Making mobility analytics work for smart cities
As telecom operators look for additional ways to unlock new revenues, smart cities are currently being actively promoted, and monetization of data is emerging as a prominent—if potentially controversial—revenue source.
November 26, 2018
As telecom operators look for additional ways to unlock new revenues, smart cities are currently being actively promoted, and monetization of data is emerging as a prominent—if potentially controversial—revenue source. A stream of valuable data is generated continuously when mobile phones are connected and used on the network—geolocation data, direction of travel, timing and length of journeys, etc.—and this could all be extremely useful for the planning of both public and commercial services within the smart city. Of course, the data needs to be both anonymized and aggregated in order to ensure personal privacy and to comply with data protection legislation. But it’s the behavior of the crowd, not that of the individual, that is most informative, and there is still real value in this aggregated, anonymized data. Companies and organizations such as national and local government, emergency services, highways authorities, civil engineering contractors, transport operators and tourism agencies are already exploring the possibilities of using this data for future planning.
An obvious example is the planning of new or upgraded roads, routes for public transport, or cycle routes. And when there’s a major road closure in a city for planned maintenance, the highways authorities would be able to make informed decisions about where to re-route traffic, and could also feed back messages to the mobile subscribers informing them of the status and the best alternative routes to take.
In the case of tourist spots like museums and other points of interest, data such as the demographics of visitors—even socio-economic status—and which countries they have traveled from, can be determined from this data. All of this can be used to help understand tourist behavior patterns and preferences, and to formulate personalized products or services to improve visitor experience.
Or consider the example a large gathering in a city, like a sporting event or a music festival. The local police could use this real-time data to plan where their officers should most effectively be placed, and whether it would be useful to direct people down a certain route to avoid congestion. For recurring events, it could also help with planning the location of street food kiosks and coordination of transportation, parking and road closure requirements. An example where data like this has been analyzed recently was the Pride Parade in Tel Aviv. The aggregated movement patterns of the participants as the event progressed could be clearly seen on a virtual map: gathering together in the city center near hotels at breakfast time, progressing towards the sea front to join the march, and then which directions the crowd dispersed in once it was finished.
How do we achieve this insight? This kind of groundbreaking data analytics requires a sophisticated AI platform as well as a reliable source of data. Isolated events such as making a call, writing a text message, or streaming video mean nothing in isolation, but when a massive number are aggregated it is possible to build an informative picture of where people live, their daily journeys to work, how far they travel and at what times, and the journeys they make in leisure time. This is the same data that we are already aggregating for our mobility analytics, to ensure superior mobile experience for Connected Cars and subscribers on the move. Unlike the connected journey experience however, what is important in this case is what is called the “Origin-Destination Matrix”. But the fact that this can be generated from the same anonymized data set, and on the same platform, means that it can be offered as an additional benefit to mobile network operators, a very valuable feature that will enable them to monetize their data.