An end to the rush hour crush? How data is liberating road and rail

“What is your idea of heav­en?” asked author Stephen Fry. His own dream: “The Sat­ur­day after­noon of an Ash­es match at Lord’s. And his idea of hell? “The M25 on a Tues­day in Feb­ru­ary.”

It’s a relat­able gripe. Traf­fic jams are the bane of mod­ern urban life. And rail offers no respite. Many com­muters live their own ver­sion of per­fect mis­ery every day at rush hour.

City plan­ners have strug­gled to ease con­ges­tion on road and rail. The cur­rent set of solu­tions are large­ly based around build­ing more infra­struc­ture, which takes years and costs tens of bil­lions of pounds. But now a new approach offers real hope, based on using data to opti­mise trans­port ser­vices and get more out of the exist­ing infra­struc­ture. The results have been dra­mat­ic.

Here’s an exam­ple. Like many cities, Boston, Mass­a­chu­setts, wants to encour­age com­muters to switch from cars to trains. But com­muters resist tak­ing the train at peak hours. In fact, at 8am, as the car num­bers soar, train usage plum­mets.

Moovit analysed the prob­lem. Moovit’s smart­phone app helps users find the best pub­lic tran­sit and shared mobil­i­ty routes for their jour­ney. The app is used by more than 200 mil­lion users in 2,500 cities, giv­ing it unprece­dent­ed reach. The data from each user jour­ney, anonymised to pro­tect users’ pri­va­cy, is col­lect­ed and stored by Moovit.

Data from Moovit made it pos­si­ble to see the actu­al com­mut­ing pat­terns from homes to offices across Boston. Researchers con­cen­trat­ed on the data along the 40-mile Worces­ter to Boston train line, in a radius of five miles.

The data revealed two crit­i­cal insights. First, the rea­son train usage declines at peak hours is a lack of park­ing spots at the sta­tions. The car parks are full by 7.45am. Com­muters have no choice but to make their jour­ney to Boston by car.

Sec­ond, there is no option to get to the rail­way sta­tion with­out using a car; so-called first-mile pub­lic ser­vices are poor to non-exis­tent in the sub­urbs. Moovit knows this because it oper­ates the world’s largest data­base of pub­lic and pri­vate trans­porta­tion, and it became clear that most com­muters need a car to get to the sta­tion.

This foren­sic trav­el data informed the options for city plan­ners. They can build more car park­ing spaces, which is expen­sive — if there is vacant land. They can intro­duce road tolls, viewed as mere­ly con­tain­ing rather than solv­ing the prob­lem. Bus­es to trans­port com­muters to the sta­tion might be an answer, but the data sug­gests few pro­duc­tive routes.

Or the gov­ern­ment can intro­duce a sub­sidy for first-mile ser­vices, such as Uber and Lyft, or oth­er options that take com­muters to the sta­tion. Sub­si­dies are com­mon­place in pub­lic trans­port, cur­rent­ly amount­ing to a tril­lion dol­lars glob­al­ly, and in this case would boost rail usage even when car parks are full, and free up road space. For the first time, city plan­ners are able to con­sid­er all options with all the data at their fin­ger­tips.

“City plan­ners are only just real­is­ing the role data plays in improv­ing ser­vices,” says Moovit co-founder and chief exec­u­tive Nir Erez. “What shocks them is the scale and gran­u­lar­i­ty of Moovit’s data.” Moovit cur­rent­ly gath­ers four bil­lion anonymised dat­a­points a day from the user­base. It tracks sub­jects the minute they leave the house, which routes they take and when they arrive. Moovit’s infor­ma­tion offers trans­port pol­i­cy­mak­ers an unprece­dent­ed insight into the pref­er­ences and routes of the pop­u­la­tion.

“We find some cities are still employ­ing stu­dents with clip­boards to run sur­veys on commuters,”says Mr Erez. “It means they run sur­veys only annu­al­ly or every few years. With Moovit, you learn imme­di­ate­ly by analysing the data from tens or hun­dreds of thou­sands of indi­vid­u­als.”

Bus routes are being redrawn using con­sumer data. The bus­es can reflect where users actu­al­ly want to go. And for cities with min­i­mal tech­nol­o­gy bud­gets, Moovit’s Time­Pro prod­uct puts a GPS device right on bus­es for author­i­ties and rid­ers alike to access on their smart­phones to know exact­ly where bus­es are. No more stand­ing at the bus stop with no clue when the bus will arrive.

Moovit’s infor­ma­tion offers trans­port pol­i­cy­mak­ers an unprece­dent­ed insight into the pref­er­ences and routes of the pop­u­la­tion

In Italy, foot­ball club AS Roma is work­ing with Moovit to help fans get to and from the sta­di­um more effi­cient­ly. The app inte­grates Rome’s trans­port sys­tem – roads, trams, rail and metro – and guides fans to the exact gate as well as com­mu­ni­cat­ing real-time tran­sit changes. This improves jour­ney times with fans no longer get­ting lost or encoun­ter­ing rivals.

“The best anal­o­gy is with air traf­fic con­trol,” says Mr Erez. “Air­ports use real-time data to man­age flights as they take off and land. The flight pat­tern is opti­mised to allow for tight sched­ules. Can you imag­ine an air­port with­out an air traf­fic con­trol sys­tem? The flights would be a tenth as fre­quent. Pas­sen­gers would be in mis­ery. Chaos would reign.

“It is the same with cities. They need to know the demand for mobil­i­ty from all their cit­i­zens and the sup­ply of all forms of mobil­i­ty, sup­plied with real-time data. When they do that, like air­ports, they can increase trans­port effi­cien­cy with­out increas­ing infra­struc­ture.”

The need to use data to opti­mise city trans­port will only inten­si­fy as new mobil­i­ty ser­vices go main­stream. Autonomous cars, scoot­ers, cycle schemes and ride-shar­ing, will need to be fac­tored into city plans. It won’t be easy.

City plan­ners keen to adopt a data-led approach will dis­cov­er sim­plic­i­ty and depth from Moovit’s cloud-based prod­ucts. With­in min­utes of log­ging on, city plan­ners can begin to explore the move­ment of cit­i­zens and start reshap­ing ser­vices.

“The chal­lenge for cities is pro­found,” Mr Erez con­cludes. “As demand grows, roads will get more clogged. Sim­ply build­ing more infra­struc­ture is huge­ly expen­sive. A sin­gle road or train line can cost bil­lions. The solu­tion is to use data to max­imise exist­ing infra­struc­ture. Data can help you keep roads clear, help you improve bus and rail sched­ules and routes, and make deci­sions based on fact, not intu­ition. The cities of the future will all run on data. We are only just explor­ing the poten­tial.”

To find out more please vis­it Moovit.com