What could a mobile operator do better if they knew what their customers’ habits would be during a typical (or even a less typical) day? What their ‘mobility patterns’ and data demands would be?
This is not ‘Big Brother’ stuff, but simply knowing—for example — that a subscriber regularly makes video conference calls while traveling daily to work in a car pool along a certain highway. Analyzing this type of aggregated big data helps operators to predict where, and when, high-speed, high-capacity mobile access will be needed. Also — especially now that electronic beam-steering is being introduced with 5G—it enables them to shape and direct their mobile coverage to be responsive in meeting that demand. In military terminology, this is called ‘Situational Awareness’ — knowing where people are and what they are doing, so that you can respond in an intelligent and timely manner.
Why is this becoming so important now? The way that we travel around our cities is undergoing significant changes, and at the same time our connectivity needs are evolving. In combination these factors make it difficult for the operator to know how to adapt the network to our needs. It’s no exaggeration to say that Connected Mobility has become an everyday necessity, and the massive network consumption that is fueled by our addiction to data-hungry apps will only increase as 5G is rolled out. Shared mobility—either in the form of shared vehicles or as ride-sharing/car-pooling in personal vehicles—is becoming more popular, and ride-hailing services like Uber are also changing the way we plan and execute our journeys. Multiple travelers in the same car will have more free time to use a data connection for either work or enjoyment, while the evolution towards autonomous cars will eventually free up the ‘driver’ to do the same. An estimated 72.5 million connected cars per annum will be sold globally by 2013, and car manufacturers will be keen to prevent a bad connected experience for their customers that, rightly or wrongly, might damage their reputation.
Using Artificial Intelligence (AI) helps mobile operators to keep the network responsive to these needs. Providing the necessary situational awareness, it can learn and determine where and when areas of traffic congestion might build up, and can give rich insights into the communications habits and travel patterns of users on the move. This analysis enables predictive actions to improve the connected journey experience. It continuously builds and benchmarks experience profiles for connected vehicles and their occupants then maps every road and highway segment for both service demands and the actual data connectivity experience.
This means that mobile subscribers on the move can get a much better service, while drivers can make better-informed decisions on travel routes. In the near future, new navigation options will be able to direct drivers, or self-driving cars, automatically onto routes that match their communication needs. Infotainment systems and innovative OEM service platforms can also buffer data, or alert drivers and passengers ahead of predicted service degradation. It is even possible to activate transparent switching between mobile operators along the route according to quality and predicted experience. The connected journey experience can actually be tailored to meet consumer needs and expectations. Keeping the subscriber’s connection uninterrupted at all times can make all the difference when asking if they’d recommend their mobile service provider as the best one.