IoT plus big data analytics translate into better services management at Auckland Transport

IoT plus big data analytics translate into better services management at Auckland Transport
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The next BriefingsDirect business transformation innovator case study discussion explores the impact and experience of using Internet of Things (IoT) technologies together with big data analysis to better control and manage a burgeoning transportation agency in New Zealand.
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To hear more about how fast big data supports rapidly-evolving demand for different types of sensor outputs — and massive information inputs — we’re joined by Roger Jones , CTO for Auckland Transport in Auckland, New Zealand. The discussion is moderated by me, Dana Gardner , Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: Tell us about your organization, its scope, its size and what you’re doing for the people in Auckland.
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Jones: Auckland Transport was formed five years ago — we just celebrated our fifth birthday — from an amalgamation of six regional councils. All the transport functions were merged along with the city functions, to form a super-city concept, of which transport was pulled out and set up as a council-controlled organization.
But it’s a semi-government organization as well. So we get funded by the government and the ratepayer and then we get our income as well.
We have multiple stakeholders. We’re run by a board, an independent board, as a commercial company.
We look after everything to do with transport in the city: All the roads, everything on the roads, light poles, rubbish bins, the maintenance of the roads and the footpaths and the grass bins, boarding lights, and public transport. We run and operate the ferries, buses and trains, and we also promote and manage cycling across the city, walking activities, commercial vehicle planning, how they operate across the ports and carry their cargoes, and also carpooling schemes.
Gardner: Well, that’s a very large, broad set of services and activities. Of course a lot of people in IT are worried about keeping the trains running on time as an analogy, but you’re literally doing that.
Real-time systems
Jones: Yeah. We have got a lot of real-time systems, and trains. We’ve just brought in a whole new electric train fleet. So all of the technology that goes with that has to be worked through. That’s the real-time systems on the platforms, right through to how we put Wi-Fi on to those trains and get data off those trains.
So all of those trains have closed-circuit television (CCTV) cameras on them for safety. It’s how you get all that information off and analyze it. There’s about a terabyte of data that comes off all of those trains every month. It’s a lot of data to go through and work out what you need to keep and what you don’t.
Gardner: Of course, you can’t manage and organize things unless you can measure and keep track of them. In addition to that terabyte you talked about from the trains, what’s the size of the data — and not just data as we understand it, unstructured data, but content — that you’re dealing with across all these other activities?
Jones: Our traditional data warehouse is about three terabytes, in round numbers, and on the CCTV we take about eight petabytes of data a week, and that’s what we’re analyzing. That’s from about 1,800 cameras that are out on the streets. They’re in a variety of places, mostly on intersections, and they’re doing a number of functions.
They’re counting vehicles. Under the new role, what we want to do is count pedestrians and cyclists and have the cyclists activate the traffic lights. From a cycle-safety perspective, the new carbon fiber bikes don’t activate the magnetic loops in the roads. That’s a bone of contention — they can’t get the lights to change.;

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