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A Unique Approach for Arterial Bandwidth Optimization

A Unique Approach for Arterial Bandwidth Optimization. Presenter: Md. Arafat Hossain Khan Advisor: Dr. Zong Tian Civil and Environmental Engineering University of Nevada, Reno. Outline. Why Bandwidth Optimization Research Goal Background Proposed Algorithm with Case Studies Analysis.

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A Unique Approach for Arterial Bandwidth Optimization

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  1. A Unique Approach for Arterial Bandwidth Optimization Presenter: Md. Arafat Hossain Khan Advisor: Dr. ZongTian Civil and Environmental Engineering University of Nevada, Reno

  2. Outline • Why Bandwidth Optimization • Research Goal • Background • Proposed Algorithm with Case Studies • Analysis

  3. Time-Space Diagram One-way Street Y=5 G=25 R=30 #1 2 8 2 8 4 4 6 6 EB Band G=35 Y=5 R=20 #2 2 2 8 4 8 4 6 6 Time

  4. Time-Space Diagram Two-way Street #1 Y=5 G=25 R=30 2 8 2 8 4 4 6 6 EB Band WB Band G=35 Y=5 R=20 2 2 #2 8 4 8 4 6 6 Time

  5. Why Bandwidth Optimization • Minimizes • Fuel consumption • Pollution emission • Stops • Queue length • Arrival of platoons at red lights • Maximizes • Smooth flow • Capacity • Driver’s satisfaction

  6. Research Goal • The goal of the research is to optimize • Phasing Sequence • Offset • Arterial Partition First two factors are most important and visible.

  7. Bandwidth and Offset Y=5 G=25 R=30 #1 2 8 2 8 4 4 6 6 EB: 15 sec WB: 18 sec G=35 Y=5 R=20 #2 2 2 8 4 8 4 6 6 Time

  8. Bandwidth and Offset Y=5 G=25 R=30 #1 2 8 2 8 4 4 6 6 EB: 15 sec WB: 18 sec G=35 Y=5 R=20 #2 2 2 8 4 8 4 6 6 Time

  9. Bandwidth and Offset Y=5 G=25 R=30 #1 2 8 2 8 4 4 6 6 WB: 18 sec EB: 30 sec G=35 Y=5 R=20 #2 2 2 8 4 8 4 6 6 Time

  10. Bandwidth and Offset Y=5 G=25 R=30 #1 2 8 2 8 4 4 6 6 WB: 30 sec EB: 30 sec G=35 Y=5 R=20 #2 2 2 8 4 8 4 6 6 Time

  11. Bandwidth and Phase Sequence(Dual Leading) 1 1 2 2 #1 8 4 6 5 6 5 WB: 8 sec EB: 20 sec #2 2 8 4 8 4 6

  12. Bandwidth and Phase Sequence(Dual Leading- Offset Adjustment) 1 1 2 2 #1 8 4 6 5 6 5 WB: 12 sec EB: 20 sec #2 2 8 4 8 4 6

  13. Bandwidth and Phase Sequence(Lead-Lag) 1 1 2 2 #1 8 4 6 5 6 5 WB: 12 sec EB: 20 sec #2 2 8 4 8 4 6

  14. Bandwidth and Phase Sequence(Lead-Lag – Offset Optimization) 1 1 2 2 #1 8 4 6 5 6 5 WB: 20 sec EB: 20 sec #2 2 8 4 8 4 6

  15. Background • W. D. Brooks, “Vehicular Traffic Control: Designing Traffic Progression Using A Digital Computer”,1965. • Equal bandwidth requires a tradeoff between attainability and bandwidth • C. J. Messer, R. N. Whitson, C. L. Dudek, and E. J. Romano. “A Variable-Sequence Multiphase Progression Optimization Program”. 1973 • Obtaining the maximum bandwidth for one direction while ensuring partial bandwidth for the other direction

  16. Background (Cont…) • ZongTianand Thomas Urbanik, “System Partition Technique to Improve Signal Coordination and Traffic Progression”, 2007 • Heuristic approach • Yi Zhao, ZongTian, “Phasing Sequence and Signal Spacing Based Progression Bandwidth Optimization Technique”, 2012 • Reformulation of Messer’s Algorithm

  17. Background (Cont…) • Wu Xianyu, Hu Peifeng and Yuan Zhenzhou, “Link-Based Signalized Arterial Progression Optimization with Practical Travel Speed”, 2013 • Improved Messer’s Algorithm • Optimal coordinated signal timing plan with both optimal link bandwidth and optimal arterial bandwidth

  18. Proposed Algorithm – Main Idea • Optimize each two intersections and proceed thereby for the whole arterial. • For a very large number of intersections the method had the capability to do partition based on any predefined objective function (e.g. Attainability) • Possible to hand calculate the whole arterial using simple geometry.

  19. Assumptions – Proposed Method • Vehicle speed is constant • Phase time is constant – Not actuated • No Trasition

  20. Methodology with Case Study

  21. Step – 1 : Optimize the phase sequence of first intersection

  22. Step – 1 (Cont…) • Lag – Lead is better

  23. Step – 1 (Cont…) • Dual Lead is better

  24. Step – 1 (Cont…)

  25. Step – 1 (Cont…)

  26. Step – 2 : Offset and Phase Sequence of Second Intersection • Adjust phase sequence according to the phase sequence of the first intersection • Offset calculation is constrained under an ‘objective function’ • Equal bandwidth • Priority based bandwidth • AM, PM peak hour priority • Arterial Priority [ In our case – equal Bandwidth was considered ]

  27. Step – 2 : Objective Function • Arguments • Band projection after optimizing the phase sequence of the first intersection • Phase time of the second intersection • Return • The ratio of the inbound and outbound bandwidth • The offset is calculated based on the return (ratio)

  28. Step – 2 (Cont…)

  29. Step – 2 (Cont…)

  30. Step – 3 : Optimize Phase Sequence of Intersection #3

  31. Step – 3 : Optimize Offset of Intersection #3

  32. Step – 4 : Optimizing Phase Sequence and Offset - other Intersections • Repeat Step 3 for the next intersection • Continue until all the phase sequences and offsets of all the intersections are optimized

  33. Optimizing Intersection # 4

  34. Optimizing Intersection # 4

  35. Optimizing Intersection # 5

  36. Optimizing Intersection # 5 Eureka 100% attainability 

  37. Computational Complexity • The method undergoes no iteration or trial and error method to determine the optimum phasing sequence and offset • For partition the method still does not require any iteration Extremely Fast

  38. Synchro Optimization Let Synchro optimize the arterial for us

  39. Synchro Vs Proposed Method • Faster (Using low level programming – not yet proved) • Better (Already proved)

  40. Close View of the Optimized Graph from Synchro and MATLAB

  41. MATLAB

  42. Synchro

  43. Problems and Solutions Problem: After optimizing the attainability may fall to a very low value. • Large number of intersections • Relationship between signal timing and intersection distances Solution: Partition Technique • Maintain the link bandwidth between the last intersection of the nth partition and the first intersection of the (n+1)th partition

  44. Objective function for Partition • Distance • Travel time • Attainability • Combination of these When the objective function falls below a ‘threshold’ the partition is done

  45. Network Partition • Based on ‘Priority Matrix’ – Segmented Arterials • Proposed method cannot ensure good network progression

  46. Some Supplementary Results • For fixed travel speed, optimum phase sequence shows an oscillating behavior with the distance between two intersections. • Offset carries the information of inbound and outbound priority

  47. Conclusion • The method is • Extremely fast • Flexible to set inbound and outbound priority • Flexible to incorporate partition very easily • The method does not • Deal with delay • Ensure network bandwidth

  48. Acknowledgement Proper direction of my supervisor Dr. ZongTian

  49. Thank You !!Questions?

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