1. SYSTEM-OF-SYSTEMS THAT ACT LOCALLY FOR OPTIMIZING GLOBALLY EU FP7 - SMALL/MEDIUM-SCALE FOCUSED RESEARCH PROJECT (STREP) FP7-ICT-2013.3.4: ADVANCED COMPUTING, EMBEDDED AND CONTROL SYSTEMS D) FROM ANALYZING TO CONTROLLING BEHAVIOUR OF SYSTEM OF SYSTEMS (SOS) Use Case 2 Simulation work description and outcomes Local4Global Consortium Meeting, 23 rd September 2015, Chania, Greece Walid Fourati, TRANSVER GmbH Ugnius Aliubavicius, TRANSVER GmbH

2. Project Acronym: Local4Global Project Number: 611538 Project Start Date: October 2013 Duration: 3 Years Funded by: EU FP7 Program Name: EU FP7 - SMALL/MEDIUM-SCALE FOCUSED RESEARCH PROJECT (STREP) FP7-ICT-2013.3.4: ADVANCED COMPUTING, EMBEDDED AND CONTROL SYSTEMS D) FROM ANALYZING TO CONTROLLING BEHAVIOUR OF SYSTEM OF SYSTEMS (SOS) Local Global General Information For information regarding this Project: Check the Project Web-Site: http://local4global-fp7.euhttp://local4global-fp7.eu Participants 1CERTH - Centre for Research and Technology 2ETHZ – Eidgenössische Technische Hochschule Zürich 3RWTH – RWTH Aachen University 4IK4 – IK4 TEKNIKER 5TRV – TRANSVER GmbH 6TUC – Technical University of Crete 7TUM – Technische Universtität Muenchen 4

3. Section length: approx. 5 km 2 lanes per driving direction 7 signalized intersections 2 types of constituent systems Junction controllers: signal control to be optimized based on vehicle flow Cooperative vehicles: vehicle flow to be optimized by influencing cruise speed Test bed Munich, Germany Traffic Use Case Overview

4. Use Case Architecture

5. Simulation Environment PTV VISSIM 5.40 cooperative vehicles signal control systems VBA Application via VISSIM COM API Local4Global Control Strategy Shared Folder Flow and occupancy Signal plan control  selection of most suitable signal plan  definition of correction parameters for speed recommendation  Global optimization through these 2 influences Python Application via VISSIM C2X API Speed and position Recommended speed Evaluation  Performance Indicator  Mean Network Speed  Number of stops  Waiting time  Travel Time traffic flow information signal plan selection per intersection Speed correction parameters Queue Length Estimator

6. Simulation Study, Phase II What has been done:  Integration and calibration of imaginary detectors  Extension of signal library plans – from 15 to 45 for each intersection  Integration of Constituent System II optimization through localized correction of speed recommendation  Implementation of dynamic queue length estimation to improve speed recommendation accuracy and credibility to the driver Undergoing:  Intensive simulation and impact assessment through multiple runs and statistical analysis What could be done next  Simulate the real functioning (time laps between decision and application and other practical constraints)  Compare centralized Vs distributed control

7. Simulation Testing Overview Four simulations parts - A, B, C and D; Experience in simulations influence next simulation scenarios - progress leads closer to the optimal set of parameters; It is assumed that TUC and L4GCAO algorithms will have the highest impact on the final results, thus it is tested firstly; Mobility impact assessment is conducted taking into account performance index, waiting time, mean network speed and travel time.

8. Simulation scenarios PARTScenariosNumber: Tuning C2XTUC INPUT Runs per demand Historical data Number of n Cost Criterion A Signal control strategy 1----Network-c1.tuc10 2----Network-c2.tuc10 B Cost criterion of optimisation 3--Performance-Network-c2.tuc20 4--Productivity-Network-c2.tuc20 C History data of speed recommendation (L4GCAO) 5Occupancy6Productivity10%Network-c2.tuc20 6Speed6Productivity10%Network-c2.tuc20 7(dynamic QLE) Queue length6Productivity10%Network-c2.tuc20 Number of n (L4GCAO) 8Queue length12Productivity10%Network-c2.tuc20 D C2X9Queue length6Productivity20%Network-c2.tuc20 Congestion algorithm 10 (dynamic QLE) Queue length6Productivity10%Network-c2.tuc20

9. Part A. Input file selection Two options: Input files, which consider only a part of the links’ lengths and capacities (file NetworkC1); Input files, which consider the full length and capacity of all links (file NetworkC2). In case both of them indicate no negative effects, new set of files will be used further, since it provides better speed estimations for the L4GCAO algorithm when applied in the speed recommendation application.

10. Part A. Input file selection The total performance index of NetworkC2 is higher only by 0.4% Other differences between indices like travel time are not larger than 3% NetworkC2 scenario indicate no negative effects and it could be used further

11. Part B. Cost Criterion Selection Two options: Tuning of the signal control parameters with the speed as the cost criterion; Tuning of the signal control parameters with the productivity (speed and demand) as the cost criterion. This part is also needed in order to find out required number of fine-tuning runs.

12. Part B. Cost Criterion Selection Total PI between two scenarios and in both demands are different by less than 1% Other indices also almost identical.

13. Part B. Cost Criterion Selection

14. Part C. speed recommendation tuning parameters selection

15. Part C. Demand 1 PI total of QLE case is lower by 0.9% comparing to speed case; After selecting queue length as a parameter for further simulations, it was also compared with scenario 8 (double n); Higher n value results with about 1% higher performance index in total.

16. Part C. Demand 1 Occupancy case show longer travel time by 7 seconds in direction south and at least by 1 second in direction north. Mean network speed is 59.7 km/h in scenario with queue length as historical data and 59.6 km/h in other scenarios, Best travel time results is part B simulations in direction south was longer by almost 5% and in direction north – longer by 6%.

17. Part C. Demand 2 Differences between cases are not higher than 2%; PI total value was lower by 4% in part B simulations and by 12% higher as same scenario in the previous master thesis.

18. Part C. Demand 2 Travel time deviations between scenarios are not higher than 2%; Mean network speed was about 34.3 km/h in all cases;

19. Part D Two parameters studied: Penetration rate of cooperative vehicles are increased from 10% to 20%; Static queue length algorithm is changed to dynamic. Since last scenario is the same as the 7 th, it will not be conducted.

20. Part D. Performance index Higher penetration rate resulted in almost 5% higher total performance index in demand 1 and 21% higher in demand 2.

21. Part D. Demand1 Travel time increased by 5% in direction south and by 2% in direction north; Mean network speed decreased from 59.7km/h to 59.3km/h.

22. Part D. Demand 2 Travel time increased by 4% in direction south and by 25% in direction north; Mean network speed decreased by 14% – from 34,2km/h to 29,5km/h.

23. Overall results. Demand 1 Basis - Fixed Signal Plan + C2X10; * - results from previous master thesis (from Julia). L4G C2X00 – no C2X, tuning for signal controllers only, FT(QLE) – fine tuning for signal controllers and speed recommendations, qle – queue length as historical data, n – number of historical data Fixed signal plan is still showing best performance for D1. Introduction of C2X vehicles decrease performance (as well as higher penetration rate), nevertheless results are better than it was in previous master thesis.

24. Overall results. Demand 2 * - results from previous master thesis. Basis - Fixed Signal Plan + C2X10; L4G C2X00 – no C2X, tuning for signal controllers only, FT(QLE) – fine tuning for signal controllers and speed recommendations, qle – queue length as historical data, n – number of historical data Initial scenario (Fixed signal plan) is showing worst performance in terms of total PI. Introduction of C2X vehicles decrease performance (as well as higher penetration rate), nevertheless results are better than it was in previous master thesis.

25. SYSTEM-OF-SYSTEMS THAT ACT LOCALLY FOR OPTIMIZING GLOBALLY THANK YOU FOR YOUR ATTENTION! Traffic Use Case Local4Global Consortium Meeting, 23 rd September 2015, Chania, Greece Walid Fourati, TRANSVER GmbH Ugnius Aliubavicius, TRANSVER GmbH