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Urban Traffic Data Hack -
Zoology, Particle Physics and the
fine art of Data Science
Roland Major – Enterprise Architect
Transport for London
25 November 2015
Agenda
•What is the business challenge?
•What data did we use
•The Winners
•The feedback
A brief historyA growing city
3
Growing brings challenges
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
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)
Data Types
• We provided data which fitted into 4 main
categories
– Demand
– Performance
– Capacity
– Geospatial and Metadata
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
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.
/var/folders/wc/_g062vdx33g3n54s0j5cs3w80000gn/T/com.apple.Prev
iew/com.apple.Preview.PasteboardItems/UrbanTrafficDataHack_Prese
ntationsv2 (dragged).pdf
Place
Movement
The Platform
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
SADL - Visualisation Award
David and the Ladies - Innovation
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??
Questions

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URBAN TRAFFIC DATA HACK - ROLAND MAJOR

  • 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
  • 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. /var/folders/wc/_g062vdx33g3n54s0j5cs3w80000gn/T/com.apple.Prev iew/com.apple.Preview.PasteboardItems/UrbanTrafficDataHack_Prese 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
  • 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??

Editor's Notes

  1. London, a growing city London is growing and at a rate faster than previously estimated. The 2011 Census showed that London had grown by one million people in 10 years [2]. At this point the 2011 London Plan predicted London would grow to a population of 8.6 million by 2026 [3]. However this is estimated to have already been reached. [4] It is now expected that the population will continue to grow and by 2036, London will have a population of 10.49 million (2.06 million additional people to 2013) [4]. This growth is approximately equivalent to the populations of Manchester, Bristol and Birmingham moving to London over 20 years.
  2. While TfL is responsible for public and private transport we would prefer if you focused this weekend on private transport due to the data we have provided How could you use this data and our apis that are already available? Consider you pain points. Think about end-to-end experiential journey planning. Is your purpose really to get from Kings Cross to Victoria. What is your journey for and what are the factors you would like to make decisions on Consider accessibility across the network Do you always want to quickest route How do people react to crowds Can you visualise our data in a more insightful manner
  3. While TfL is responsible for public and private transport we would prefer if you focused this weekend on private transport due to the data we have provided How could you use this data and our apis that are already available? Consider you pain points. Think about end-to-end experiential journey planning. Is your purpose really to get from Kings Cross to Victoria. What is your journey for and what are the factors you would like to make decisions on Consider accessibility across the network Do you always want to quickest route How do people react to crowds Can you visualise our data in a more insightful manner
  4. While TfL is responsible for public and private transport we would prefer if you focused this weekend on private transport due to the data we have provided How could you use this data and our apis that are already available? Consider you pain points. Think about end-to-end experiential journey planning. Is your purpose really to get from Kings Cross to Victoria. What is your journey for and what are the factors you would like to make decisions on Consider accessibility across the network Do you always want to quickest route How do people react to crowds Can you visualise our data in a more insightful manner