS data collected from the 2,200 buses in action in the city and the 4,800 stops scattered around, they were capable of creating a detailed picture of how the bus system operated.

“We were able to write our own algorithms to derive exactly how much time it takes for a bus to move from one bus stop to the next, so we can see how fast it’s moving and where the delays occur and where there are no delays,” the authors say. “Really, this is about understanding human mobility patterns.”

Real world applications

A nice thing about the project is that it isn’t just an academic exercise.  The dashboard that the team developed is now being used by planners in Foraleza to help them more successfully manage the bus network, whether it’s increasing the volume, or trying to ease the flow of the network.

“They can pick a particular bus route, they can pick the direction, they can pick a particular date and a particular hour, and they can see how many people were on the bus and where there were delays,” the researchers say. “They can then take this information and decide whether it’s worth putting dedicated bus lanes on those segments of the road where there are most delays. And they’re able to justify and explain to their citizens why these decisions are important.”

This intelligence has already led to a number of decisions being made with the data, and the team are hopeful that the model could easily be deployed in other cities around the world.

“This is really about harnessing or leveraging data that is coming from the ‘internet of things,’ the GPS signals and the smart cards — these are all objects that generate signals, and they have a time stamp and a location and you want to be able to put all these points together and see patterns,” they conclude. “To build a smart city, you want to be able to get all that data together to understand people’s usage patterns, mobility patterns, and then design services that are actually going to help them.”