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Performance Evaluation of Adaptive Ramp Metering Algorithms in PARAMICS Simulation

Performance Evaluation of Adaptive Ramp Metering Algorithms in PARAMICS Simulation. Lianyu Chu , Henry X. Liu, Will Recker California PATH, UC Irvine H. Michael Zhang Department of Civil and Environmental Engineering, UC Davis. Presentation Outline. Introduction Methodologies

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Performance Evaluation of Adaptive Ramp Metering Algorithms in PARAMICS Simulation

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  1. Performance Evaluation of Adaptive Ramp Metering Algorithms in PARAMICS Simulation Lianyu Chu, Henry X. Liu, Will Recker California PATH, UC Irvine H. Michael Zhang Department of Civil and Environmental Engineering, UC Davis

  2. Presentation Outline • Introduction • Methodologies • Evaluation study • Calibration & Validation • Ramp metering algorithms • Evaluation results • Conclusions

  3. Background • California PATH program Project • Objective • Evaluating ramp-metering algorithms in a micro-simulation environment

  4. IntroductionCategories of ramp-metering control • Fixed-time • Local traffic responsive • ALINEA • Coordinated traffic responsive • BOTTLENECK • ZONE

  5. Methodologies • Choosing an ITS-capable model (PARAMICS, VISSIM, AIMSUM2,…) • Developing ATMIS modules • Good calibration of studied network • Development, design, calibration and optimization of ramp-metering algorithms • Performance evaluation under different scenarios

  6. Methodologies Micro-simulator PARAMICS • Scalable, high-performance microscopic traffic simulation package • ITS-capable • API programming => Capability enhancement through API development

  7. Methodologies API development: A Hierarchical Approach Provided API Library Signal Ramp ATMIS Modules Routing Demand Developed API Library CORBA Data Handling Databases Adaptive Signal Control Advanced Algorithms Adaptive Ramp Metering Dynamic Network Loading

  8. MethodologiesEvaluation framework

  9. Evaluation studystudy site

  10. Evaluation study Network coding in PARAMICS

  11. Evaluation study Model calibration • Accurate Network Geometry • Vehicle characteristics & Performance • The proportion of vehicle types • Driving restrictions • The signposting setting for links • Driver behavior factors in car-following and lane-changing models

  12. Evaluation study Model Validation (volume-occupancy) Loop station @ 3.04 Real world Simulation

  13. Evaluation study Model Validation (volume comparison)

  14. Evaluation study ALINEA • maintaining a optimal occupancy on the downstream mainline freeway • Calibration: • KR = 70 • Odesired = 20% • Location: 60 m

  15. Evaluation study BOTTLENECK • System level metering rate • Occupancy at Downstream > Desired occupancy • Vehicle storage in the section • Local level metering rate:Occupancy control • Calibration: - Area of influence of each section - Weighting factor of each on-ramp

  16. Evaluation study ZONE • System level metering rate: volume control • Local level metering rate:Occupancy control • Calibration • Identify bottleneck, divide the network into zones • 6-level metering plan for each entrance ramp

  17. Evaluation study Assumptions & experimental designs • Override strategy. • Metering rate restriction • No diversion • Same occupancy control calibration used in BOTTLENECK and ZONE. • 15 simulation runs for each scenario • Compared with fixed-time control

  18. Evaluation StudyPerformance measures • Total vehicle travel time (TVTT) • Average mainline travel time (AMTT) • Total mainline delay (TMD) • Total on-ramp delay (waiting time) (TOD)

  19. Evaluation studyScenarios • Morning peak hour (6:30-10:00) • highly congestion • lower congestion • Incidents: block the rightmost lane for 10 minute • at the beginning of congestion • at the end of congestion

  20. Evaluation studyalgorithms to be evaluated • ALINEA • Traditional BOTTLENECK • Improved BOTTLENECK: replacing the local control strategy, i.e. occupancy control, with ALINEA control • ZONE • Improved ZONE

  21. Evaluation studyTotal vehicle travel time

  22. Evaluation studyAverage mainline travel time

  23. Evaluation studyTotal mainline delay

  24. Evaluation studyTotal on-ramp delay

  25. Evaluation results • All algorithms can be used for improving freeway congestion. • ALINEA shows very good performance under all scenarios. • The two coordinated ramp-metering algorithms, i.e., BOTTLENECK and ZONE, are a little more efficient than ALINEA under normal conditions. • Compared with ZONE, BOTTLENECK can identify a bottleneck dynamically. • Coordinated algorithms can be improved by integrating a better local algorithm, such as the ALINEA algorithm .

  26. Conclusions • A capability-enhanced micro-simulation laboratory has been developed for evaluating ramp-metering algorithms, potentially, some ATMIS applications. • Adaptive ramp-metering algorithms can ameliorate freeway traffic congestion effectively. • Compared with local algorithm, coordinated algorithms are more efficient, but the improvement is limited.

  27. More Information • PATH reports: http://www.path.berkeley.edu • Liu, X., Chu, L., and Recker, W. PARAMICS API Design Document for Actuated Signal, Signal Coordination and Ramp Control, California PATH Working Paper, UCB-ITS-PWP-2001-11 • Zhang, H. M., Kim, T., Nie, X., Jin, W., Chu, L. and Recker, W. Evaluation of On-ramp Control Algorithm, California PATH Research Report, UCB-ITS-PRR-2001-36.

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