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,

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, 23rd September 2015, Chania, Greece

Walid Fourati, TRANSVER GmbHUgnius Aliubavicius, TRANSVER GmbH

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 SYSTEMSD) FROM ANALYZING TO CONTROLLING BEHAVIOUR OF SYSTEM OF SYSTEMS (SOS)

Local GlobalGeneral InformationFor information regarding this Project: Check the Project Web-Site: http://local4global-fp7.eu

Participants

1 CERTH - Centre for Research and Technology

2 ETHZ – Eidgenössische Technische Hochschule Zürich

3 RWTH – RWTH Aachen University

4 IK4 – IK4 TEKNIKER

5 TRV – TRANSVER GmbH

6 TUC – Technical University of Crete

7 TUM – Technische Universtität Muenchen

4

http://local4global-fp7.eu/

• 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 CaseOverview

Use Case ArchitectureIK4

TUM Road Admin.

Central Traffic Computer

L4G Web Service

App server

Field data collection

DPM

L4GCAO

Constituent System IConstituent System II

Input

TUC

Signal plans

TUC Measurts Data

Urban Traffic Control

Output

Queue Length Estimation

Raw Traffic DataData Collector

CS1 Controller

CS2 Controller

Simulation EnvironmentPTV 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

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 driverUndergoing:- Intensive simulation and impact assessment through multiple runs and

statistical analysisWhat could be done next- Simulate the real functioning (time laps between decision and application

and other practical constraints)- Compare centralized Vs distributed control

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.

Simulation scenarios

PART Scenarios Number:Tuning

C2X TUC INPUT Runs per demandHistorical data Number

of nCost

Criterion

A Signal control strategy

1 - - - - Network-c1.tuc 10

2 - - - - Network-c2.tuc 10

B Cost criterion of optimisation

3 - - Performance - Network-c2.tuc 20

4 - - Productivity - Network-c2.tuc 20

C

History data of speed

recommendation (L4GCAO)

5 Occupancy 6 Productivity 10% Network-c2.tuc 20

6 Speed 6 Productivity 10% Network-c2.tuc 20

7(dynamic QLE) Queue length 6 Productivity 10% Network-c2.tuc 20

Number of n (L4GCAO) 8 Queue length 12 Productivity 10% Network-c2.tuc 20

DC2X 9 Queue length 6 Productivity 20% Network-c2.tuc 20

Congestion algorithm

10 (dynamic QLE) Queue length 6 Productivity 10% Network-c2.tuc 20

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.

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

PI main PI secondary PI total0

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3949

5

1179

04

6103

9

4088

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1151

66

6129

0

Demand II - Performance Index

NetworkC1 NetworkC2

PI [-

]

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.


• Total PI between two scenarios and in both demands are different by less than 1%• Other indices also almost identical.


22000

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2535

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Demand I - Performance Index

D1 Performance D1 Producitivity

PI [-

]


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D2 Performance D2 Producitivity

PI [-

]


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

-95-90-85-80-75-70-65-60-55 Productivity criterion in performance case

D1 Performance D2 Performance

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

-95000

-90000

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Productivity criterion in productivity case

D1 Productivity D2 Productivity

Part C. speed recommendation tuningparameters selection• Two parameters to define:

• Which historical data (speed, occupancy or queue length) as input to formula:

• Number of previous historical data values (n).

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.


25000

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2702

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2895

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2689

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2637

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4

2713

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Demand I- Performance Index

Speed Occupancy QLE QLE(n=12)

PI [-

]

Part C. Demand 1

355

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Demand I - travel time [s/veh](both directions)


trav

el ti

me

[s/v

eh]

Direction south Direction north

• 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%.

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.


20000

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60000

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100000

120000

4301

1

1077

59

6096

1

4242

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1075

55

6047

5

4309

3

1090

96

6139

7

4272

9

1087

43

6102

8



PI [-

]

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;

0

50

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385420

386 414389421

384418

Demand II - travel time [s/veh](both directions)


trav

el ti

me

[s/v

eh]

Direction south Direction north

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 7th, it will not be conducted.

Part D. Performance index

• Higher penetration rate resulted in almost 5% higher total performance index in demand 1 and 21% higher in demand 2.

Series124500

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Demand I- Performance Index

C2X10 C2X20

PI [-

]

PI main

PI sec-ondary

PI totalPI main PI secondary PI total

0

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40000

60000

80000

100000

120000

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4309

3

1090

96

6139

7

5895

4

1171

84

7432

1


C2X10 C2X20

PI [-

]

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.

350

360

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383

370

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378

Demand I - travel time [s/veh](both directions)

C2X10 C2X20tr

avel

tim

e [s

/veh

] Direction south

Direction north

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.

0

100

200

300

400

500

600

389 421403

527

Demand II - travel time [s/veh](both directions)

C2X10 C2X20

trav

el ti

me

[s/v

eh]

Overall results. Demand 1

PI total22000

23000

24000

25000

26000

27000

28000

29000

30000

Demand 1. Performance index

Basis* LG4 C2X00 LG4 C2X10* LG4 C2X10 FT(QLE,n=6)LG4 C2X10 FT(QLE,n=12) LG4 C2X20 FT(QLE,n=6)

• 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.

Overall results. Demand 2

PI total55000

60000

65000

70000

75000

80000

85000

Demand 2. Performance index

Basis* LG4 C2X00 LG4 C2X10* LG4 C2X10 FT(QLE,n=6)LG4 C2X10 FT(QLE,n=12) LG4 C2X20 FT(QLE,n=6)

• * - 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.

SYSTEM-OF-SYSTEMS THAT ACT LOCALLY FOR OPTIMIZING

GLOBALLY

THANK YOU FOR YOUR ATTENTION!

Traffic Use Case

Local4Global Consortium Meeting, 23rd September 2015, Chania, Greece

Walid Fourati, TRANSVER GmbHUgnius Aliubavicius, TRANSVER GmbH

Documents

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,