1. 2010 Fall Transportation Conference A Guideline for Choosing Cycle Length to Maximize Two-Way Progression in Downtown Area Saeedeh Farivar Zong Tian University of Nevada, Reno June 2012

2. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Outline  Background and Problem Statement  Research Objective  Analysis Method  Results  Summary and Conclusion 2

3. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Background and Problem Statement  Cycle length is one of the important signal timing parameters in determining the optimal solution of coordinated traffic signal control.  Cycle is constrained by a number of factors such as traffic volume, intersection spacing, travel speed, and pedestrian crossing time.  In general in downtown areas traffic volume is not a governing factor and travel time and pedestrian crossing time play more important roles.

4. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Background and Problem Statement  Henry’s Study: for uniformly-spaced intersections and when traffic demand is balanced in both direction, 2, 4, or 6 times of travel time btw intersections would provide a good progression bandwidth in both direction  Oregon DOT study: a relationship btw cycle length, signal spacing and speed to maximize progression efficiency (2 times of travel time)

5. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Background and Problem Statement The Relationship Between Cycle Length and Max Bandwidth Time Space TijTji i j Cycle Length Optimum Cycle Length= Tij+ Tji Tij= Tji=TT Optimum Cycle Length=2 *TT

6. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Background and Problem Statement  The intersection spacing in downtown areas is generally short (e.g. 200 to 400 ft)  A min green time is required to serve pedestrians, therefore there is a min cycle length Two times of travel time is a small value so that is NOT feasible to be considered as the Cycle Length

7. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Research Objectives a) Developing a guideline for choosing the best cycle length that provides the best two-way progression in downtown areas with respect to various signal spacing: Uniformly and Randomly b) Analyzing the impact of intersection spacing and number of signals on progression bandwidth

8. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Analysis Method  Messer’s Algorithm- a volume independent model that starts with signals with LT phases.  A specific case of Messer’s method with only 2-phase:

9. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Max Bandwidth-Min Interference Upper Interference Lower Interference GmGm C C GmGm Gj I uj I lj mmjj

10. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Study Assumptions Multiple scenarios were generated assuming:  Number of signals: 2-20  Travel time btw signals: 7 to 50 sec  Minor street phase split (necessary to serve pedestrians): With considering 2 lanes in each direction, 5 sec walking time, and 3.5 ft/sec walking speed:

11. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Study Assumptions  Major Street phase split : C-20  Cycle length (C): 45 to 120 sec with an increment of 5sec  Bandwidth attainability (A) as for MOE:  The same green time for all intersections (min A=0.5 means one way progression bandwidth)

12. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Results- Optimum Cycle Length Uniformly-spaced intersections 22

13. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Results- Optimum Cycle Length Uniformly-spaced intersections- Small travel times 11 22 y = 3.939 x

14. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Results- Bandwidth Attainability Number of Signals Travel Time (sec)2345678910 70.930.50 80.920.50 90.910.50 100.50 11 0.54 0.52 0.50 12 0.580.570.53 0.50 13 0.620.590.570.540.530.50 14 0.660.60 0.59 0.57 0.56 15 0.700.63 160.740.630.62 0.61 0.60 0.59 170.780.640.62 0.60 0.58 0.56 180.820.64 0.62 0.60 0.58 190.860.720.65 0.64 0.63 200.900.800.67 210.940.880.820.760.700.65 0.64

15. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Results- Optimum Cycle Length Randomly-spaced intersections 22

16. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Results- Optimum Cycle Length Randomly-spaced intersections- Small travel times 22

17. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Results- Bandwidth Attainability

18. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 The impact of intersection spacing and number of signals on bandwidth attainability

19. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Summary and Conclusions Uniformly-spaced intersections Randomly-spaced intersections

20. C.A.T.E.R Center for Advanced Transportation Education and Research ITE Santa Barbara 2012 Summary and Conclusions Uniformly-spaced intersections provides more effective bandwidth progression especially when travel time btw intersections increase. Uniformly-spaced intersections provides more effective bandwidth progression especially with large number of signals (more than 7 signals). Less bandwidth attainability with more number of signals When the average travel time btw intersection is less than 15 sec, increase of cycle length does not improve bandwidth attainability significantly.

21. Thank You