USING BIG DATA AND DATA FUSION TO FACILITATE COOPERATIVE TRAFFIC MANAGEMENT

Transcription

1 USING BIG DATA AND DATA FUSION TO FACILITATE COOPERATIVE TRAFFIC MANAGEMENT 1

2 PAS DIT AAN OF VERWIJDER HET 2

3 From current to a Cooperative Mobility future Current situation Many legacy roadside systems large investments Vision and ambition National focus on cooperative traffic management A big GAP Short term return on investments? Short term applicability? Technological GAP Data Connections & Latency? 100% 80% 60% 40% 20% 0% ? Cooperative vehicles Cooperative traffic systems 3

4 From current to a Cooperative Mobility future Smart Traffic is a way to close this GAP Investment GAP Direct applicability Technological GAP Modelbased datafusion Predictive We introduce the term : Virtual Cooperative Mobility 4

5 TRANSITION 5

6 Today s traffic systems TLC Own detection Limited knowledge of local traffic state Fixed control strategy Reactive 6

7 Tomorrow s traffic systems TLC Cooperative Network wide knowledge of traffic state Dynamic TLC TLC TLC Proactive Cooperative traffic control TLC 7

8 Transition period 100% 90% 80% 70% 60% 50% 40% Cooperative vehicles Cooperative traffic systems 30% 20% 10% 0% ? 8

9 VIRTUAL COOPERATIVE MOBILITY 9

10 Virtual cooperative mobility Realtime input x, t, ID Realtime traffic model x, t x, t x, t x, t Realtime traffic state estimation x, t, ID x, t x, t x, t x, t 10

11 Virtual cooperative traffic system Virtual cooperative traffic system Cooperative traffic system 11

12 Virtual cooperative traffic systems 100% 90% 80% 70% 60% 50% 40% 30% All vehicles are virtual cooperative Current systems can be used No big investments needed Cooperative vehicles Cooperative traffic management Virtual cooperative vehicles Virtual cooperative traffic systems 20% 10% 0% ? 12

13 SMART TRAFFIC 13

14 Smart Traffic Monitoring Local detection Individual vehicles Network-wide Realtime Travel times Intensities Speed Queues OD-matrix Emissions 14

15 Smart Traffic Forecasting +5 minutes Expected Travel times Intensities Speed Queues OD-matrix Emissions 15

16 Smart Traffic Control Proactive traffic light control Predictive Adaptive Flexible, no fixed sequence Policy-dependend optimisation sg 2 sg 11 sg 5 sg 2 sg 8 sg 8 sg 11 now time +2 minutes sg 5 16

17 Time to green: 20 sec Smart Traffic Advice Time to red: 15 sec Sg 2 Sg 5 Sg 8 Sg 11 now time + 2 minutes Optimal speed: 30 km/h 17

18 DEMONSTRATION S 18

19 Demonstration 1 Smart Traffic Monitoring N201, Hoofddorp 8 km provincial road 6 traffic light controllers 4 travel time sections in both directions Travel times measured with camera s 19

20 Demonstration 1 Smart Traffic Monitoring Realtime simulation model (input TLC-data) Very good match in travel times Travel time camers s Smart Traffic Monitoring 20

21 Demonstration 2 prototype Smart Traffic Control 21

22 CONCLUSIONS 22

23 Added Value for Traffic Managers - operations 1. Proactive TM 2. Beter throughput 3. Free Flow & Less emissions 4. Adaptive & automated TM 5. Lower lifecycle costs 6. No Blackbox 23

24 Added Value for Traffic Managers - policy 1. Priorities based on policy Bike priority with less disruption PT priority with less disruption 2. Basis for policy analysis 3. Ready for C-ITS and automated mobility 4. No privacy issues 24

25 So Virtual Cooperative Predictive & Proactive & Automated Short term Return on Investments Lower costs Extension of life current systems Less loops Less functional management ( verkeerskundig beheer ) Closing the GAP Available now 25

26 Contact For questions, please contact: Bas van der Bijl Researcher Smart Mobility Gerbrand Klijn Manager Product Development 26

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