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Big Data and Transportation: Use Cases for Urban Planning

The right recipe for cooking up better urban planning using big data and transportation

Despite the morning rush hour, the platform looks deserted. Ten minutes go by — not the slightest clack of a train. Twenty minutes go by — your mobile phone emits a pathetic blip. The battery is down to 10%. No sign of a train, though.

“Excusez-moi,” you tentatively ask a loitering couple. “Do you happen to know when the airport train will arrive?” They look bewildered. “But it’s a public transport strike today, so *Parisian shrug* it could be any time, but not today.”

Back in those dark ages, Uber isn’t yet a thing. You need to rush all the way up the escalators, jump into the middle of the road to stop a passing taxi in its tracks, gesture deliriously that you need to get to the airport, and pray that there are no traffic jams on the way. Because you’re already late.

Today, your average traveler is far more connected thanks to the ubiquity of real-time big data in transportation. You know exactly when to leave, which mode of transportation to take, and what your contingency plan is if there’s a strike, a blizzard, an alien invasion, or any other type of traffic disruption.

Yet as cities grow denser and more crowded with private, public, and shared vehicles, managing the entire conundrum becomes not only harder but critical.

Time to shift gears: How will big data change the future of transportation?

According to the United Nations, there are 37 megacities today — dense metro areas with a population of 10+ million. By 2030, that figure is projected to increase to 47.

This highly concentrated form of urban dwelling we are shifting towards poses a host of challenges including resource supplies, waste management, and rising inequality.

But arguably the biggest issue (and at the same time, solution) is transportation management.

City congestion levels and population density

Big Data and Transportation: Use Cases for Urban Planning

Source: Accenture — Society disrupted, now what?

In addition to people, world cities house a growing array of mobility players:

  • Personal vehicles
  • Public transport fleets
  • Commercial fleets and last-mile delivery providers
  • Micro-mobility actors (e-scooters, shared bikes, etc.)
  • Mobility as a service providers

All of them navigate a city’s arteries at different capacities, with fluctuating demand, under varying weather conditions. Such ubiquity brings new dilemmas:

  • What should we do about ride-hailing apps: ban them, regulate them, or leave them be?
  • Does expanding shared bike infrastructure help decongest roads?
  • How do we minimize the impact on existing traffic flows of constructing new infrastructure for shared mobility, e-vehicles, and public transport?
  • And my personal favorite — Where should we put e-scooters?

Given the rapid growth in emerging mobility sectors such as ACES vehicles (autonomous, connected, electric, shared) and micro-mobility solutions, we cannot afford to leave those questions unanswered.

And this means we need to reconcile big data and transportation management.

There’s no shortage of big data in logistics, transportation, and urban planning. But that raw intel is rarely integrated into transportation planning activities, or is used only to a limited extent.

So how does big data affect the transportation industry at the moment? What can we do to put it to better use in the future? Buckle up and let’s go on a drive around the block.


Read the full article here.

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