Procurement of Floating Car Data A National Strategy. Edoardo Felici

Transcription

1 Procurement of Floating Car Data A National Strategy Edoardo Felici

2 A unique alliance of 19 authorities

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4 Traffic information Up-to-date, complete and unambiguous

5 Traffic management Central source for all road authorities

6 Traffic policy and traffic research

7 Goals Less traffic jams Safer roads Less emissions

8 Happy road users

9 Services Purchasing data Quality monitoring Purchase and management of IT-systems Shared (open) data portal ( Standardisation and transition management Shared services Slide 9

10 Real-time traffic data of 7100 km of national, provincial and municipal roads

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12 Loops, camera s, Bluetooth, ANPR and FCD

13 Framework agreement for purchasing real-time traffic data With 14 private companies: ARS T&TT Be-mobile Cellint Connection Systems Goudappel Coffeng HERE HIG Dynniq (Imtech) Swarco Technolution TomTom Traffic ITS (INRIX reseller) Vialis VID Slide 13

14 What is NDW facing? Task to reduce the cost of data procurement by 15%; Pressure from road authorities to reduce the amount of roadside equipment along the road networks; High investments needed to replace measurement infrastructure in the coming years; Political interest in using ITS-related technologies. Slide 14

15 Other important factors! New legislation for sound level measurements (SWUNG) based on traffic volumes (and corresponding vehicle classes). Massive investment done in the past years in roadside equipment which still has a good technical life expectancy. Uncertainty in procuring Floating Car Data: how do we measure the quality and predict the effects on existing traffic models and legacy IT-systems? Slide 15

16 Procurement strategy framework Slide 16

17 Preparations for procurement FCD pilots What can we do with off-the-shelf FCD-products? Can FCD be used to show travel times on VMS? Are FCD travel times still useful at very low traffic volumes? How does FCD perform in city environments? What can be done with origin-destination data? What can be done with incident detection? Slide 17

18 Preparations for procurement FCD pilots Which customized products can we ask? Can FCD be used for bicycle data? Can FCD be used for traffic safety reports?(v85) Slide 18

19 Preparations for procurement FCD pilots What can we use in a traffic management environment? Can (predictive) FCD be used to open and close rush hour lanes in a Traffic Management Center? Can FCD be used to operate back of-queue warnings on VMS? Research in the Innovatiecentrale (Traffic Innovation Centre) Slide 19

20 Travel times pilot in province of Zuid-Holland Looking at 50 routes in the region (highways, provincialand city roads) Comparison between FCD and NDW-data (our ground truth ) Looking at various performance measures Two data batches (21-28 Sep 2015 and 8-18 Nov 2015) Slide 20

21 Quality measures on 5-min aggregations Quality measures: averaging the difference between FCD and NDW data every 5 mins. Different ways to determine average. Not looking at patterns! Differences can be caused by: Natural deviation of travel times Routes don t match eachother exactly alignment needed! Spread of speeds/travel times during congestion NDW data is not ground truth data! Slide 21

22 Quality measures Root mean square error (RMSE): Mean absolute error (MAE): Mean absolute percentage error (MAPE): Slide 22

23 QKZ method (Qualitätskennzahl) Determining if congestion is correctly detected (hit rate) or wrongfully detected (false alarm) QKZ does not look at the intensity or duration of congestion Slide 23

24 Latency analysis Moving the data to see if a better pattern match can be made Slide 24

25 End of 2016: procurement for national coverage with FCD!