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Predictive Capability within Transport for London Vladimir Vorotovic – Lead Modelling Specialist, Transport for London
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TfL’s Modelling Capability TfL strategic planning: - LTS, SATURN through LoHAM models TfL Operational modelling: - ONE (Visum), Vissim, Aimsun, LinSig, TRANSYT Visualisation - 3ds Max, CGIs, 360 videos, VR, MR
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TfL’s operational modelling hierarchy
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Predictive Modelling Capability – 2010/11 Demonstrators Enabling the vision “Predict traffic conditions in the next 30 minutes and take action to manage the effects within 5 minutes”
Capability for:
What is modelling and why do • Real-time data management we need it?• Real-time incident detection • Real-time traffic network modeling • Faster than real-time assessment of alternative traffic (signal) management scenarios
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Real Time Operations - RODAT Existing on-street sensors Automatic Traffic Counters
ANPR cameras
Flow and Journey Times along routes ending at Piccadilly – AM Peak 0700-1000 SCOOT
Data fusion Innovative algorithms & computing
Outputs Real-time detailed view of traffic behaviour on our network
ANPR ATC
SCOOT
Combined Data
Flow and Journey Times along routes originating from the A4 GWR – AM Peak 0700-1000
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Project EDMOND (Estimating Demand from MObile Network Data)
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The best modelling software in the world (“offline”) • PIN 3 levels: Network, Micro-simulation and Tactical • Industry Day (60 visitors, 40+ companies) • PQQ (7-8 suppliers per level) • ITT (up to 3 per level) Network: LinSig and TRANSYT Micro-simulation: Aimsun and Vissim Tactical: Aimsun, Visum and SATURN
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SCOOT: Where and how many sites?
Split Cycle Offset Optimisation Technique
• London has c.6300 sets of traffic signals. • c. 4953 of the signals are centrally controlled by TfL’s UTC system. • c. 4300 operate intelligently using UTC SCOOT (shown figure left). • SCOOT (a.k.a. RTO) – tender underway – contract award aim October 2017. • SITS Phase 2 – post RTO capability assessment
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SITS (Surface Intelligent Transport System)
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SITS video
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Contact
Vladimir Vorotovic Lead Modelling Specialist
Email address:
[email protected] Telephone: 020 3054 2695