Traffic Data Hack Day – Register Now

**Registration for this event is now closed**


TfL’s Urban Traffic Control System (UTC) uses 12,000 sensors located at junctions across the Capital to measure traffic flow. The data produced is used to drive SCOOT – the traffic light optimiser system – and on Wednesday 6 April we’re aiming to generate greater value from this as-yet untapped data source.

We want to configure a data processing engine for UTC data and begin exploring this large data set using the tools business users are familiar with, such as R Studio and Tableau, as well as event streams, map reduce type platforms and machine learning tools.

The event is part of our ongoing drive to work with the dev community to create great products from our open data, and offers an opportunity to learn and experiment with cloud tools in a safe, sandbox type environment. Experts from TfL’s Road Space Management team will be on hand to provide support.

Where and when

Venue: Amazon Development Centre, Leadenhall Court, One Leadenhall Street, London, EC3V 1PP

6 April 2016 09:00 – 17:00

Lunch will be provided, so please let us know any dietary requirements.

To register

Please confirm your attendance by Tuesday 5th April – you can register for the event here:

Venue for the hack day
Amazon are hosting the Traffic Data Hack Day on April 6 at the Amazon Development Centre in Leadenhall Street, London

Format of the day

  • Opening presentation from AWS & Road Space Management team to give background on the solution and the data.
  • Demonstration of the loading and processing of the data from files into scalable storage.
  • Split into teams and begin the hack!


  • Our host – Amazon Web Services – will talk about the cloud and solutions we will be using
  • Loading and processing of data from files into scalable storage
  • Access to the data to identify analytics and visualisations that are possible based on the data and environment

Structure of the hack

The teams will be divided across three broad initiatives; Ingest, Analytics and Visualisation.

Ingest: The purpose of the Ingest work stream will be to identify new ways to bulk load data into the cloud. This team will be comprised of engineers who have an interest in learning about AWS big data technology and who’d like to explore these services, with hands-on support from AWS solutions architects.

Analytics: This work stream is focussed on utilising the solution to garner maximum value from the UTC data set. The group will be split into multiple teams, each with a specific focus. The teams will be comprised of Data Scientists with an interest in discovering new insights from a broad dataset.

Visualisation: The focus of this work stream is to display the data in a manner that makes it easy and insightful to consume and understand. We’ll be joined by ESRIi, who will likely take the lead on hacking a solution.

Tableu - Data over time (traffic flow)
We’ll be using tools business users are familiar with, such as Tableu. Here we see traffic data over time (traffic flow)

Who’ll be attending?

  • TfL employees working with information or systems that generate data
  • Academics working within this subject area
  • Data scientists
  • Transport app developers
  • Data visualisation developers

Innovation ideas

Areas of interest using UTC data (these are not prescriptive or mandatory and other ideas are welcomed and encouraged):

  • Understanding traffic flow
  • Identifying congestion
  • Estimating journey times

What do I need bring?

A laptop with WiFi, which you can configure to use cloud hosted tools. If you would like to interrogate the data, please ensure you have already installed analytics software (i.e. R and R studio).

Useful resources


    1. Hi Stu, there was a Twitter hashtag for the event – see #TfLOpenData. We’re hoping to get something written up on this blog as soon as possible, so please watch this space.

      Just in case you hadn’t seen it, there is also a data challenge kicking off tomorrow morning that may be of interest to you – see this post for details.

      1. That’s great thanks – I shall keep an eye on the data challenge too, but I cannot make it down to London for it.

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