Roland is currently working with TfL on the Surface Intelligent Transport System, which is looking to improve the insight available from existing and new data sources. Have worked on event driven architectures for many years and across many sectors although with a primary focus on Transport.

1. Urban Traffic Data Hack -
Zoology, Particle Physics and the
fine art of Data Science
Roland Major – Enterprise Architect
Transport for London
25 November 2015

2. Agenda
•What is the business challenge?
•What data did we use
•The Winners
•The feedback

3. A brief historyA growing city
3

4. Growing brings challenges

5. What is needed
• The ability to understand the real time
demand for time and space
• The intelligence to make sure the needs of all
customers drives what happens on our roads
• New thinking and better information, enabled
by technology

6. The Data bits
• We have limited the
scope of the data to a
wedge of South
London, which
follows two of a our
key corridors (A24
and A23)

7. Data Types
• We provided data which fitted into 4 main
categories
– Demand
– Performance
– Capacity
– Geospatial and Metadata

8. Demand Data
Urban Traffic Control and SCOOT:
This data provides the intelligence to our
traffic signals to optimise vehicle
movements. The induction loops record
vehicle presence as a count and as a
percentage of time, known as occupancy.
Oyster and Mastercard tap ins:
We have collated our Oyster card data
and have further information about
contactless payments through
Mastercard
0000001111111111111100000000000

9. A model segmentation of our network, based on Movement and Place
functions
TfL has proposed the concept of movement and place functions to
define our network, from this, 9 street types can to be defined.
Using our data can we define these functions and describe appropriate
boundaries for changes in use of our road network, leading to suitable
segmentation to define our street types within this region.
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ntationsv2 (dragged).pdf
Place
Movement
The Platform

10. Time For L - Movement Award
1.3.2. Influence of Incidents on demand and delay
For a given point on our network we would like to understand the effect of disruptions
on the observed traffic throughput and ultimately delay. When a disruption occurs in
the local area we understand the impact on the throughput of vehicles and delay incurred.
But it is when disruption occurred in the area surrounding the road, we are yet to quantify
a cost, where changes in demand are observed as a result of rerouting or avoidance
of disruptions. We would like to produce a breakdown of the key influencers of the
demand and delay.”
What does the environment look like around an incident?
• Pre incident - can we spot an incident before it’s reported? – Yes 60Mins
• Post incident - how is the surrounding area affected? – Yes flow tracked

11. SADL - Visualisation Award

12. David and the Ladies - Innovation

13. Feedback
• Superb! Not enjoyed myself so much over the weekend for ages*, better than
going on holiday. (*I should probably get out more !!!)
• Carlos & Charles - the data science toolbox was f-ing amazing, this is the 1st time
ever I’ve seen this in a hackathon. Keep up w/ the good work! :-)
• Great idea of having real cases documented and relevant big data. This is very
unusual to see in hackathons. Thanks to Carlos and DSL team for a fantastic w/e.
• Most of the "big data” hakathons Ive attended in the past were not really about
big data at all, you know: a few larg-ish .CSV and stuff and few “ analyse this data”
kind of thingy. the UrbanDataHack was truly big data&datascience. Never seen a
hack were +120 ppl could access +TBs of complex data and use so many data
science tools. Thanks, Carlos & Charles. more pleeeeeaaaaaassse!
• lack of beer #fail! but everything else cool
• One of the TfL bosses said they want to do more of this with DSL? Any dates
planned for the next one??

14. Questions