1. Stochastic Optimization Method for Coordinated Actuated Traffic Control May 16, 2008 Joyoung Lee and Byungkyu “Brian” Park, Ph.D Presented at VISSIM UGM Philadelphia, PA

2. Outline Research Purpose Past Research Controller Setting Optimization Practical Implementation Conclusions and Recommendations

3. Purpose  Apply a stochastic optimization method (SOM) to an arterial network in Northern Virginia  Improve the SOM Performance by VISSIM and Shuffled Frog Leaping Algorithm (SFLA)  Quantify the benefits of SOM via before-and-after study Research Purpose

4. Past Research Calibration / Validation Field vs. Uncalibrated Model Field vs. Calibrated Model Park, B. And Won, J. (2006)

5. Controller settings optimization Traffic signal controller settings to be optimized  Cycle length  Green splits  Offsets  Phase sequence  Recall mode GA + CORSIM GA + VISSIM

6. Controller settings optimization It works! Yun, I., and Park, B.(2008)

7. Controller setting optimization However, it creates a huge search space.  Total parameters per intersection (standard NEMA 8 phases) =Offset + 8×MaxGreen+8×MinGreen+4×MajorPhaseSequence +4XMinorPhaseSequence+8XAmber+8XRed+8XVehicleExt. +8XOtherDectorSettings….  Offset is a killer. The combination of offsets for a corridor with N intersections =(CycleLength-1)^(N-1) It requires the exponential amount of computation time as the number of parameters linearly increases.

8. Practical Implementation An opportunity  Virginia Northern Region Operation Project A 6-mile long Coordinated-Actuated Corridor with 16 Intersections Apply SOM and Obtain optimal plans for  Mid-day  PM(in progress) Field Implementation by SOM optimal plans Benefit assessment by Before/After study

9. Study Corridor Lee Jackson Mem. Hwy & Rugby Rd. John Mosby Hwy. & Pleasant Valley Rd.

10. Network Calibration Efforts Data Collection  Volume Data Video recording on the Entry/Exit Points of Route 50  Travel Time Data Probe vehicles with a GPS receiver  Existing Signal Plan Data Virginia Northern Region Operation Calibration  Latin Hypercube Sampling 200 Samples 5 replications for each sample 16 target parameters

11. Network Calibration Mid-day Westbound

12. Network Calibration Mid-day Eastbound

13. Optimization Efforts Distributed Computing Environment  Master-Slave type DCE  a VISSIM simulation / a Processor  Distributed.NET Remoting Master Slave Processor#1 Processor#2

14. Optimization Efforts Genetic Algorithm  Famous  Challenging for huge search space cases. Shuffled Frog Leaping Algorithm (SFLA)  A heuristic algorithm based on an evolutionary algorithm  Perform both local search and global search

15. Optimization Efforts Genetic Algorithm Optimal(684.2) The best(=693)

16. Optimization Efforts Shuffled Frog Leaping Algorithm (SFLA)

17. Traffic Signal Settings Optimization Parameters Optimized  Cycle Length  Offsets  Minimum & maximum green times  Yellow and Red  Extension time  Phase Sequences  Lock Modes (Red Lock, Yellow Lock)  Recall Modes( Vehicle Recall, Max Recall)

18. Optimization Results Target Parameters and Field Constraints Fixed Parameters - Cycle Length(MD:150sec, PM:200sec) - Yellow and Red time (5~7 sec) Target Parameters - Offset - Min Green - Max Green - Extension time

19. Optimization Results VISSIM Evaluation Scenarios  FieldSetting : Current field controller setting.  SYNCHRO : SYNCHRO’s optimal setting.  Optimized : SOM’s optimal setting free from field constraints

20. Optimization Results Midday Optimization

21. Optimization Results PM-Peak Optimization

22. Optimization Results Total Travel Time (Veh-hours) ScenarioMeanSt.Dev.p-value Midday Field734.021.81 0.000 Optimized708.516.27 Gain(%)25.5 (3.5%)- PM-Peak Field1549.038.4 0.000 Optimized1517.028.6 Gain(%)32 (2.1%)-

23. Conclusions and Recommendations Conclusions  SOM improves. Field settings  3.5% of Mid-Day total travel time (Vehicle-Hours)  2.1% of PM-Peak total travel time (Vehicle-Hours) SYNCHRO  7.5% of Mid-Day  8.8% of PM-Peak  SFLA seems working. Reduce total evaluations Obtain better solution than GA

24. Conclusions and Recommendations Recommendations  Calibration/validation process should be embedded in SOM.  SOM can be applicable for the signal plan of coordinated-actuated traffic control  Optimization on advanced settings should be employed.

25. Thank you Questions & Comments