This is the in-depth version of my column posted in Dezeen this week, around the impact of predictive analytics on cities. This version particularly uses public transport services and new transport startups as the pivot for its arguments, as transport (or transit, or mobility) is a fundamental aspect of city services currently being transformed, disrupted and contested through such dynamics. The arguments get usefully tangible when we're looking at Uber, Lyft, Bridj alongside MTA, Transport for London and MBTA. This also features a bit of a Q&A with Bridj CEO Matt George—I'll post more from that separately shortly. I now realise that, completely unintentionally, this is a follow-on to a piece on 'transport informatics' I posted around six years ago.
1. Clockwork City
For the last 150 years or so, we’ve run our cities like clockwork.
I don’t mean that as a compliment, a suggestion of flawless efficiency. Just that we’ve designed, planned and run our cities based on regulated industrial rhythms, bound to pre-digital engineering and organisations, and we still do.
We expect a rush hour at the beginning and end of work-week days, and planners intensify mass transit at these times along major arteries, usually into the middle of cities via a form of ‘hub-and-spoke’ model. Citizens must move towards the nearest nodes in that network—the bus stop, the metro station—rather than their actual origin or destination, and these must necessarily be organised along averages of demand.
These patterns are in-part derived from mass industrialisation, and its physical impacts, and the 20th century urban planner’s instinct to separate functions like retail, offices, housing and industry into different zones of the city.
These days, however, not only are we now trying to create ‘mixed-use’ urban environments, dissolving zones left, right and centre, but many of our patterns of working are fragmenting—whether that’s through zero hours contracts or the burgeoning freelance sector—as are many other patterns of living, generally.
But those clockwork patterns run deep. Few western cities look like a Lowry painting anymore. The factories have gone, the workers have gone, the tramlines that delivered them have often gone too. Yet traffic still tends to runs along those now-buried lines, even though the route’s raison d’être has long since departed.
Our bureaucracies are also based on processes that would not be all that unfamiliar to the Dickensian clerk hunched over reams of paper in stygian gloom, shuffling applications, plans, appeals, and accounts back and forth. Those processes have sometimes been digitised, yet the Estonian president Toomas Hendrik Ilves has suggested that the shift from digital to paper is not just a shift of format, for the same processes; he says means completely ”redesigning government and how you interact with people.” This probably applies to to municipal governance more than national or federal, given their remit in actually delivering services, yet few, if any, cities have redesigned accordingly.
City policies and services are still derived from planning around averages and rounding down, informed by a muddy stew of samples, snapshots, censuses and ideologies, and delivered en masse, one size near-enough fits all, and then spotily measured or policed, to see what actually happened. Sometimes.
There are two ways this may about to change, as cities begin to address Ilves's challenge—by enabling services on-demand, and by using data to predict the need for services in a particular place at a particular time, with great precision (allegedly.)