Tired of packed trains? Here’s how smart cities can make your commute less painful

It might be hard to believe – when you’re waiting for a bus that finally arrives so full, you’d have been better off walking – that we are any closer to the smart cities that have been pitched to us for years.

The daily commutes in the world’s largest cities easily run into millions, and managing those travel flows remains a major task for the authorities as they work towards smarter networks. 

Transport for Greater Manchester (TfGM), for example, recently announced that it is working with data-warehouse specialist Snowflake to try and coordinate public transportation in the city to meet the needs and schedules of travellers.

Snowflake lets TfGM use a cloud-built data platform that aggregates information about the 5.6 million journeys made across the region’s transport network every day. The data warehouse service also manages the data so that city planners can access and interpret it more easily.

It may sound like a simple idea, but it is a big step, especially given how TfGM used to process data before it adopted the platform. 

Malcolm Lowe, head of IT at TfGM, said the authority always had lots of data but accessed it using techniques that ranged from spreadsheets to modelling tools. 

“It was all very siloed and it took a lot of manual processing, and a lot of time, to get any value from it,” he told ZDNet.

“With Snowflake’s platform, we can access the data and carry out analyses that weren’t possible before. We can find out things we never thought we could know.”

More specifically, this means TfGM can establish exactly, and in real time, how many people are travelling on the transport network, in which stations, and plan accordingly to have enough trams and staff in the right place, at the right moment. In theory, this advance should limit the number of crammed carriages and angry commuters. 

Last summer, the city introduced a contactless system on the Metrolink light rail service – an additional wealth of data, which enables Snowflake to gather extra insights by also tracking travellers as they tap in and out of the transport system. 

Lowe explained that since then, TfGM has identified exactly when the rush hour peaks in the morning and in the evenings – which before was only estimated using anecdotal evidence. In an effort to spread the peaks, the organisation is launching a “contactless early bird” initiative, which offers off-peak prices to commuters if they tap in outside the hours identified by Snowflake. 

Lowe said Snowflake’s technology will allow for immediate feedback on projects like contactless early bird. “We’ll be able to understand how the initiative has an impact on the network, and from there make evidence-based decisions, rather than perception-based ones,” he said.

It is only the start of Greater Manchester’s switch to smarter transportation, partly driven by public demand, said Lowe.

In London, planners have been using big data for a number of years already, based on contactless card payments, Oyster card tapping, as well as Apple Pay, Android Pay, and even cameras set up around the network to keep an eye on traffic flows. Every week, the city’s transport organisation, Transport for London (TfL), collects 19 million smart-card ticketing transactions.