1. Evaluating Robustness of Signal Timings for Conditions of Varying Traffic Flows 2013 Mid-Continent Transportation Research Symposium – August 16, 2013 – 9:30AM – 12:00PM Cameron Kergaye, PhD, PE, PMP Director of Research Utah Department of Transportation

2. Traffic Signal Optimization  Should signal timings be optimized for high- than-average traffic counts?  How should signal timing optimization accommodate multiple counts?

3. Summary  Signal timing plans perform best based on average traffic flows  mean, mode, and median  when exposed to day-to-day traffic flow variability  Optimizing signal timings for higher traffic demand is better than for lower traffic demand  Should be used only when sufficient traffic data are unavailable.

4. Introduction  Optimization of signal timings is considered to be one of the most effective tools to improve traffic operations on urban arterials.  However, once traffic signal systems are retimed and implemented, quality of their performance largely depends on day-to-day variability of traffic flows in the field.  As soon as traffic patterns change significantly, performance of the signal timings deteriorates.  Current signal timing practice recommends development of separate signal timing plans for major day-to-day traffic patterns (weekday, weekend, special events, etc.).

5. Introduction  When adaptive traffic control is implemented it is impractical to develop plans for every traffic pattern that warrants a separate signal timing plan.  Therefore, it is important to develop signal timing plans that will minimize disbenefits of implementing signal timing plans in variable traffic conditions.

6. Research Background  Signal timing plans are based on traffic data that are usually collected during a short term effort (e.g. 1-week).  The data includes 24-hour weekly volume profiles, turning movement counts, vehicular speeds, and travel time runs.  The data is analyzed with other sources to ensure that they are representative of common field conditions.

7. Research Background  What is a representative traffic volume pattern?  It is the one that generates signal timings that work best in a variety of traffic conditions.  Finding such a traffic volume pattern and optimizing signal timings in field experiments requires significant resources.  Therefore, we use traffic simulation and other methods that do not require field experiments.

8. Research Contribution  Weekday PM-Peak hour traffic flows from the field are modeled in microsimulation for a year.  Two factors made this modeling approach possible:  Comprehensive set of field traffic flows collected during an entire year  Special tool to validate and balance traffic flows for the model.  Signal timing plans are optimized for each of the representative traffic flows resembling the process that usually occurs in practice.  Each of the signal timing plans is evaluated for the entire set of traffic flows to determine the best representative set of traffic flows.

9. Study Area Park City, UT 14 Intersections Long Corridor and Small Business District

10. Validation Results – Southbound

11. Validation Results – Northbound

12. Methodology Accurately model day-to-day traffic variations in microsimulation Collect and Process Field Traffic Volumes Traffic Volumes Recorded by SCATS Traffic Volumes Collected by Automatic Traffic Recorders Manually Collected Traffic Volumes Verify SCATS Traffic Volumes Build, Calibrate, and Validate the VISSIM Model Model Variability of Traffic Flows in VISSIM Prepare for Modeling Variability of Traffic Flows in Microsimulation Verify the reasonableness of SCATS traffic volumes Balance traffic flows in the network Verify VISSIM Traffic Flows Develop signal timing plans based on traffic flows of representative days Select scenarios of ‘representative-day traffic volumes’ Optimize signal timings in VISGAOST Evaluate Optimized Signal Timings in VISSIM

13. Scats Output with Traffic Volumes

14. Variation of Traffic Flows

15. Balancing Traffic Flows

16. Peak-Hour ATR & Scats Data

17. Verifying Reasonableness of Scats Traffic Volumes

18. Verifying Match of Scats & Vissim Traffic Flows

19. Optimizing Signal Timings In Visgaost VISSIMVISGAOST Signal Timings Performance Measures

20. “Representative Days” of Traffic Volumes AVERAGE MAX MEDIAN MIN MODE 75 th PERCENTILE 85 th PERCENTILE

21. Reduction of PI During Visgaost Optimizations

22. Variations in Performance Indices

23. Performances Based on Various ‘Representative-Days’

24. Conclusions Signal timings optimized for median traffic flows were best. Similar results were found for other average traffic flows (i.e. mean and mode). Findings show that signal timings developed for traffic flows that most frequently occur in the field bring more benefits than those that are developed for less frequent but higher traffic flows. Basing signal timings on higher-than-average traffic demand still generates better results than those developed for lower traffic flows. This is justified where there is a shortage of reliable traffic flow data from the field and when demand is expected to grow significantly.