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Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)
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Driving the Future of Smart Cities - How to Beat the Traffic (Pivotal talk at Strata 2014)

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As traffic volumes in cities around the world are constantly growing we are faced with the challenge to track and control car movements in a more detailed and intelligent way to beat the traffic. …

As traffic volumes in cities around the world are constantly growing we are faced with the challenge to track and control car movements in a more detailed and intelligent way to beat the traffic. Real-time information on traffic including automotive sensors and crowd-sourced data feeds are an interesting new source of data. However, to utilize this data to its full extent and turn it into valuable information, intelligent methods for analyzing and predicting traffic are needed.

Pivotal’s Data Science Team has developed several innovative methods to analyze this traffic flow information harvested from real-time and in-car data sources including GPS. These methods by themselves are highly useful for predicting future traffic conditions and dissecting traffic data. We will describe how we created these algorithms and show different interesting results from their application. This example demonstrates how deeper insights into a problem can be found by combining different machine learning methods.

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  • 1. A NEW PLATFORM FOR A NEW ERA
  • 2. Driving the Future of Smart Cities How to Beat the Traffic Strata Santa Clara – February 13, 2013 Alexander Kagoshima, Data Scientist, @akagoshima Noelle Sio, Senior Data Scientist, @noellesio Ian Huston, Data Scientist, @ianhuston @gopivotal © Copyright 2014 Pivotal. All rights reserved. 2013 2
  • 3. What Matters: Apps. Data. Analytics. Apps power businesses, and those apps generate data Analytic insights from that data drive new app functionality, which in-turn drives new data The faster you can move around that cycle, the faster you learn, innovate & pull away from the competition @noellesio, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 3
  • 4. Pivotal’s Opportunity Uniquely positioned to help enterprises modernize each facet of this cycle today Comprehensive portfolio of products spanning Apps, Data & Analytics Converging these technologies into a coherent, next-gen Enterprise PaaS platform @noellesio, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 4
  • 5. The Connected Car Drives Innovation Telematics Stolen vehicle Remote recovery Behaviour diagnosis monitoring Remote Car2X Driver activation assistance solutions Floating Real-time eMobility car data parking info solutions Share my trip Traffic updates Car search Social Media Responsive Navigation PoIs Next gen Navigation Hybrid Predictive Navigation traffic info Map/PoI updates Vehicle Concierge Fleet Geo Tracking services Management fencing Handsfree telephony Music WiFi streaming hotspot Pay as Online you drive Payment Web games solutions radio Road Environmental Parking tolls browsing VoD space reservation Car sharing eCommerce City toll Rich media comms Car2X comms Communication Entertainment @noellesio, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 5
  • 6. Possible Data Science Use-Cases !  Predictive Car Maintenance –  More accurately predict part failure –  Optimize part repair and replacement schedule !  Leveraging Driving Behavior –  Useful to differentiate insurance pricing based on driving style –  Optimize car design !  Improving GPS Systems –  Establish baseline for traffic congestion –  Gain a detailed view on traffic –  Create more meaningful metrics for routing !  Predictive Power for Assistance Systems –  Optimize fuel efficiency –  Predict the future state of a car in the next 2 minutes (starts, stops, emergency braking) !  Traffic Light Assistance –  Signal timing of traffic lights –  Crowd sourcing of traffic signals @noellesio, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 6
  • 7. What does traffic data look like? © Copyright 2014 Pivotal. All rights reserved. 2013 7
  • 8. …like this? @noellesio, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 8
  • 9. How fast are vehicles moving? @noellesio, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 9
  • 10. How fast are vehicles moving? 0.015 0.000 0.005 0.010 density 0.020 0.025 0.030 Link 1000064869 0 50 100 km/h 150 200 @noellesio, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 10
  • 11. When do disruptions happen? @noellesio, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 11
  • 12. [seconds] When will the light change? @noellesio, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 12
  • 13. Taking Lessons From Other Disciplines Change-Point Detection can be used to uncover regimes in wind-turbine data. It can also be applied to uncover regimes in traffic light switching patterns. @noellesio, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 13
  • 14. Taking Lessons From Other Disciplines Link 1000064869 Different Cell Populations 0.015 0.010 density 0.020 0.025 0.030 Combined Component 1 Component 2 Component 3 Component 4 0.000 0.005 Different Driving Conditions 0 50 100 150 200 @noellesio, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 14
  • 15. Understanding Traffic Flow A dynamic, more detailed understanding of traffic is now possible. Can we answer both ‘What velocity?’ and ‘Why?’ Context !  Current GPS systems are based on average velocity over street segments !  Real-time traffic information (e.g. Waze) does not deliver detailed view nor prediction @akagoshima, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 15
  • 16. Our Approach – Multi-step algorithm From our experience, real-world data often requires multi-step procedures Step 1: Answer ‘What velocity?’ First find distinct velocity groups Link 1000064869 Find influencing effects ? ? ? ? ? ? 0.000 0.005 0.010 density 0.015 0.020 0.025 0.030 Combined Component 1 Component 2 Component 3 Component 4 Step 2: Answer ‘Why?’ 0 50 100 150 200 km/h @akagoshima, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 16
  • 17. Find Velocity Groups !  Velocity distributions can be fit well with Gaussians !  An ‘overlay’ of multiple Gaussians is called Gaussian Mixture Model 0.030 0.025 0.020 density 0.015 0.010 0.005 !  Shapes and positions of Gaussians determine velocity groups Combined Component 1 Component 2 Component 3 Component 4 0.000 !  GMM fitting of the velocity distribution is done by ExpectationMaximization algorithm Link 1000064869 0 50 100 150 200 km/h @akagoshima, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 17
  • 18. Gaussian Mixture Model Link 1000064869 Link 1000064869 0.030 Combined Component Component Component Component 1 2 3 4 0.020 density 0.010 0.005 0.000 0 50 100 150 200 0 50 100 150 200 km/h km/h Link 1000064869 0.025 1 2 3 4 density 0.000 0.000 0.005 0.005 0.010 0.010 0.015 0.015 0.020 0.020 0.025 0.030 Combined Component Component Component Component 0.030 Link 1000064869 density 1 2 3 4 0.015 0.020 0.015 0.000 0.005 0.010 density Combined Component Component Component Component 0.025 1 2 3 4 0.025 0.030 Combined Component Component Component Component 0 0 50 100 km/h 150 200 50 100 150 200 km/h @akagoshima, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 18
  • 19. 0.04 Predict Gaussians 0.00 0.01 "  Classification task! 0.02 density 0.03 !  The second step seeks to explain/predict which Gaussian a data point belongs to Combined Component 1 Component 2 Component 3 0 50 100 150 200 250 !  Features for classification: –  –  –  –  –  Time of day, day of week Weather Direction Special Events … ? ? ? ?… @akagoshima, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 19
  • 20. White and Black Boxes !  Analyze correlations between features of a data point and its assignment to a Gaussian !  From a Machine Learning point of view, this is classification !  Generate an interpretable model description !  Can capture more complex correlations " Explanation of behavior " Prediction of Gaussian assignment @akagoshima, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 20
  • 21. 0.04 Putting it all together… Combined Component 1 Component 2 Component 3 0.02 Average velocity = 85 km/h •  Weekdays after 8pm •  Weekdays before 2pm, exiting Average velocity = 120 km/h •  Weekends •  Weekdays before 2pm, not exiting Two-Step algorithm: GMM + Classification •  Identified multiple velocity profiles for every road segment •  Intuitive and easily interpretable results •  Highly scalable for more features and data 0.00 0.01 density 0.03 Average velocity = 45 km/h •  Weekdays between 2 – 8pm 0 50 100 km/h 150 200 250 @akagoshima, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 21
  • 22. Better Travel Time Prediction !  Traffic profiles emerged from data –  Without using metadata, we uncovered road segment traffic patterns !  Identified Bias Effects –  Inferring the impact of turns and day of week on velocity –  Able to predict rush hour by day and time by road segment !  Traffic Light Patterns –  Infer public transportation effects on traffic –  Automatically determine different switching patterns @akagoshima, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 22
  • 23. London Road Traffic Disruptions Can we predict when unexpected incidents will end? Publicly available data: !  Transport for London traffic feed (refreshed every 5 minutes) !  Weather Underground reports Photo by James Blunt Photography on Flickr (CC BY-ND 2.0) @ianhuston, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 23
  • 24. @ianhuston, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 24
  • 25. Major storm hits UK (Photo: BBC) @ianhuston, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 25
  • 26. @ianhuston, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 26
  • 27. @ianhuston, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 27
  • 28. Durations are very different for different types of incident. Mean duration for Surface Damage incidents is 107 hours! @ianhuston, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 28
  • 29. Rain affects duration in a surprising way. Incidents which start when it is raining finish faster than others. @ianhuston, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 29
  • 30. Models Linear Regression •  Disruption reports & weather features Random Forests •  Rounded categorical •  Regression Category MAP •  Only use category of incident •  Maximum Likelihood estimate @ianhuston, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 30
  • 31. Live Predictions http://ds-demo-transport.cfapps.io Using: @ianhuston, @gopivotal © Copyright 2014 Pivotal. All rights reserved. 31
  • 32. Summary ! Making use of a vibrant ecosystem of traffic data ! Innovative approaches needed to generate value from abundant and complex sources ! Connecting predictive models to traffic in the physical world is the future of smart cities © Copyright 2014 Pivotal. All rights reserved. 32
  • 33. Thank You! Check out more of our Data Science use-cases at www.goPivotal.com © Copyright 2014 Pivotal. All rights reserved. 2013 33
  • 34. A NEW PLATFORM FOR A NEW ERA

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