1. Aaron B Wilson, EIT WesTech Engineering, Inc., SLC, UT Mitsuru Saito, PhD, PE Brigham Young University, Provo, UT ITE Western District Annual Meeting Santa Barbara, CA June 24 – June 27, 2012 1

2. Presentation Outline Background Purpose and Objectives Site Selection Data Collection Messages Shown on VMSs Statistical Analysis Defining Slow Traffic During Analysis Results Conclusion and Recommendations 2

3. Background More people, more traffic and need for improvement, meaning many work zones on the highways.  Technical and non-technical approaches have been taken. ITS application to work zone queue mitigation: Many studies performed on variable speed limit (VSL) signs but not many studies on VASS. First detailed analysis on VASS: Study of VSL effectiveness by Kwon et al. (2007). They used a small warning sign to display recommended speeds. No study has been done on a VASS system that uses regular-sized VMSs to convey advisory speeds to the drivers 3 Example of variable message sign on study site (Kwon et al. 2007).

4. Purpose and Objectives The purpose of this project was to evaluate the effectiveness of a Variable Advisory Speed System (VASS) in work zones to see if it would help mitigate queues. The three objectives of this study were: Investigate the possible VASS systems to be implemented in Utah Select a VASS and test it in a long term work zone Conduct a statistical analysis to evaluate the effectiveness of the selected VASS 4

5. Site Selection 5 Work zone used for the study was a segment of I-15: a widening project between 600 N and 2300 N interchanges on I-15 in Salt Lake City, to add an HOV lane in each direction. (about 3 miles) 5 Microwave sensors (orange dots) and 2 VMSs (green dots), provided by ASTI Transportation Systems. (www.asti-trans.com)

6. Site Characteristics 6

7. Data Collection Before data were collected starting March 30 th 2010 VMSs were turned on from April 27 to June 14 to collect after data. Weather data was collected by BYU Data collected by sensors were made available using an ftp site 7

8. Messages Shown on VMSs 8 XX MPH TRAFFIC AHEAD 55 MPH TRAFFIC AHEAD STOPPED TRAFFIC AHEAD 15 to 55 mph with a 5-mph increment (eg. 42 mph  40 mph) Location of VMS 1 Location of VMS 2

9. Statistical Analysis Null hypothesis: No difference between before (with VMS off) and after (with VMS on) data in mitigating queues or improving traffic flows Alternative hypothesis: VASS reduced queues and improved traffic flow characteristics in the work zone 9

10. Statistical Analysis (cont.) Initial analysis plan Use volume (throughputs) to evaluate effectiveness using flow rate and a comparison of before and after data (if volume is higher, then more throughput, and less chance of queue formation) Evaluate interaction of weather with work zone Evaluate the same situations in both before and after data 10

11. Statistical Analysis (cont.) Actual analysis  Surrogate factors were used to evaluate effectiveness of the VASS due to inconsistencies in volume data Mean speed (if higher, smoother flow, less chance of queue forming) 15 th percentile speed (if higher, closer to mean, less variation, smoother flow) 85 th percentile speed (if higher, closer to speed limit, smoother flow) Variance in speed (if smaller, less variation, smoother flow, less crash potential)  Weather data was also inconsistent and weather interactions were not analyzed 11 A sample congestion scene taken by a camera of UDOT at the entry to the work zone on NB I-15, near sensor 4.

12. Statistical Analysis (cont.) Actual analysis (continued)  All factors were evaluated during the evening peak hours (3:00 PM –7:00 PM)  Day Group Mondays Fridays Weekends (Sat – Sun) Workdays (Tue – Thu)  With or without slowdowns 12

13. Statistical Analysis (cont.):Defining Slow Traffic 13 Slowdown defined as traffic speeds falling below 50 mph for more than 30 minutes

14. Initial data analysis did not yield convincing results for some combinations of parameters, (e.g. there were fewer slowdowns seen in the before data compared to the number of slowdowns seen in the after data).  Before data sample sizes were smaller than after data  Weather factor –it was not feasible to perform meaningful means tests because there weren’t enough days with bad weather Eventually the following factors were analyzed with respect to the surrogate parameters for the evening peaks Sensor location Existence of slow down Day Group: Mondays, Workdays, Fridays, Weekends Time of day (evening peak) 14 Statistical Analysis (cont.)

15. Mean Speed at Evening Peak 15

16. Variance at Evening Peak 16

17. Conclusion When VASS was on, statistical significance of the difference in mean speeds and variances was seen during the weekend when slowdowns were present. When VASS was on, speeds were closer to the speed limit of 55 mph near the entry to the active work zone. Speed in the work zone approach seemed to be more stabilized. “After” speed variances were relatively smaller than “before” speed variances while there were slow downs at all sensors, indicating less variation in speeds and therefore providing smoother flow and contributing to queue mitigation. 17

18. Recommendations Equipment installation and configuration may take time; hence, short term work zones may not be a good candidate for implementing a large scale VASS. VASS is meant for a long-term work zone. Traffic studies should be performed before implementing VASS to ensure that queues are expected to form regularly to get maximum benefits from VASS. VASS showed some level of effectiveness for the work zone studied; however, additional studies are recommended to further evaluate the effectiveness of VASS in different work zone layouts. 18

19. 19 Thank you! Any questions? A 2-page handout is available.