A Guideline for Choosing Cycle Length to Maximize Two-Way Progression in Downtown Area

1. 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. Outline • Background and Problem Statement • Research Objective • Analysis Method • Results • Summary and Conclusion

3. 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 andpedestrian crossing time play more important roles.

4. 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. Background and Problem Statement The Relationship Between Cycle Length and Max Bandwidth j Space i Tij Tji Cycle Length Time Optimum Cycle Length= Tij+ Tji Tij= Tji=TT Optimum Cycle Length=2 *TT

6. 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 What would be the best cycle length to operate signal system when travel time is small?

7. 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 andnumber of signals on progression bandwidth

8. 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. Max Bandwidth-Min Interference I uj I lj Gj Gj C C Gm Gm m j m j Upper Interference Lower Interference

10. 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. 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. Results- Optimum Cycle Length Uniformly-spaced intersections 22

13. Results- Optimum Cycle Length Uniformly-spaced intersections- Small travel times y = 3.939 x 11 22

14. Results- Bandwidth Attainability

15. Results- Optimum Cycle Length Randomly-spaced intersections 22

16. Results- Optimum Cycle Length Randomly-spaced intersections- Small travel times 22

17. Results- Bandwidth Attainability

18. The impact of intersection spacing and number of signals on bandwidth attainability

19. Summary and Conclusions Uniformly-spaced intersections Randomly-spaced intersections

20. Summary and Conclusions • When the average travel time btw intersection is less than 15 sec, increase of cycle length does not improve bandwidth attainability significantly. • 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

21. Thank You