1. 1 Optimization of ALINEA Ramp-metering Control Using Genetic Algorithm with Micro-simulation Lianyu Chu and Xu Yang California PATH ATMS Center University of California, Irvine

2. 2 Overview Background: ALINEA Genetic Algorithm Optimization Framework Simulation Modeling Optimization Study Conclusion Remarks

3. 3 Background ALINEA, proposed by Papageorgiou in 1990s A local feedback ramp-metering strategy Remarkably simple, highly efficient and easily implemented Good performance – Field tests – Simulation-based studies Potential applications

4. 4 Background: ALINEA

5. 5 Parameter values in field tests: – Desired occupancy O* : 0.18 -- 0.31 – K R =70, in real-world experiments – Downstream detector location: 40 m -- 500 m downstream – Update cycle  t: 40 seconds -- 5 minutes

6. 6 Background: Purposes How to optimize ALINEA’s operational parameters in order to maximize its performance? Method: -> Hybrid method: simulation + GA

7. 7 Genetic Algorithm Mimic the the mechanics of natural selection and evolution Proven to be a useful method for optimization Useful when there are too many parameters to be considered

8. 8 Optimization Framework

9. 9 Simulation Modeling Study site

10. 10 Simulation Modeling Model Calibration

11. 11 Optimization Study MOE: Total vehicle travel time (TVTT) N i,j : total number of vehicles that actually traveled between origin i and destination j D i,j : travel demand from origin i to destination j for the whole simulation time (D i,j is not equal to N i,j because of the randomness of the micro-simulation) T k i,j : travel time of the kth vehicle that traveled from origin i to destination j

12. 12 Optimization Study Setup the range of calibrated parameters for ALINEA

13. 13 Optimization Study The best, worst and average fitness values of each generation

14. 14 Optimization Study The results of optimized ALINEA parameters

15. 15 Findings When the regulator K R, used for adjusting the constant disturbances of the feedback control, is within the range from 70 to 200, the metering system is found to perform well. The optimal location of the downstream detector is found to be between 120~140 meters downstream of the on-ramp nose in our simulation study.

16. 16 Findings The update cycle of the metering rate implementation gives the best system performance when it ranges from 30 to 60 seconds in our study. The desired occupancy of the downstream detector station is found to be within two ranges, either from 19% to 21% or around 30% to 31%. Finally, 19% to 21% is selected for its better network reliability performance.

17. 17 Findings

18. 18 Conclusions This paper presents a hybrid GA-simulation method to find the optimized parameter values for the ALINEA control. This method is effective to find the optimized parameter values. Practitioners can use our optimization results as a basic operational reference if they implement ALINEA control in the real world.

19. 19 Conclusions This study shows that micro-simulation can be used to calibrate and optimize the operational parameters of ramp metering control. Potentially, micro-simulation may also be used to fine-tune parameters for various other ITS strategies.

20. 20 Thank you