05 - Innovation Lab - Multisource Traffic Density v1.0

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1. Multisource measurement of traffic density - Trademex Guillaume Levasseur, R&D Team 01/02/2019

9. Traffic metrics

14. Future work Theory Consolidation of heterogeneous data with traffic metrics Spatial aggregation of metrics Traffic jam detection through flow - density diagram Practice Validate theory with real world data Check scalability for big datasets Levels of service Motivation Get traffic indicators from real - time measurements

12. Data pipeline overview 12

13. Data pipeline overview 13

7. Traffic: a compressible fluid Naive flow conservation flow A = flow C + flow E 7

2. What tools for traffic analysis? 2 Navigation solutions Model - based solutions Source: Google Source: PTV Group

5. Striving for real - time traffic analytics 5 Dynamic traffic indicators - Levels of service - O/D matrix ?

8. Naive flow conservation 8 Source: report.az flow A = flow C + flow E Traffic: a compressible fluid

6. Traffic: a 4 - dimensional space X Y 6 Source: Google

3. Per vehicle data 3 Averaged data Source: Google Source: Satellic Source: Google Source: Wikimedia x70 x 600 000 x?? x700 What tools for traffic analysis in Trademex ?

11. Spatial aggregation 11 ∆t = 1 min x 1 x 2 x 1 →x 2 Operator N [veh] q [veh/h] v [km/h] k [veh/km] 9 540 12.4 43.5 19 1140 45.1 25.3 9 540 24.4 28.8 = minimum = minimum = harmonic mean = arithmetic mean

10. Traffic theory 10 Source: Wikimedia (adapted) Fundamental equation: 푙푙푙푙푙푙 ∆ 푥푥 → 0 , ∆ 푡푡 → 0 푘푘 = 푞푞 푣푣 ⇒ 푞푞 = 푘푘 푣푣 flow = density x speed

4. What tools for traffic analysis? 4  Levels of service ( left )  O/D matrices  Counts  Percentages  Trip time estimation ( below )

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