Big Data Quality or Quantity. by Jorgen Pedersen and Tip Franklin

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1 Big Data Quality or Quantity by Jorgen Pedersen and Tip Franklin

2 To set the scene Big data is like teenage sex: Everyone talks about it, Nobody really knows how to do it, Everyone thinks everyone else is doing it, So everyone claims they are doing it. 14 June

3 Points of Discussion 1. Quick look at traditional ITS technologies 2. Overview of some current progressive ITS technologies 3. Where will data take us 4. How can we use this data 5. Data Visualization 6. Answering the Question Quality or Quantity? 7. The Processes Surrounding Successful Integration 14 June

4 Binary Data Delivery Occupancy Volume Can deduce flow But does count every vehicle ` Not robust Maintenance nightmare 60% functional

5 Non-Binary Data Delivery Volume Classification Can deduce flow Counts every vehicle Speed ` Not robust Maintenance nightmare 60% functional

6 GFVD GPS Floating Vehicle Data 1:00P 12:50P 12:40P 12:30P 12:20P 12:10P 12:00P 11:50A 11:40A 11:30A 11:20A 0.5m 1.0m 1.5m 2.0m 2.5m 3.0m 3.5m

7 GFVD GPS Floating Vehicle Data 1:00P 12:50P 12:40P 12:30P 12:20P 12:10P 12:00P 11:50A 11:40A 11:30A 11:20A 0.5m 1.0m 1.5m 2.0m 2.5m 3.0m 3.5m

8 GFVD GPS Floating Vehicle Data 1:00P 12:50P 12:40P 12:30P 12:20P 12:10P 12:00P 11:50A 11:40A Rich data availability Speed Volume Can Clearly identify flow Can identify Prediction Statistically representative? Most accurate at peak flow Arterial Assessment? 11:30A 11:20A 0.5m 1.0m 1.5m 2.0m 2.5m 3.0m 3.5m

9 CFVD Cellular Floating Vehicle Data 1:00P 12:50P 12:40P 12:30P 12:20P 12:10P 12:00P 11:50A 11:40A Rich data availability Speed Volume Can Clearly identify flow Can identify Prediction Phones don t have to be on Statistically representative? Most accurate at peak flow Arterial Assessment? 11:30A 11:20A 0.5m 1.0m 1.5m 2.0m 2.5m 3.0m 3.5m

10 MVT176 MVF786 ACESS1 T176KY T1876 -VA -MD -TX ALPR Automatic License Plate Recognition MVT176 MVF786 T176KY ACESS1 HFT12 T1876 -VA -MD -TX MVT176 MVF786 ACESS1 T176KY HFT12 T1876 POP8 -VA -MD -TX MVT176 K16754 ACESS1 T176KY T1876 HFT12 POP8 -VA -MD -TX WQ786 ACESS1 T176KY K16754 HFT12 T1876 POP8 -MD -TX -VA WQ786 ACESS1 LU123Y K16754 HFT12 T1876 POP8 -MD -TN -VA WQ786 LU123Y K16754 HFT12 T1876 POP8 -MD -VA -TN WQ786 LU123Y K16754 FJ897Y HFT12 POP8 -VA -CA -MD -TN 14 June

11 ALPR Automatic License Plate Recognition Red Speed Toll Collection Light Enforcement Rich data availability Speed Volume Can Clearly identify flow Can identify prediction Identifies every vehicle Single Unit - Multi Uses Single/Multi Lane Scanning Local processing Low bandwidth 14 June

12 Connected Vehicle Smart City All imaginable data Statistically Representative Using DSRC Prediction Cars communicating with: Flow Cars Speed of every road Street lights Safety Traffic Signals Predictive braking Workers on Foot Warning other users DeliveringSignal Controls Adaptive Speed Maximize flow Braking Minimize pollution Road Road Userconditions Charging Lights Road on Charging Open Windscreen Hot Lanes wipers on Transit to Passenger Express Lanes Comms

13 But what is Big Data? We have identified some of the elements required to deliver big data. But what is big data? Big data is the concept of collecting data from multiple different and often disparate data sources with a view to identifying underlying data trends. For example: Google predicted an outbreak of Flu due to the numbers of people searching cold medications CDC took a week longer. We identified the volcano effect using CFVD, what if we now combined cell tower usage in the event that we started to identify a complete lack of data? If this peaks disproportionately, this could be just one of many signs of congestion. Undertaking an Enterprise Asset Management Program MTA recently identified that the main contributors to metro failures, could be significantly reduced by understanding and analyzing the data they currently have. 14 June

14 Data Visualization Tools Data Integration Tools Data Visualization Tools Management Dashboards Mapping Tools Standardization TMDD Processes 14 June

15 Quality or Quantity? Both. But data is only as good as the systems, standards, processes, tools and capabilities of the underlying systems and environments in which it is to be used. As data transitions and becomes richer, and more abundant, the limitations of what we can do with it will be only limited by our own imaginations. Cautionary note. One of the limitations with currently available data is cost from commercial distributors. It is therefore suggested that these new and abundant data sources, should remain in the public domain, enabling this data to be used to improve every element of the transportation network from collection, operational control to public dissemination. 14 June

16 Mr. Tip Franklin 14 June

17 Extracting Qualified Intelligence Data Data Data Data Data Data once collected begets Information Information through Systems develops Intelligence Intelligence once distributed becomes Actionable The development of big data requires multi-layered data extraction capabilities, all of which individually or collectively can be used by: Other Systems Operations Staff Processes Processes Processes Filters Processes Processes Information Information Information Information Information Systems Systems Processes Systems Systems Systems Intelligence Intelligence Intelligence Intelligence Intelligence 14 June

18 To Ensure Successful Transition In order to understand and address this increase in data availability and successfully make transition from data to information we must: Better understand the data sources. Understand data limitations Understand data gaps Understand how data gaps can be filled to provide smoothed data Deliver automated pre-processing systems which convert in real-time In order to use information as actionable intelligence we must: Better integrate data from multiple data sources Have the ability to (both automatically and manually) remove or add additional data sets to complete an otherwise incomplete picture Better data visualization Provide automated updates to TMC Operators and Managers Intelligence Distribution Network 14 June

19 Questions to be Answered? Some of the questions that must be addressed: How will you use data? How will you process, store and distribute it? What will be your litmus test as to viability, usability and credibility? How do you identify and control your sources? How will you process visual and aural data (i.e. CCTV, machine vision and telephonic reports? How will you prioritize establishment, restoration and maintenance of data sources? How will you prioritize the flow of data through the various communication media? What is the evaluation process for inclusion/exclusion of sources? Who else can use your data; how do you get it to them and in what form (raw or processed); and what is the timeline to provide it? What are the external sources of data that could be useful to you? What is the filter process for identifying the correct data to store for planning processes? What changes, if any, need to be made within your personnel organization and individual attributes (skills, knowledge and abilities SKA) to be able to handle this? 14 June

20 Thank you for listening Jorgen Pedersen Business Development Manager (ITS) Allied Vision Technologies Tip Franklin Generally old bod Company No Bloody Idea or Homing Pigeon Any Questions? 14 June

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