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Using Autonomous Vehicle Standards to Extract Traffic Signal Data

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Presentation on theme: "Using Autonomous Vehicle Standards to Extract Traffic Signal Data"— Presentation transcript:

1 Using Autonomous Vehicle Standards to Extract Traffic Signal Data
Andrew Radford, Birmingham City Council

2 Using autonomous vehicle standards to extract traffic signal data
What is GLOSA? Required Information Potential Benefits WM GLOSA Project The Future

3 reen ight ptimal G L O S A peed dvisory Optimised Optimized Advice
Adaption “Slow Down” 00:04 00:03 00:05 00:01 00:00 00:02

4 Required Information What colour is the light?
How is the road configured? Where is the vehicle? Standards – CEN/TC 278 SPAT – Signal Phase and Timing MAP – Geographic Data CAM – Co-operative Awareness Message

5 Demo http://www.andyradford.uk/
Data Demo Demo

6 Potential Benefits Cost Policy Fuel Emissions Road Wear Braking Noise
Accidents A first step Towards CAV? Smoother journeys and journey times

7 WM GLOSA Dft funded, C-ITS deployment project
Adaptive Control Environment SCOOT MOVA Utilising Existing Infrastructure UTC Centre Mobile Phones / 4G Existing Wifi network ‘beacons’ = Cheap, Deliverable and Scalable

8 Location – A45 Coventry Road
UK CITE Greenwave GLOSA openstreetmap.org Kings Road Berkeley Road Forest Road Gilbertstone Road Lyndon Road Wheatsheaf Damson Parkway Birmingham Solihull Coventry SCOOT Joint SCOOT/ MOVA MOVA Bus Priority Coalway Avenue Wagon Lane Hatchford Brook Coventry Road – A45

9 Architecture

10 Why Freight? Uses Amey freight vehicles working on Birmingham Contract
Benefits from stop-start greatest with freight vehicles Policy desire to ‘do something for freight’ Known fleet ‘easy to measure’

11 Evaluation Methodology
System Indicators Latency Accuracy Availablity Output Indicators Number of Stops Journey Time Journey Reliability Outcome Indicators Air Quality / Fuel Use Driver Satisfaction Road Safety & others

12 System Indicators 500 runs total (250 in each direction)
300 runs SCOOT, 200 runs MOVA 3G/4G latency – av 0.481s no problem Data collection, some minor issues with app crashing etc System availability – no real problems Accuracy – problems at Wagon Lane but tests at 3 junctions

13 SCOOT Variation SCOOT makes a maximum change of 4 seconds per cycle. If it has changed in the previous cycle, it can change a further 1 second in that cycle

14

15

16 Geographical View

17 Speed / Distance / Phase view

18 Overall Results Total impact on GLOSA affected sections:-
6.8 stops  5.8 stops inbound (9 GLOSA junctions) 4.8 stops  4.3 stops outbound (6 GLOSA junctions) Stop is defined as at least 5 secs below 5mph

19 However… Inbound with GLOSA Without GLOSA Results Junction Samples
with GLOSA Without GLOSA Results Junction Samples Stops Av Speed Reliability Speed SD Notes 1991 158 -6% 40% 33% 1992 161 -28% 47% 17% 1994 147 0.96 17.716 80% 8% 10% 2105 137 -27% 19% 2110 144 0.48 20.308 -17% 22% -2% 2111 160 5.3634 99% -37% 2112 14% 0% 2115 159 108% 1% -23% SCOOT data incorrect 2130 154 0.408 21.3 61% -10% 9999 117 0.65 13.778 5% control junction Whole Route 92 5.8125 30% 7% Just GLOSA junctions Outbound 125 82% -20% -16% 129 56% -4% 121 -3% 27% 130 2% -19% 12.65 -25% 12% 119 -5% 107 26% all 81 4.9 18.92 -15% 24%

20 Outcomes Test drivers really liked the app and thought it was useful
Atkins to undertake analysis of ‘profiles’ to determine air quality impact – however, this is expected to be limited based on the results we have Not enough data for other outcomes MOVA inconclusive

21 The Future… Roll out to other comms equipped junctions in West Midlands – Tyburn Road Pedestrian Crossings More Robust Trial Improve the algorithms Establish use of data in modelling

22 Thank you


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