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Arterial Bandwidth Optimization Methods for Traffic Progression

Explore innovative techniques to optimize bandwidth and offset for smooth traffic flow, minimizing fuel consumption and improving driver satisfaction. Research delves into an effective algorithm with case studies and time-space diagrams.

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Arterial Bandwidth Optimization Methods for Traffic Progression

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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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