But what does being ‘smart’ mean and what are its benefits? At Maynard we believe it’s about creating cities which can adapt and respond intelligently to the ever-changing needs of its citizens. It’s about improving the quality of life, experience and wellbeing of its inhabitants, and at the same time maximising the efficiency of the city’s governance.

Being smart isn’t necessarily about what new technology you have developed or implemented; it’s about appreciating the data that is being collected, and understanding how this new intelligence can be used to realise positive benefits for society.

Personalised website adverts are all too frequent examples of how the advertising industry has applied this concept for financial gain. By collecting data from our everyday actions and anticipating our next steps, they are able to offer us tailored products before we even realise we need them. But how does this translate to our urban realm and transport infrastructure?

Automatic traffic counters have long since provided robust evidence bases from which transport planners can make informed decisions about the design of our road networks. The emergence of real-time intelligent sensors has taken this further. Pedestrians, cyclists, rail passengers and all other mode users can now be monitored anonymously and accurately using Bluetooth, WiFi and mobile phone network data, for example. This data can be used by designers and service operators to identify what people are currently doing, why and how frequent are they doing it, and ultimately predict their future behaviours. Their needs can now be confidently planned for, whether they be long term, like the upgrading of a metro line, introducing new cycle lanes, or in the more immediate, reactive sense, for example, prioritising traffic signals, opening or closing of station gatelines.

A key challenge we need to overcome is how we manage and share this data. Most Londoners will be unaware that each time they step onto a tube carriage, in-built weighing equipment or infrared sensors can automatically detect the number of passengers on board. This data is potentially gold dust. In theory this could alert passengers at the next station where exactly along the platform they should stand depending on how full each carriage is. Not only would this improve passengers’ comfort and experience, it could lead to operational efficiencies through decreased boarding times, higher capacities and improved passenger safety. A win-win for all. The difficulty lies in how this mass of fluctuating data can be analysed, disseminated and communicated clearly to its intended audience, often within a few seconds, so that it’s available exactly when and where they need it. How this data is organised, shared and visually presented is vital to its effectiveness.

Our belief is that the act of data collection shouldn’t always be discrete and one-way. Part of living in the digital age is our familiarity with instantaneous information and services. We are arguably more demanding; as a customer we expect suppliers and operators to know what we want, and we assume that services can be tailored to our specific needs, or at the very least be offered a range of options to suit our preferences. If you’re booking a train ticket to the airport, you may want to know that sufficient luggage space is allocated for you on board. Or imagine you have a 5 minute walk to a train station, but your next train is 20 minutes away. You may want your trusty wayfinding app to navigate you via a more pleasant route through the park, or past a local coffee shop, even though it may take longer. This may sound trivial but that small difference could have knock-on benefits such as to health and wellbeing (increased walking along a less polluted route), to the local economy (increased spending at local businesses), and even comfort and safety (decreased congestion along the station platform itself).

Operators should continue to foster increased two-way communication with their customers to not only offer personalised services when they’re requested, but also to learn to pre-empt needs and recommend the appropriate information before it’s asked. If we want to continue collecting data ethically from the public, some of which could be deemed highly sensitive (facial recognition data being a good example), then society needs to clearly see the resulting benefits. An improved customer experience at a personal level is a very tangible way of achieving this, and one which all of us at Maynard puts at the top of our agenda.