Traffic Management with
 IBM InfoSphere Streams




Haris N. Koutsopoulos and MahmoodRahmani
 Department of Transport Scie...
Outline



     • The problem
     • Opportunities
     • Role of IBM Infosphere Streams




                             ...
Urbanization
In 2007, more than 50% of the world’s population lived in cities
By 2050, it is expected to be more than 70%
...
Traffic congestion




• Productivity
• Quality of life
• Environment and scarce resources

                              ...
Intelligent Transportation Systems (ITS)

        Sensor, communications, computing, and
        IT technologies to improv...
Stockholm region data
    • Traffic data
       – loop detectors
       – GPS (1500 vehicles)
       – microwave detectors...
Data Overload



                • Information has
                  gone from scarce
                  to superabundant.
...
The ITS Laboratory at KTH

     • NexTMC3: Next Generation Traffic
       Management, Communications, and
       Control C...
The ITS Laboratory at KTH

     • Real time streaming data
       traffic + PT + environment + weather + ….
     • Hardwar...
The ITS
Laboratory
at KTH




             10
(Real Time) Travel/Traffic Information




                                     11
Problem Characteristics
     • Large quantities of continuous,
       heterogeneous data streams in
       motion
     • R...
Powered by InfoSphere Streams
                                               Real time delivery
Streams delivers:
 Ability...
IBM InfoSphere Streams
     • Scalability
     • Modularity and Extensibility
          A toolkit of basic stream-relation...
Stream Computing
  Continuous Ingestion   Continuous Complex Analysis in low latency




                                 ...
InfoSphere Streams


                     Connections
High level
                                   PE        PE
         ...
Stockholm data

    • 1500 vehicle probes
       • More expected in the future
    • 10 million GPS points per month
    •...
Example: Impact of weather




                             18
Example: Speed




                 19
Example:Speed Variability




                            20
Conclusion

     • Increased availability of large
       amounts of traffic data

     • IBM Infosphere Streams provides ...
Acknowledgments

    •   IBM Sweden and IBM Watson Labs
    •   Swedish Traffic Administration
    •   Trafik Stockholm
  ...
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IBM Business Analytics and Optimization - Traffic Management with IBM InfoSphere Streams

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Professor Haris och hans forskare på KTH har ett samarbete med de som utvecklar InfoSphere Streams på IBM Research. I detta projektet analyseras trafikdata från Stockholmsområdet för att se hur man kan nyttja informationen på bästa sätt för att styra trafiken smartare och informera resenärerna om hur man tar sig fram på bästa sätt.
Förutom själva Stockholmsområdet, så analyserar man trafiken till/från Arlanda. Bland annat vill man prediktera sannolikheten för att man kommer i tid till sin flygavgång på Arlanda beroende på vilken tid man skall åka och beroende på vilket transportsätt man väljer. KTH är vinnare till priset för en smartare planet 2010.

Talare: Haris N. Koutsopoulos, Professor and Head of Transportation and Logistics Division, KTH

Denna presentation hölls på ett seminariepass för Business Analytics & Optimization under IBM Software Day 2010.

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IBM Business Analytics and Optimization - Traffic Management with IBM InfoSphere Streams

  1. 1. Traffic Management with IBM InfoSphere Streams Haris N. Koutsopoulos and MahmoodRahmani Department of Transport Sciences, KTH ErlingWeibust, IBM 1
  2. 2. Outline • The problem • Opportunities • Role of IBM Infosphere Streams 2
  3. 3. Urbanization In 2007, more than 50% of the world’s population lived in cities By 2050, it is expected to be more than 70% 3
  4. 4. Traffic congestion • Productivity • Quality of life • Environment and scarce resources 4
  5. 5. Intelligent Transportation Systems (ITS) Sensor, communications, computing, and IT technologies to improvethe efficiency and safety of the transport system Widespread adoption of data collection technologies 5
  6. 6. Stockholm region data • Traffic data – loop detectors – GPS (1500 vehicles) – microwave detectors – traveltimes • Public transport • Environmental data • Weather • Infrastructure and roadworks • Parking • Incidents and events 6
  7. 7. Data Overload • Information has gone from scarce to superabundant. That brings huge new benefits but also big headaches. Economist, Feb. 2010 7
  8. 8. The ITS Laboratory at KTH • NexTMC3: Next Generation Traffic Management, Communications, and Control Center for Sustainable Urban Transport • Support from IBM (Shared University Research Award), Transport Administration, Trafik Stockholm, KTH 8
  9. 9. The ITS Laboratory at KTH • Real time streaming data traffic + PT + environment + weather + …. • Hardware • IBM Blade Center • 10 blade servers (HS22) • 80 CPU cores 2.53 GHz • 240 GB of memory • 16 TB external storage • Software IBM Infosphere Streams and databases Redhat Linux 9
  10. 10. The ITS Laboratory at KTH 10
  11. 11. (Real Time) Travel/Traffic Information 11
  12. 12. Problem Characteristics • Large quantities of continuous, heterogeneous data streams in motion • Real time operations • Performance and scalability • Information on demand • Traffic management centers • Individuals • Fleet monitoring • Exceptions/deviations • Complex analytics 12
  13. 13. Powered by InfoSphere Streams Real time delivery Streams delivers: Ability to fuse structured and Powerful unstructured data types Analytics Scalability for large urban traffic management centers Millions of Microsecond Intuitive programming model events per Latency second Example: GPS location mapping 4 x86 blade servers Traditional / 250,000 GPS probes per second Non-traditional data sources Mapped to 630,000 road segments 13
  14. 14. IBM InfoSphere Streams • Scalability • Modularity and Extensibility A toolkit of basic stream-relational operators and user defined operators (in C++ or Java) • Stream adapters to ingest/publish data • Fast processing of high volumes of data Query on streams Parallel/distributed platform • Complex analytics • High level programming language 14
  15. 15. Stream Computing Continuous Ingestion Continuous Complex Analysis in low latency 15
  16. 16. InfoSphere Streams Connections High level PE PE PE Job manager Source language source compiler PE PE Sink PE PE PE PE PE Sink Source PE PE PE Sink Source PE PE Sink PE Processing Processing Processing Processing Processing Element Element Element Element Element Container Container Container Container Container Streams Data Fabric Physical Network TCP-IP / Ethernet x86 x86 x86 x86 x86 X86 X86 X86 X86 X86 Node Node Node Node Node Blade Blade Blade Blade Blade 16
  17. 17. Stockholm data • 1500 vehicle probes • More expected in the future • 10 million GPS points per month • 1,000 GPS points per minute peak • 600,000 road segments in a 80km x 80km area 17
  18. 18. Example: Impact of weather 18
  19. 19. Example: Speed 19
  20. 20. Example:Speed Variability 20
  21. 21. Conclusion • Increased availability of large amounts of traffic data • IBM Infosphere Streams provides the real time stream processing capabilities required to facilitate applications and services targeting serious traffic problems 21
  22. 22. Acknowledgments • IBM Sweden and IBM Watson Labs • Swedish Traffic Administration • Trafik Stockholm • Stockholm City • KTH 22

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