Field Evaluation of DetectionControl System
CHAPTER 4. DATA ANALYSIS RESULTS
This chapter begins with descriptions of data collection sites because site details might help explain some of the findings. These details include drawings of the intersection geometry and tables of controller settings for the before and after conditions. The analysis of the actual crash data came near the end of the project. The goal of the traffic data collection was to determine propensity for running, vehicles caught in the dilemma zone, and maxouts. The Upper Limit Study used similar measures of effectiveness as studies 1 and 3. The following four studies were introduced in chapter1 and explained in chapter 2:
 Study 1: Performance Monitoring of Dilemma Zone Occupancy.
 Study 2: BeforeAfter Crash Data Study.
 Study 3: BeforeAfter Crash Surrogate Study.
 Study 4: Upper Limit Study.
This chapter contains detailed site information on each of the eight sites to assist in better understanding the results. There is a site map showing geographic information followed by site schematics on each site to present the necessary details for understanding the geometric layout of the intersections and other detectors that were used for the before detection scenario. There is signal timing information on each site in both the before and after conditions. There is discussion about site, detection, and controller features that might have affected the outcome of the beforeafter comparison. The order of the site information in the remainder of this chapter is alphabetical. Florida (sites 1, 2, and 3) is first, followed by Illinois (sites 4 and 5), Louisiana (site6), and Texas (sites 7 and 8).
U.S. 27 is a northsouth arterial that lies along the western edge of the city of Fort Lauderdale. The area to the west of U.S. 27 is mostly undeveloped swamps, and to the immediate east is urbanized residential development. Figure 4 shows a map of the local area showing these three intersections. The Pines intersection is a “T” intersection, whereas Griffin and Johnson are fourway intersections. All three intersections have two through lanes on the highspeed U.S. 27 approaches, and all have single leftturn and single rightturn lanes near the intersection. All three intersections have wide medians as shown in figure 5 through figure 7.
Original image: ©2009 Google® Tele Atlas; map annotations provided by TTI.
Figure 4. Map. Fort Lauderdale DCS sites.^{(4)}
Figure 5. Map. Intersection layout at U.S. 27 and Griffin Rd.
Figure 6. Map. Intersection layout at U.S. 27 and Johnson Rd.
Figure 7. Map. Intersection layout at U.S. 27 and Pines Blvd.
Table 13 through table 18 summarize the controller settings for the three Florida intersections. Figure 8 and figure 9 show the signal phasing sequence for the Pines and Johnson intersections. The Griffin intersection had controller settings that were reasonably straightforward and did not use overlaps like the other two Florida intersections did. The signal phasing at the Pines intersection was an issue because DCS needs the green phases on the main street approaches (NEMA phases 2 and 6) to begin simultaneously. BCT officials agreed to change the signal timing to cause these two phases to begin simultaneously and to eliminate one of the overlaps used previously. The other needed element was that the minimum green setting needed to conform to the DCS recommended minimum of 15 s for approach speed limits of 55 mi/h or higher.^{(1)} If there is no stopline detection, the minimum green time must be at least sufficient to clear the stopped queue. There was no dilemmazone detection in the before condition; the only detection used was stopline detection.
The following definitions apply to table 13 through table 18 and other discussions of controller settings.
The following definitions apply to the before DCS phase:
 MinGrn (minimum green, or initial green): The shortest possible vehicle green time before any added initial or vehicle extensions.^{(5)}
 Passage (passage time, vehicle extension): When minimum green finishes timing, the green interval is allowed to extend for a length of time equal to maximum time in effect. Actual length of extension period depends on this phase vehicle extension time, frequency of vehicle actuations, and minimum gap setting.^{(5)}
 Max1 and Max2: Maximum green time allowed in the presence of an opposing call. The highernumbered maximum green selected will be in effect.^{(5)}
 Yel (yellow change interval): the time that the phase yellow indication is displayed following a green indication.^{(5)}
 Red Clearance Interval (or AllRed Interval): The interval at the end of the yellow change interval during which the phase has a redsignal display before the display of green for the following phase. Its purpose is to allow vehicles that entered the intersection on the yellow change interval to clear the intersection prior to the next phase.^{(6)}
The following definitions apply to the after DCS phase:^{(1,7)}
 TrapDist: TrapDist is the distance from the downstream end of the detector trap to the stop line of the intersection, in feet. The traps should be between 700 and 1,000 ft from the stop line.
 DZArrive: DZArrive is the travel time from the upstream end of the dilemma zone to the stop line, in seconds. The DZArrive time cannot be smaller than DZExit.
 DZExit: DZExit is the travel time from the downstream end of the dilemma zone to the stop line, in seconds. The DZExit time cannot be larger than DZArrival.
 Stage: The maximum green time is divided into two stages, stage 1 and stage 2. The Stage is the percentage of the maximum green time that is allocated to stage 1. Phase termination in stage 1 requires that all dilemma zones are clear. During stage 2, DCS searches for a time when the number of vehicles in the dilemma zone is at a minimum.
 MaxSpeed (maximum speed): MaxSpeed is the maximum acceptable travel speed to be used by DCS in mi/h. Speeds detected higher than this value are considered to be errors and are set to the maximum speed.
 MaxLength (maximum length): MaxLength is the maximum acceptable vehicle length for DCS, in feet. Vehicle lengths reported to DCS that are longer than the maximum length are considered to be errors, and the maximum length is reported instead.
 ZoneLength: ZoneLength is the measurement between the exit end of the upstream inductive loop and the exit end of the downstream inductive loop, in feet. The minimum zone length is 20 ft, although longer distances are also allowed.
Table 13. Controller settings U.S. 27/Griffin Rd.—before.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

MinGrn (s) 
— 
25 
— 
6 
— 
25 
— 
6 
Passage (s) 
— 
2.5 
— 
2 
— 
2.5 
— 
2 
Max1 (s) 
— 
70 
— 
30 
— 
70 
— 
30 
Max2 (s) 
— 
0 
— 
0 
— 
0 
— 
0 
Yel (s) 
— 
5 
— 
4 
— 
5 
— 
4 
Red Clearance (s) 
— 
3 
— 
2 
— 
3 
— 
2 
—No data.
Table 14. Controller settings U.S. 27/Griffin Rd.—after DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

TrapDist (ft) 
— 
790 
— 
— 
— 
800 
— 
— 
DZArrive (s) 
— 
6 
— 
— 
— 
6 
— 
— 
DZExit (s) 
— 
2 
— 
— 
— 
2 
— 
— 
Stage (percent) 
— 
65 
— 
— 
— 
65 
— 
— 
MaxSpeed (mi/h) 
— 
70 
— 
— 
— 
70 
— 
— 
MaxLength (ft) 
— 
75 
— 
— 
— 
75 
— 
— 
ZoneLength (ft) 
— 
20 
— 
— 
— 
20 
— 
— 
—No data.
Another issue in Florida was the maximum green setting in the Naztec 2070 controller after initiating the DCS algorithm. The recommended range is 55 to 80 s, but the setting at Pines during field data collection was 50 s.^{(1)} TTI did not check the settings because Naztec had been onsite to provide training and should have set up the controller properly. This error was discovered too late to change it during the field data collection at the Pines intersection. However, TTI checked the other intersections and set the value to 70 s at Griffin and Johnson. Even though the setting at Pines was lower than the recommended range, the result might still indicate how well it operates under these conditions.
Table 15. Controller settings for U.S. 27/Johnson Rd.—before DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

MinGrn (s) 
6 
7 
5 
6 
5 
— 
— 
— 
Passage (s) 
2 
0 
2 
2 
2 
— 
— 
— 
Max1 (s) 
25 
70 
18 
25 
12 
— 
— 
— 
Max2 (s) 
25 
50 
18 
30 
12 
— 
— 
— 
Yel (s) 
4 
5 
4 
4 
4 
— 
— 
— 
Red Clearance (s) 
2 
3 
2 
2 
2 
— 
— 
— 
—No data.
Table 16. Controller settings for U.S. 27/Johnson Dr.—after DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

TrapDist (ft) 
— 
790 
— 
— 
— 
800 
— 
— 
DZArrive (s) 
— 
6 
— 
— 
— 
6 
— 
— 
DZExit (s) 
— 
2 
— 
— 
— 
2 
— 
— 
Stage (percent) 
— 
65 
— 
— 
— 
65 
— 
— 
MaxSpeed (mi/h) 
— 
70 
— 
— 
— 
70 
— 
— 
MaxLength (ft) 
— 
75 
— 
— 
— 
75 
— 
— 
ZoneLength (ft) 
— 
20 
— 
— 
— 
20 
— 
— 
—No data.
Table 17. Controller settings for U.S. 27/Pines Blvd.—before DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

MinGrn (s) 
5 
20 
5 
10 
— 
— 
— 
— 
Passage (s) 
0 
3 
2 
2 
— 
— 
— 
— 
Max1 (s) 
5 
50 
20 
35 
— 
— 
— 
— 
Max2 (s) 
0 
0 
0 
0 
— 
— 
— 
— 
Yel (s) 
4 
5 
4 
4 
— 
— 
— 
— 
Red Clearance (s) 
2 
3 
2 
2 
— 
— 
— 
— 
—No data.
Table 18. Controller settings for U.S. 27/Pines Blvd.—after DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

TrapDist (ft) 
— 
800 
— 
— 
— 
815 
— 
— 
DZArrive (s) 
— 
6 
— 
— 
— 
6 
— 
— 
DZExit (s) 
— 
2 
— 
— 
— 
2 
— 
— 
Stage (percent) 
— 
65 
— 
— 
— 
65 
— 
— 
MaxSpeed (mi/h) 
— 
70 
— 
— 
— 
70 
— 
— 
MaxLength (ft) 
— 
75 
— 
— 
— 
75 
— 
— 
ZoneLength (ft) 
— 
20 
— 
— 
— 
20 
— 
— 
—No data.
Figure 8. Chart. Phase sequence for U.S. 27/Pines Blvd.
Figure 9. Chart. Phase sequence for U.S. 27/Johnson St.
Illinois data collection followed Florida. The two Illinois sites were located east of Peoria on U.S. 24—one at the intersection with Cummings Lane and the other at the intersection with Main Street near Washington, IL. Figure 10 shows an area map indicating the location of the two Illinois sites. The two intersections had almost identical geometry, with two through lanes on each highspeed approach and single lane leftturn bays on each highspeed approach. Figure 11 and figure 12 show the intersection details. Table 19 through table 22 provide controller settings.
Original image: ©Google® Map Data 2009 Tele Atlas; map annotations provided by TTI.
Figure 10. Map. Washington, IL, DCS sites.^{(8)}
Challenges to Data Collection
IDOT used a single 6ft by 6ft inductive loop located about 5 s travel time upstream of the intersection for dilemmazone detection at the Cummings and Main intersections. These loops were all still operational for collecting the before data. The distances from the DCS loops to the stop line were 1,000 ft in all cases. Vehicular speeds at these sites adhered closely to the speed limit of 55 mi/h. Traffic at these intersections appeared to be primarily commuter traffic, with a pronounced peak in the morning and afternoon periods.
One of the challenges to the before data collection at the Illinois sites was loss of power, resulting in loss of some controller settings. Upon restoration of power, IDOT personnel noticed the problem with controller settings and worked with TTI personnel to get the appropriate settings reloaded. IDOT apparently set a relatively high value for passage time in the before condition to ensure clearance of the stopped queue, even though the only detector was 330 ft from the stop line. A question arose in trying to reset the passage time regarding how much time would be appropriate. TTI research personnel convinced IDOT to reduce it to 5 s.
Figure 11. Map. Intersection layout at U.S. 24 and Cummings Ln.
Figure 12. Map. Intersection layout at U.S. 24 and Main St.
Table 19. Controller settings for U.S. 24/Cummings Ln.—before DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

MinGrn (s) 
4 
15 
— 
10 
4 
15 
— 
10 
Passage (s) 
2 
6 
— 
2 
2 
6 
— 
25 
Max1 (s) 
20 
65 
— 
25 
20 
65 
— 
2 
Max2 (s) 
25 
60 
— 
30 
25 
60 
— 
30 
Yel (s) 
3.5 
5 
— 
4.5 
3.5 
5 
— 
4.5 
Red Clearance (s) 
1.5 
1.7 
— 
2.2 
1.5 
1.7 
— 
2.2 
—No data.
Table 20. Controller settings for U.S. 24/Cummings Ln.—after DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

TrapDist (ft) 
— 
1,000 
— 
— 
— 
1,000 
—  — 
DZArrive (s) 
— 
6 
— 
— 
— 
6 
—  — 
DZExit (s) 
— 
2 
— 
— 
— 
2 
—  — 
Stage (percent) 
— 
75 
— 
— 
— 
75 
—  — 
MaxSpeed (mi/h) 
— 
70 
— 
— 
— 
70 
—  — 
MaxLength (ft) 
— 
75 
— 
— 
— 
75 
—  — 
ZoneLength (ft) 
— 
20.3 
— 
— 
— 
20.2 
—  — 
—No data.
Table 21. Controller settings for U.S. 24/Main St.—before DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

MinGrn (s) 
4 
15 
— 
10 
4 
15 
— 
10 
Passage (s) 
2 
6 
— 
2 
2 
6 
— 
25 
Max1 (s) 
20 
65 
— 
25 
20 
65 
— 
2 
Max2 (s) 
25 
60 
— 
30 
25 
60 
— 
30 
Yel (s) 
3.5 
5 
— 
4.5 
3.5 
5 
— 
4.5 
Red Clearance (s) 
1.5 
1.7 
— 
2.2 
1.5 
1.7 
— 
2.2 
—No data.
Table 22. Controller settings for U.S. 24/Main St.—after DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

TrapDist (ft) 
— 
1,000 
— 
— 
— 
1,000 
—  — 
DZArrive (s) 
— 
6 
— 
— 
— 
6 
—  — 
DZExit (s) 
— 
2 
— 
— 
— 
2 
—  — 
Stage (percent) 
— 
75 
— 
— 
— 
75 
—  — 
MaxSpeed (mi/h) 
— 
70 
— 
— 
— 
70 
—  — 
MaxLength (ft) 
— 
75 
— 
— 
— 
75 
—  — 
ZoneLength (ft) 
— 
20.3 
— 
— 
— 
20.2 
—  — 
—No data.
Figure 13 shows an area map indicating the location of the Louisiana site, which is the intersection of LA 3235 and LA 3162. The location is near the small town of Galliano, LA, in the Lafourche Parish, about 60 mi south of New Orleans. LA 3235 is a highspeed roadway with speed limits on each approach at 55 mi/h, although observed local traffic speeds were higher. The DCS approaches have two through lanes, a leftturn lane for the southbound approach, and a rightturn lane for the northbound approach. Three of the intersection legs serve general purpose traffic, and the fourth leg (eastbound) serves a casino and a convenience store. The only before detection at this intersection was at the stop line, so DCS should significantly improve the safety of the intersection. Figure 14 shows the geometric layout of the intersection, indicating the location of detectors used for field data collection. Table 23 and table 24 provide controller settings for the before and after conditions.
A challenge at this site was an apparent conflict in the cabinet between the serial ports on the PC running the TTI software and the extra unused BIUs being turned on. After turning these extra BIUs off and operating with only the needed BIUs, the TTI data collection system ran normally. Solving this problem required an additional trip by one TTI person to meet Naztec personnel at the site. A second challenge was being able to monitor side street demand to determine how many legitimate maxouts occurred. In the before data collection, LaDOTD had set the main street phases to maximum recall (i.e., the main street phases were maxing out during each cycle of the day in the before data collection). LaDOTD was using an Autoscope™ minihub connected to the controller bus, which precluded the TTI equipment from monitoring the side street detectors in the after data collection. Therefore, determining the number of maxouts was not possible. However, the intersection functioned as it did prior to the installation of the DCS equipment, which was important in determining beforeafter differences.
Original image: ©Google® Map Data 2009 Tele Atlas; map annotation provided by TTI.
Figure 13. Map. Louisiana DCS site.^{(9)}
Figure 14. Map. Intersection layout at LA 3235 and LA 3162.
Table 23. Controller settings for LA 3235/LA 3162—before DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

MinGrn (s) 
6 
15 
5 
10 
5 
15 
5 
10 
Passage (s) 
1.5 
2 
1 
1.5 
1 
2 
1 
1.5 
Max1 (s) 
15 
75 
25 
25 
25 
75 
25 
35 
Max2 (s) 
15 
75 
50 
25 
50 
75 
50 
35 
Yel (s) 
5.8 
5.8 
3.5 
4.3 
3.5 
5.8 
3.5 
4.3 
Red Clearance (s) 
1 
1 
1.5 
1.2 
1.5 
1 
1.5 
1.2 
Table 24. Controller settings for LA 3235/LA 3162—after DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

TrapDist (ft) 
— 
1,000 
— 
— 
— 
1,000 
—  — 
DZArrive (s) 
— 
6 
— 
— 
— 
6 
—  — 
DZExit (s) 
— 
2 
— 
— 
— 
2 
—  — 
Stage (percent) 
— 
75 
— 
— 
— 
75 
—  — 
MaxSpeed (mi/h) 
— 
75 
— 
— 
— 
75 
—  — 
MaxLength (ft) 
— 
80 
— 
— 
— 
80 
—  — 
ZoneLength (ft) 
— 
20.5 
— 
— 
— 
20.5 
—  — 
—No data.
U.S. 281 at E. Borgfeld Drive in San Antonio
Figure 15 shows an area map indicating the location of the San Antonio site. The location is an isolated intersection north of the urbanized area surrounding San Antonio. Peak periods at this site indicated that a significant portion of the traffic was commuter traffic, with heavy inbound movement in the morning hours and heavy outbound movement during the late afternoon hours. This traffic was the heaviest of any of the DCS data collection sites used in this project and was useful for conducting the Upper Limit Study.
Figure 16 shows the geometric layout of the intersection. The threeway intersection has two through lanes on the U.S. 281 approaches, while Borgfeld Drive has one lane in each direction away from the intersection. At the intersection, the northbound and eastbound approaches have leftturn bays. The U.S. 281 approaches had a series of seven inductive loops (each loop crossing both lanes) for dilemmazone protection with distances from the stop line of 48, 93, 157, 239, 321, 425, and 534 ft. Table 25 through table 27 provide the controller settings for the original Eagle controller, the Naztec 2070 controller with the before settings, and the Naztec 2070 controller with the DCS settings, respectively. Naztec representatives were supposed to enter settings in their controller (table 26) to replicate the operation of the original Eagle controller (table 25).
Original image: ©Google® Map Data 2009 Tele Atlas; map annotation provided by TTI.
Figure 15. Map. San Antonio, TX, DCS site.^{(10)}
Figure 16. Map. Intersection layout at U.S. 281/E. Borgfeld Dr.
Table 25. Controller settings for U.S. 281/Borgfeld Dr.—before DCS, Eagle controller.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

MinGrn (s) 
6 
20 
— 
— 
— 
20 
— 
8 
Passage (s) 
5 
10 
— 
— 
— 
10 
— 
0 
Max1 (s) 
30 
55 
— 
— 
— 
55 
— 
30 
Max2 (s) 
30 
65 
— 
— 
— 
65 
— 
30 
Yel (s) 
5 
5.8 
— 
— 
— 
5.8 
— 
4.3 
Red Clearance (s) 
1.4 
1.9 
— 
— 
— 
1.9 
— 
1.8 
—No data.
Table 26. Controller settings for U.S. 281/Borgfeld Dr.—before DCS, Naztec controller.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

MinGrn (s) 
6 
20 
— 
— 
— 
20 
— 
8 
Passage (s) 
5 
10 
— 
— 
— 
10 
— 
0 
Max1 (s) 
30 
75 
— 
— 
— 
75 
— 
30 
Max2 (s) 
30 
65 
— 
— 
— 
65 
— 
30 
Yel (s) 
4.7 
5.8 
— 
— 
— 
5.8 
— 
4.3 
Red Clearance (s) 
1.6 
1.9 
— 
— 
1.9 
— 
1.8 
—No data.
Table 27. Controller settings for U.S. 281/Borgfeld Dr.—after DCS, Naztec controller.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

TrapDist (ft) 
— 
965 
— 
— 
— 
994 
—  — 
DZArrive (s) 
— 
6 
— 
— 
— 
6 
—  — 
DZExit (s) 
— 
2 
— 
— 
— 
2 
—  — 
Stage (percent) 
— 
60 
— 
— 
— 
60 
—  — 
MaxSpeed (mi/h) 
— 
70 
— 
— 
— 
70 
—  — 
MaxLength (ft) 
— 
65 
— 
— 
— 
65 
—  — 
ZoneLength (ft) 
— 
20 
— 
— 
— 
19 
—  — 
—No data.
An important aspect of this site involved using the Naztec 2070 controller with a nonNaztec (Eagle) cabinet. Six of the eight sites involved in this research had Naztec controllers and cabinets, so this site offered a good opportunity to test the compatibility of this controller with a different cabinet (the other nonNaztec controller was at the Waco site, described below). During the initial installation of the Naztec controller for beginning data collection about May 30, 2009, the intersection went into flash mode when a detector BIU was unplugged. This occurrence could have been coincidental with removal of the BIU, but Naztec replaced the MMU anyway and was able to restore normal operation following this change.
TTI completed its normal data collection during the selected before/after period, which ended on July 27, 2009, before another problem occurred related to lightning (according to district personnel). The lightning strike again caused the intersection to go into flash mode. The Naztec controller had operated the intersection successfully for the 8week period between the installation date of the Naztec controller and the lightning strike. As a temporary fix, district personnel replaced the Naztec controller with the original Eagle controller until Naztec could troubleshoot its 2070 controller. TxDOT shipped the controller back to Naztec during the week of August 17, 2009, to allow Naztec to troubleshoot the problem. Naztec found that the CPU board had been damaged; a technician from Naztec returned the repaired controller to the district on September 31, 2009. TTI reinstalled its monitoring equipment at the U.S.281/Borgfeld intersection on October 1, 2009, and began collecting data for the Upper Limit Study.
Upper Limit Study: TTI increased the maximum green setting in the DCS 2070 controller on November 3, 2009, at the intersection of U.S. 281/Borgfeld to test its upper limit. At this intersection, Naztec and TTI set an initial maximum green of 75 s for phases 2 and 6. After some consideration of adding 80 and 85 s, TTI selected 85 and 95 s as the desired additional values to test because of the increased statistical significance in the greater spread.
U.S. 84 at Speegleville Road in Waco
Figure 17 shows an area map indicating the location of the Waco site, which is at the intersection of U.S. 84 and Speegleville Road. The location is southwest of Waco and outside the urban area. U.S. 84 is a highspeed roadway with a speed limit of 60 mi/h on each approach and a significant number of trucks. The DCS approaches have two through lanes, single leftturn lanes, and single rightturn lanes. All four of the intersection legs serve general purpose traffic. Detection prior to installation of DCS being installed consisted of a series of inductive loops upstream for dilemmazone protection. Dilemmazone detectors were 6ft by 6ft loops in each lane at 493, 267, and 111ft from the stop line. Through lanes on U.S. 84 had no stopline detection, but leftturn bays did. Figure 18 shows the geometric layout of the intersection, indicating the location of detectors used for field data collection.
Original Image: ©Google® Map Data 2009 Tele Atlas; map annotation provided by TTI.
Figure 17. Map. Waco, TX, DCS site.^{(11)}
The DCS site in Waco had a NEMA TS1 cabinet, and City of Waco decisionmakers chose not to replace this cabinet with a TS2 cabinet to accommodate the DCS, even though they were convinced that DCS had made a significant difference in improving safety. On the first visit to this site, TTI researchers found that the PC running the DCS algorithm had failed, so they prepared a replacement PC to be installed in the cabinet. Other TTI researchers had already wired the cabinet for a PC system, reducing the effort required to reinstall DCS.
An issue that surfaced immediately following the reinstallation of DCS at this site was due to not having stopline detectors on through lanes. The City of Waco technicians had disconnected all previously installed dilemmazone loops so that only the DCS loops were connected for main street dilemmazone protection. The DCS algorithm does not start its search for gaps in the traffic stream until the termination of the minimum green in the controller. In this case, with the formation of long queues extending past the DCS loops, the algorithm did not detect gaps appropriately, so it terminated the green phase prematurely with each cycle. The solution involved reconnecting the nearest inductive loops (located 111 ft from the stop line) and allowing the queue to begin clearing and for DCS to function properly. With reconnection of the closest loops to the stop line, DCS was able to function properly. At the first gapout (or at the end of the minimum green setting, which was usually sooner), DCS took over and started looking for gaps to safely end the green phase. Table 28 and table 29 provide the controller settings for the U.S. 84/Speegleville Road intersection.
Figure 18. Map. Intersection layout at U.S. 84/Speegleville Rd.
Table 30 summarizes the speed limit and the dilemmazone detection type used before installation of DCS, stopline detection, and distance to DCS loops. Obviously, the best comparison of DCS was with systems that had at least reasonably adequate dilemmazone protection in the before condition. Therefore, the best comparisons are with Illinois and Texas sites.
Table 28. Controller settings for U.S. 84/Speegleville Rd.—before DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

MinGrn (s) 
4 
15 
— 
4 
3 
15 
4 
4 
Passage (s) 
30 
30 
— 
15 
15 
40 
30 
15 
Max1 (s) 
15 
70 
— 
15 
15 
70 
30 
15 
Max2 (s) 
30 
65 
— 
— 
— 
65 
— 
30 
Yel (s) 
4 
4.5 
— 
4.5 
4 
4.5 
4 
4.5 
Red Clearance (s) 
1 
2.5 
— 
2.5 
1 
2.5 
1 
2.5 
—No data.
Table 29. Controller settings for U.S. 84/Speegleville Rd.—after DCS.
Setting 
Phase 

1 
2 
3 
4 
5 
6 
7 
8 

TrapDist (ft) 
— 
969 
— 
— 
— 
976 
—  — 
DZArrive (s) 
— 
6 
— 
— 
— 
6 
—  — 
DZExit (s) 
— 
2 
— 
— 
— 
2 
—  — 
Stage (percent) 
— 
60 
— 
— 
— 
60 
—  — 
MaxSpeed (mi/h) 
— 
70 
— 
— 
— 
70 
—  — 
MaxLength (ft) 
— 
75 
— 
— 
— 
75 
—  — 
ZoneLength (ft) 
— 
20 
— 
— 
— 
19 
—  — 
—No data.
Table 30. Site summary information.
Site Description 
Speed Limit (mi/h) 
Dilemma Zone Detection Used During Before Period 
Stop Line Detection 
Distance to DCS Loops^{1} 
Phase 
U.S. 27/ Pines Blvd. 
55 (NB) 55 (SB) 
None 
Video 
800 ft (NB) 815 ft (SB) 
2 6 
U.S. 27/ Griffin Rd. 
55 (NB) 55 (SB) 
None 
Video 
790 ft (NB) 800 ft (SB) 
2 6 
U.S. 27/ Johnson St. 
55 (NB) 55 (SB) 
None 
Video 
790 ft (NB) 800 ft (SB) 
2 6 
U.S. 24/ Main St. 
55 (EB) 55 (WB) 
One loop/approach^{2} 
Video 
1,000 ft (EB) 1,000 ft (WB) 
2 6 
U.S. 24/ Cummings La. 
55 (EB) 55 (WB) 
One loop/approach 
Video 
1,000 ft (EB) 1,000 ft (WB) 
2 6 
LA 3162/ LA 3235 
55 (NB) 55 (SB) 
None 
Video 
1,000 ft (NB) 1,000 ft (SB) 
2 6 
U.S. 281/ E. Borgfeld Dr. 
65 (NB) 65 (SB) 
Multiple loops 7 sets 
Loops 
994 ft (NB) 965 ft (SB) 
6 2 
U.S. 84/ Speegleville Rd 
60 (EB) 60 (WB) 
Multiple loops 3 sets^{3} 
Loops 
969 ft (EB) 976 ft (WB) 
2 6 
^{1}Measured from stop line to trailing edge of exit loop.
^{2}Single 6ft by 6ft loop located at 405 ft (about 5s travel time) from stop line.
^{3}City of Waco used distances from the stop line of 111, 267, and 493 ft.
Most of the results presented in this section were generated by human observers watching replay of recorded video of vehicles at each of the eight intersections. The monitoring system in each case involved a video camera/processor system to detect RLRs and a second (redundant) system that monitored vehicles caught in the dilemma zone. At each site, the process defined the dilemma zone as a range in travel time from 2 to 6 s. The redundant monitoring system consisted of two components: 1) the inductive loop pairs used for the DCS, and 2) a Wavetronix™ Advance radar detector for each highspeed approach. The camera providing data to the video image processor also served as a surveillance camera to assist observers in verifying RLRs and vehicles in the dilemma zone. The inductive loops provided vehicle length and speed, and the radar detectors monitored speed and distance to each vehicle on the approach. TTI researchers were able to read the data stream from the radar detector’s serial port that updated speed and distance for each approaching vehicle every few milliseconds. This continuous stream of data provided enough information on approaching vehicles to serve as its own prediction of vehicles in the dilemma zone.
The following results are categorized according to the four studies. Studies 1 and 3 are similar, so results are combined into one section. Results from study 4, the Upper Limit Study, also come later in this section. Results from study 2 will come later in the final report at the end of the project because final crash data will not be available until that time.
Table 31 and table 32 provide summary statistics describing the variables in the beforeafter database. The data in each row of either table reflect about 1 h of data collection for one signal phase at the associated intersection location. The data in table 31 indicate that the study observed more than 1,300 signal cycles at six locations. During these cycles, 88 vehicles entered the intersection within 6 s following the change in signal indication from yellow to red. Collectively, the intersections had both very low and very high traffic flow rates (120 to 1,512vehicles/h). They also experienced a wide range in cycle length (57 to 127 s). These wide ranges added a desired breadth in the range of conditions represented in the database.
As a first step in the analysis of the data, analysts computed redlight violation rates for each intersection approach, resulting in computation of two rates. The first rate is expressed in terms of redlightrunning events per 1,000 vehicles. The second rate represents the number of redlightrunning events per 10,000 vehiclecycles, where “cycles” represents the average number of cycles per h during the period for which vehicles are counted. The use of “vehiclecycles” is based on previous research demonstrating that exposure for redlight violations should be based on the count of vehicles and the count of cycles.^{(3)} Table 32 includes both of these rates.
The overall average rates at the study locations are 5.3 redlight violations per 1,000vehicles and 1.1 redlight violations per 10,000 vehiclecycles. This number is the average for all sites where the rate in column 4 of table 32 is computed using total redlight running events divided by the total approach vehicles, and the rate in column 5 is computed using the equation in footnote C. The former rate is at the higher end of a range found in the literature (3.0 to 5.3 violations per 1,000 vehicles). Specifically, data reported by Kamyab et al. indicate an average rate of 3.0violations per 1,000vehicles.^{(6)} Data reported by Baguley indicate an average rate of 5.3violations per 1,000vehicles.^{(7)} Bonneson and Son reported 4.1 violations per 1,000 vehicles and 1.0 violations per 10,000 vehiclecycles.^{(3)}
^{ }Table 31. Beforeafter database summary—total observations.
Location 
Study Hour and Phase 
Study Period 
Cycles 
Flow Rate,^{1} vehicles/h 
Cycle Length^{2} 
Number of Vehicles in Dilemma Zone,^{1} Vehicles 
Number of MaxOuts^{1} 
Number of RedLight Violations,^{1} Vehicles 
U.S. 27/ 
17:00 Ph. 6 
Before 
56 
493 
65 
23 
0 
9 
After 
46 
475 
79 
7 
0 
1 

7:00 Ph. 6 
Before 
52 
560 
69 
26 
0 
8 

After 
43 
502 
84 
2 
1 
0 

U.S. 27/ 
8:00 Ph. 2 
Before 
40 
402 
89 
15 
4 
7 
After 
40 
324 
89 
3 
0 
0 

13:00 Ph. 2 
Before 
37 
401 
94 
17 
0 
9 

After 
39 
388 
89 
1 
0 
0 

U.S. 24/ 
10:00 Ph. 2 
Before 
62 
137 
58 
0 
0 
1 
After 
58 
120 
60 
0 
0 
0 

U.S. 24/ 
7:00 Ph. 6 
Before 
57 
696 
63 
5 
0 
3 
After 
54 
663 
66 
3 
0 
0 

8:10 Ph. 6 
Before 
63 
537 
57 
9 
0 
3 

After 
60 
577 
59 
0 
0 
0 

17:10 Ph. 2 
Before 
57 
534 
62 
— 
0 
2 

After 
49 
551 
72 
— 
1 
0 

16:00 Ph. 2 
Before 
57 
622 
63 
— 
0 
2 

After 
48 
617 
75 
— 
0 
1 

LA 3162/ 
14:20 Ph. 6 
Before 
31 
836 
113 
5 
— 
2 
After 
53 
845 
68 
9 
— 
1 

13:10 Ph. 6 
Before 
30 
361 
118 
9 
0 
3 

After 
54 
359 
66 
7 
0 
1 

U.S. 84/ 
7:00 Ph. 2 
Before 
34 
1465 
107 
52 
20 
13 
After 
28 
1512 
127 
13 
7 
4 

8:10 Ph. 2 
Before 
46 
582 
78 
— 
6 
3 

After 
41 
601 
89 
— 
0 
1 

16:30 Ph. 6 
Before 
41 
885 
91 
— 
0 
10 

After 
35 
896 
106 
— 
2 
4 

Total 
Before 
663 
8511 
81 
161 
30 
75 

After 
648 
8430 
81 
45 
11 
13 

Percent Change^{3} 
2.3 
1.0 
0.0 
72 
63 
83 

Total 
1,311 
16,941 
81 
206 
41 
88 
^{1}Flow rate and counts include both passenger cars and heavy vehicles.
^{2}Cycle length in the total rows represents an average length (not a sum).
^{3}Percent change = 100 x (after/before  1.0).
—Data not available.
Table 32. Beforeafter database summary—redlightrunning violation rates.
Location  Study Hour  Study Period  RLRs per 1,000 Vehicles  RLRs per 10,000 VehicleCycles 
U.S. 27/Griffin Rd. 
17:00 
Before 
18.4* 
3.3 
After 
2.1 
0.5 

7:00 
Before 
14.6 
2.7 

After 
0.0 
0.0 

U.S. 27/Johnson St. 
8:00 
Before 
17.9 
4.4* 
After 
0.0 
0.0 

13:00 
Before 
23.8* 
6.1* 

After 
0.0 
0.0 

U.S. 24/Main St. 
10:00 
Before 
7.4 
1.2 
After 
0.0 
0.0 

U.S. 24/Cummings Ln. 
7:00 
Before 
4.4 
0.8 
After 
0.0 
0.0 

8:10 
Before 
5.7 
0.9 

After 
0.0 
0.0 

17:10 
Before 
3.9 
0.7 

After 
0.0 
0.0 

16:00 
Before 
3.3 
0.6 

After 
1.7 
0.3 

LA 3162/LA 3235 
14:20 
Before 
2.5 
0.8 
After 
1.2 
0.2 

13:10 
Before 
8.8 
2.8 

After 
2.9 
0.5 

U.S. 84/Speegleville Rd. 
7:00 
Before 
9.0 
2.6 
After 
2.8 
0.9* 

8:10 
Before 
5.3 
1.1 

After 
1.7 
0.4 

16:30 
Before 
11.1 
2.8 

After 
4.5* 
1.3 

Overall Average Rates Based on Total Observations for All Sites 
Before 
9.0 
1.9 

After 
1.6 
0.3 

Percent Change^{1} 
82 
82 

Average^{2} 
5.3 
1.1 
^{1}Percent change = 100 x (after/before  1.0).
^{2}RLRs per 10,000 vehiclecycles = count of redlight violations x 10,000 x Σ study hours / (Σ vehicles x Σ cycles).
*Values exceed the average rate by a factor of 2.0 or more.
The redlight violation rates listed in table 32 provide some indication of the extent of redlight violations at the intersections studied. The vehiclebased rates listed in column 4 indicate that three locations exceeded the average rate for the corresponding study period by a factor of 2.0 or more. The rates listed in column 5 indicate that three locations exceeded the corresponding average rate by 2.0 or more. Clearly, there is some discrepancy regarding which locations are the most problematic. This discrepancy illustrates the importance of considering both volume and numberofcycles when computing the redlight violation rate for locationbased comparison or evaluation. The vehiclecyclebased rate logically represents a more reliable measure of the propensity for redlight violation than the vehiclebased rate because it accounts for two measures of exposure to a redlight violation.
Statistical Analysis Method
A preliminary examination of the data indicated that they are neither normally distributed nor of constant variance, as is assumed when using traditional leastsquares regression. Under these conditions, the generalized linear modeling technique is appropriate because it accommodates the explicit specification of an error distribution using maximumlikelihood methods for coefficient estimation.
The distribution of violation frequency can be described as negative binomial because there are two different sources of variability. One source of variability stems from the differences in the mean frequency m among the otherwise similar intersection approaches. The other source stems from the randomness in frequency at any given site, which follows the Poisson distribution. The variance of the negative binomial distribution is seen in figure 19:
Figure 19. Equation. Variance of distribution.
Where:
V(x) is the variance of the distribution.
x is the observed violation frequency for a given approach having an expected frequency of E(m) and dispersion parameter k.
Researchers used the GENMOD regression procedure in the statistical analysis system software to estimate the model coefficients.^{(12) }This procedure is often used to calibrate regression models using count data with a large amount of variability (e.g., crash frequency).
Model Calibration
The regression analysis indicated that relationships existed between redlight violation frequency and exposure (expressed as the ratio of flow rate to cycle length), location, and the type of detectionandcontrol system used. Findings indicate that the regression coefficient associated with each of these factors was significant at a level of confidence that exceeded 95 percent. As a result of this analysis, the linear regression terms were specified in the model using the formulation in figure 20.
Figure 20. Equation. Expected redlightrunning frequency.
Where:
E[R] = expected redlightrunning frequency, vehicles/h.
Q = approach flow rate, vehicles/h.
C = cycle length, s.
I_{Cummings} = indicator variable (= 1.0 for U.S. 24/Cummings Lane location; 0.0 otherwise).
I_{after} = indicator variable (= 1.0 for data from the after study period, 0.0 otherwise).
This analysis also applied the equation shown to model dilemmazone frequency and maxout frequency. In each case, the process substituted the corresponding dependent variable for E[R] in figure 20.
RedLight Violation Model Results
The regression analysis indicated that the calibrated model accounted for most of the variability in the data. The U.S.24/Cummings Lane location required one locationspecific indicator variable because this location experienced less than onehalf of the violations of the other locations, all other factors considered. The model variables explain the differences among the other locations.
Table 33 shows the statistics related to the calibrated redlightrunning model. The calibrated coefficient values can be used with figure 20 to predict the hourly redlightrunning frequency for a given intersection approach. The analysis found that a dispersion parameter k of 54.9 yielded a scaled Pearson χ^{2} statistic for the model is 25.7, and the degrees of freedom are 24 (= np1 = 2831). Because this statistic is less than χ^{2}_{0.05, 24} (= 36), the hypothesis that the model fits the data cannot be rejected. A measure of model fit that is appropriate for negative binomial error distributions is R_{K}^{2}, as developed by Miaou.^{(13)} The interpretation of this statistic is the same as for the coefficient of determination R^{2} (i.e., that values near 1.0 suggest a very good fit to the data). R_{K}^{2} for the calibrated model is 0.98.
Table 33. Calibrated redlight violation model statistical description.
Model Statistics 
Value 

RK^{2}: 
0.98 

Scaled Pearson χ^{2}: 
1.07 

Pearson χ^{2}: 
25.7 (χ20.05, 24 = 36) 

Dispersion Parameter k: 
54.9 

Observations: 
28 h 

Range of Model Variables 

Variable 
Variable Name 
Units 
Minimum 
Maximum 
Q 
Approach flow rate 
vehicles/h 
120 
1,512 
C 
Cycle length 
s 
57 
127 
Calibrated Coefficient Values 

Variable 
Definition 
Value 
Standard Deviation 
tstatistic 
b0 
Intercept 
7.895 
2.378 
3.3 
b1 
Effect of exposure 
0.970 
0.269 
3.6 
b2 
Effect of U.S. 24/Cummings Lane location 
1.374 
0.340 
4.0 
b3 
Effect of change in detection and control 
1.733 
0.309 
5.6 
The last rows of table 33 list the regression coefficients for the model. The tstatistic shown indicates that all coefficients are significant at a 95percent level of confidence or higher. A negative coefficient for b_{3} indicates that redlight violations were less frequent during the after period. Based on the model structure, this coefficient can be converted into an equivalent reduction percentage of 82 percent (= 100 [1  e^{−1.733}]). Thus, the analysis indicates that the after study periods experienced 82 percent fewer redlight violations than the before study periods. This reduction factor is consistent with the average change in violation rates shown at the bottom of table 32.
Researchers assessed the fit of the model through the graphical comparison of the observed and predicted redlightrunning frequencies as shown in figure 21. The trend line in this figure does not represent the line of best fit; rather, it is a “y = x” line. The data would fall on this line if the model predictions exactly equaled the observed data. The trends shown in this figure indicate that the model is able to predict the redlight violation frequency without bias.
Figure 21. Graph. Comparison of observed and predicted redlightrunning frequency.
Dilemma Zone Model Results
The model used for the dilemmazone frequency analysis was the same as used for the redlight violation model. The regression analysis indicated that the calibrated model accounted for most of the variability in the data. Again, one locationspecific indicator variable was needed to account for the U.S.24/Cummings Lane location. This location experienced less than onehalf of the dilemmazone count compared with the other locations, all other factors considered. The model variables explain differences among the other locations.
Table 34 shows the statistics related to the calibrated dilemma zone model. The calibrated coefficient values can be used with figure 20 to predict the hourly number of vehicles in the dilemma zone at yellow onset for a given intersection approach. A dispersion parameter k of 6.2 yielded a scaled The Pearson _{X}2 statistic for the model is 19.7 and the degrees of freedom are 20 (= np1 = 2431). Because this statistic is less than 2 0.05, 20 (= 26), the hypothesis that the model fits the data cannot be rejected. R_{K}^{2} for the calibrated model is 0.87. This value suggests that the model explains most of the variability in the data.
The last rows in table 34 show the regression coefficients for the model. The tstatistic shown indicates that all coefficients are significant at a 95percent level of confidence or higher. A negative coefficient for b_{3} indicates that there were fewer vehicles caught in the dilemma zone during the after period. Based on the model structure, this coefficient can be converted into an equivalent reduction percentage of 73 percent (= 100 [1  e^{−1.317}]). Thus, the analysis indicates that the after study periods experienced 73 percent fewer vehicles in the dilemma zone than the before study periods.
Table 34. Calibrated dilemma zone model statistical description.
Model Statistics 
Value 

RK^{2}: 
0.87 

Scaled Pearson χ^{2}: 
1.07 

Pearson χ^{2}: 
19.7(χ20.05, 20 = 26) 

Dispersion Parameter k: 
6.2 

Observations: 
20 h 

Range of Model Variables 

Variable 
Variable Name 
Units 
Minimum 
Maximum 
Q 
Approach flow rate 
vehicles/h 
120 
1,512 
C 
Cycle length 
s 
57 
127 
Calibrated Coefficient Values 

Variable 
Definition 
Value 
Standard Deviation 
tstatistic 
b0 
Intercept 
10.912 
2.745 
4.0 
b1 
Effect of exposure 
1.376 
0.274 
5.0 
b2 
Effect of U.S. 24/Cummings Lane location 
1.621 
0.381 
4.3 
b3 
Effect of change in detection and control 
1.317 
0.266 
5.0 
Researchers assessed the fit of the model through the graphical comparison of the observed and predicted dilemmazone counts as indicated in figure 22. The trend line in this figure does not represent the line of best fit; rather, it is a “y = x” line. The data would fall on this line if the model predictions exactly equaled the observed data. The trends shown in this figure indicate that the model is able to predict the dilemmazone count without bias.
MaxOut Model Results
The model used for the maxout frequency analysis was the same as used for the redlight violation model. The regression analysis indicated that the calibrated model accounted for most of the variability in the data. The U.S.24/Cummings Lane location required one locationspecific indicator variable. This location experienced very few maxouts relative to the other locations. Model variables explain differences among the other locations.
Table 35 shows the statistics related to the calibrated maxout model. The calibrated coefficient values can be used with figure 20 to predict the hourly maxout frequency for a given intersection approach. A dispersion parameter k of 0.43 yielded a scaled Pearson χ^{2} of 1.19. The Pearson χ^{2} _{0.05, 22} (= 34), the hypothesis that the model fits the data cannot be rejected. R_{K}^{2} for the calibrated model is 0.72. This value suggests that the model explains much of the variability in the data.
The last rows of table 35 show the regression coefficients for the model. The tstatistic shown indicates that all coefficients but one are significant at a 95percent level. The coefficient b_{3} for “effect of a change in detection and control” was not statistically significant.
Figure 22. Graph. Comparison of observed and predicted number of vehicles in the dilemma zone.
Table 35. Calibrated maxout model statistical description.
Model Statistics 
Value 

RK^{2}: 
0.72 

Scaled Pearson χ^{2}: 
1.19 

Pearson χ^{2}: 
26.3 (χ20.05, 22 = 34) 

Dispersion Parameter k: 
0.43 

Observations: 
22 h 

Range of Model Variables 

Variable 
Variable Name 
Units 
Minimum 
Maximum 
Q 
Approach flow rate 
vehicles/h 
120 
1512 
C 
Cycle length 
s 
57 
127 
Calibrated Coefficient Values 

Variable 
Definition 
Value 
Standard Deviation 
tstatistic 
b0 
Intercept 
25.971 
9.942 
2.6 
b1 
Effect of exposure 
2.635 
0.983 
2.7 
b2 
Effect of U.S. 24/Cummings Lane location 
3.063 
1.296 
2.4 
b3 
Effect of change in detection and control 
0.722 
0.866 
0.8 
A negative coefficient for b_{3} indicates that maxouts were less frequent during the after period. Based on the model structure, this coefficient can be converted into an equivalent reduction percentage of 51 percent (= 100 [1  e^{−0.722}]). Thus, the analysis indicates that the after study periods experienced 51 percent fewer maxouts than the before study periods. However, this percentage varied widely among locations, and it was relatively infrequent at all locations (except the U.S. 84/Speegleville Road location). For these reasons, it appears that the change in detection and control reduces maxout frequency, but the trend is not known with certainty. The available data make it impossible to rule out the possibility that the maxout frequency actually increased in the after period.
Researchers analyzed the fit of the model through the graphical comparison of the observed and predicted maxout frequencies as indicated in figure 23. The trend line in this figure does not represent the line of best fit; rather, it is a “y = x” line. The data would fall on this line if the model predictions exactly equaled the observed data.
Figure 23. Graph. Comparison of observed and predicted maxout frequency.
Table 36 summarizes the findings of the upper limit comparison of different maximum green settings of 75, 85, and 95 s. The results indicated in this tabular summary are followed by statistical analysis. Visual observation of the results does not make a compelling case indicating improvement in the standard MOEs except for phase 6 maxouts. Phase 2 maxouts remain constant because the northbound through movement is impeded less often (only by phase 8) than phase 6. There is no apparent trend in RLRs from these data.
Table 36. Upper Limit Study summary.^{(1)}
Max1: 75 s 
Max1: 85 s 
Max1: 95 s 

Date 
10/28/09 
10/29/09 
11/04/09 
11/05/09 
11/11/09 
11/12/09 
Phase 2 MaxOut 
1 
0 
1 
0 
0 
1 
Total No. Phase 2 Cycles/Day 
874 
871 
852 
862 
875 
859 
Phase Average Green (s) 
68 
68 
69 
68 
67 
68 
Phase6 MaxOut 
16 
13 
6 
9 
4 
3 
Total No. Phase6 Cycles/Day 
615 
609 
590 
573 
584 
610 
Phase 6 Average Green (s) 
114 
118 
121 
126 
122 
118 
Phase 8 MaxOut 
11 
8 
8 
12 
9 
10 
Total No. Phase 8 Cycles/Day 
615 
609 
590 
573 
584 
610 
Phase 8 Average Green (s) 
10 
10 
11 
11 
11 
11 
MP Phase2 LL RLRs 
6 
5 
3 
4 
1 
6 
MP Phase2 RL RLRs 
6 
9 
5 
5 
5 
3 
MP Phase6 LL RLRs 
5 
0 
2 
1 
2 
1 
MP Phase6 RL RLRs 
3 
5 
5 
4 
5 
2 
Total MP 
20 
19 
15 
14 
13 
12 
MOP Phase2 LL RLRs 
6 
5 
1 
1 
5 
3 
MOP Phase2 RL RLRs 
2 
2 
8 
4 
5 
6 
MOP Phase6 LL RLRs 
2 
2 
0 
1 
3 
2 
MOP Phase6 RL RLRs 
7 
6 
4 
2 
5 
4 
Total MOP 
17 
15 
13 
8 
18 
15 
EP Phase2 LL RLRs 
1 
3 
4 
1 
1 
4 
EP Phase2 RL RLRs 
3 
3 
3 
6 
7 
4 
EP Phase6 LL RLRs 
7 
4 
0 
4 
2 
0 
EP Phase6 RL RLRs 
3 
5 
5 
9 
2 
3 
Total EP 
14 
15 
12 
20 
12 
11 
EOP Phase2 LL RLRs 
0 
1 
3 
2 
1 
2 
EOP Phase2 RL RLRs 
3 
4 
5 
7 
2 
2 
EOP Phase6 LL RLRs 
0 
2 
0 
0 
0 
0 
EOP Phase6 RL RLRs 
1 
2 
0 
3 
1 
1 
Total EOP 
4 
9 
8 
12 
4 
5 
Total Peak RLRs 
34 
34 
27 
34 
25 
23 
Total OffPeak RLRs 
21 
24 
21 
20 
22 
20 
Total RLR Per Day 
55 
58 
48 
54 
47 
43 
RLR Percent Reduction Per Day 
13 
7 
15 
26 
^{1}AM peak 6–9; PM peak 4–8.
Blank cell = base condition.
EOP = evening off peak.
EP = evening peak.
LL = left lane.
MOP = morning off peak.
MP = morning peak.
RL = right lane.
Table 37 provides summary statistics describing the variables in the upperlimit database. The data in each row of this table reflect about 1 h of data collection for 1 signal phase at the U.S.281/E. Borgfeld Drive location. The data in table 37 indicate that the research project observed more than 300 signal cycles at this location. During these cycles, the subject phase terminated by maxout for 14 signal cycles. The traffic flow rate ranged from 1,255 to 1,450vehicles/h, and the cycle length ranged from 91 to 109 s.
The last column of table 37 indicates that the sample size is somewhat small, having only 14observations during the collective set of study hours. The trend in the data (shown in the last few rows of the last column) indicates that the maxout frequency decreased with increasing maximum green duration. The number of maxouts decreased by 43 percent when operating the intersection with an 85s maximum green compared with a 75s maximum green. The number of maxouts decreased 57 percent when operating at a 95s maximum green, compared with a 75s maximum green.
Table 37. Upperlimit database summary—total observations.
Location 
Maximum Green, s 
Study Hour 
Cycles 
Flow Rate,^{1} vehicles/h 
Cycle Length,^{2} s 
Number of MaxOuts^{1} 
U.S. 281/ 
75 
17:10 
39 
1,407 
93 
0 
17:00 
36 
1,281 
101 
3 

16:00 
38 
1,290 
95 
4 

85 
17:20 
35 
1,428 
101 
3 

17:10 
38 
1,303 
91 
1 

16:20 
38 
1,291 
93 
0 

95 
17:20 
35 
1,255 
98 
0 

16:00 
34 
1,288 
107 
1 

17:00 
33 
1,450 
109 
2 

Total 
75 
all 
113 
3,978 
96 
7 
85 
all 
111 
4,022 
95 
4 

95 
all 
102 
3,993 
105 
3 

Percent Change 75 to 85^{3} 
1.8 
1.1 
1.1 
43 

Percent Change 75 to 95^{3} 
9.7 
0.4 
9.0 
57 

Total 
326 
11,993 
81 
14 
^{1}Flow rate and counts include both passenger cars and heavy vehicles.
^{2}Cycle length in the total rows represents an average length (not a sum).
^{3}Percent change = 100 x (after/before  1.0).
The statistical analysis used a model similar to figure 20 but with an additional variable for maximum green duration. The results of this analysis indicate that the trend in the last column of table 37 was not statistically significant. Thus, it appears that a longer maximum green setting may reduce maxout frequency, but the trend is not known with certainty. The available data make it impossible to rule out the possibility that the maxout frequency actually increases with maximum green duration.
The original intent for the Upper Limit Study was to compare the MOEs—redlight running, vehicles caught in the dilemma zone, and maxout frequency—to those caused by traditional detection, accounting for any variations in traffic or other conditions. The only comparison that was available for this intersection was comparing the before data from the existing inductive loops with the after data with DCS, both with Max1 set at 75 s, which was done in the earlier comparisons. Further analysis using simulation would be the only way to evaluate the desired increase in demand and determine at what volume DCS is no longer able to provide adequate dilemmazone protection.
The primary objective of study 2 was to evaluate beforeafter crash data to determine the effectiveness of DCS in reducing motor vehicle crashes at signalcontrolled intersections. The section reports on a statistical analysis to evaluate the effectiveness that DCS had on all (TOT), fatal and injury (FI), and angle plus rear end (angle plus RE) crashes. The reason some categories were combined was to increase the sample size.
Methodology
The research team used a beforeafter study to evaluate the safety effectiveness of DCS at the eight selected sites. The evaluation used the comparison group method with correction for traffic flow to overcome some of the problems with a simple or naïve beforeafter study. This method uses a comparison group that has influencing factors similar to those of the treated group. The following assumptions underlie this approach:^{(13)}
 The factors that affected safety have changed in the same way from before the improvement to after the improvement for both the treatment and the control groups.
 The changes in the various factors influence the safety of the treatment and the control groups in the same manner.
The results from this approach are considered more accurate and reliable than the simple beforeafter study because the new approach can account for external causal factors. Although this approach can improve the weakness of the simple method through careful selection ofng the comparison groups, it is still subject to the regressiontothemean (RTM) bias because it predicts the expected number of target crashes of a site based on the beforeperiod crash number only. RTM refers to the tendency for a fluctuating characteristic of an entity to return to a typical value in the period after an extraordinary value has been observed.^{(16)}
The comparison group method considers only the factors that are unidentified, unmeasured, and not understood.^{(14)} If a beforeafter study is planned, information about traffic flow in the before and after periods should always be secured.^{(14)} Because the effect of change in traffic flow on safety may be large, it is important to try to account for it directly and explicitly. Harwood et al. used a variation of the comparison group approach, which makes the traffic volume adjustment using a regression relationship between crash frequency and traffic volume. The beforeafter study uses the following steps:^{(16)}
Step 1. Define the target crashes.
Safety of DCS is measured in terms of its ability to reduce crashes related to phase termination (e.g., rearend and angle crashes). The target crashes are the types of crashes that are likely influenced by DCS.
Step 2. Define the comparison group.
The comparison group method uses sites that are similar to the treated entities but without DCS installation. Comparison group crashes are useful in explaining factors other than DCS that might have influenced the safety of an intersection. In each case, the operating agency offered sites that were similar to the treated sites.
Step 3. Predict the expected number of crashes and variances for after period.
This information is required to account for factors other than the treatment that affect safety but are either not measured or the influence of which on safety is not known. The expected number of after period crashes and their variance for site i had the treatment not been implemented at the treated site is shown in figure 24:
Figure 24. Equation. Expected number of crashes and variances for after period.
Where:
_{a} = the ratio of duration of after period to the duration of before period.
_{r}= the ratio of after to before target crashes at comparison sites = (N/M)/(1=1/M).
_{tf }= the ratio of the functional relationship between traffic flow and safety in the before period to that in the after period = f(A_{avg})/f(B_{avg}).
K = total crash counts during the before period in a treated site.
M = total crash counts during the before period in a comparison site.
N = total crash counts during the after period in a comparison site.
A_{avg} = expected traffic volume (averaged over the number of years) in the after period.
B_{avg} = expected traffic volume (averaged over the number of years) in the before period:
Step 4. Compute the sum of the predicted crashes over all treated sites and its variance.
It is widely recognized that the safety effect of a treatment varies from one site to another. Thus, instead of a single site, one would like to know the average safety effect of the treatment for a group of sites. The expected number of afterperiod crashes and their variances for a group of sites had the treatment not been implemented at the treated sites is given in figure 25:
Figure 25. Equation. Sum of predicted crashes and its variance.
Where:n = total number of sites in the treatment group.
= expected afterperiod crashes at all treated sites had there been no treatment.
Step 5. Compute the sum of the actual crashes over all treated sites.
For a treated site, the crashes in the after period are influenced by the implementation of the treatment. The safety effectiveness of a treatment is known by comparing the actual crashes with the treatment to the expected crashes without the treatment. The actual number of afterperiod crashes for a group of treated sites is given in figure 26:
Figure 26. Equation. Sum of actual crashes for treated sites after period.
Where:
L_{i} = total crash counts during the after period at site i.
Step 6. Compute the unbiased estimate of safetyeffectiveness of the treatment and its variance.
The “index of effectiveness (θ)” is defined as the ratio of what safety was with the treatment to what it would have been without the treatment. The parameter gives the overall safety effect of the treatment and is seen in figure 27.
Figure 27. Equation. Index of effectiveness.
The percent increase in the number of target crashes owing to the treatment is calculated by 100(1  ) percent. If is less than 1, then the treatment has a positive safety effect. The estimated variance and standard error of the estimated safetyeffectiveness are shown in figure 28 and figure 29:
Figure 28. Equation. Variance of estimated safetyeffectiveness.
Figure29. Equation. Standard error of safetyeffectiveness.
The approximate 95percent confidence interval for θ is given by adding and subtracting 196s.e.() to . If the confidence interval contains the value 1, then no significant effect has been observed.
Data Description
Table 38 summarizes the number of crashes occurring in the four States by the following metrics:
 Treatment period (before DCS and after DCS).
 Length of analysis period (duration in years).
 TOT crashes.
 Severity of crashes (FI).
 Type of crashes related to DCS (angle plus RE).
All sites except one (U.S. 281/E. Borgfeld in Texas) have a comparison site, although in most cases, more than one treatment site uses the same nearby comparison site.
Table 38. Number of crashes at treatment and comparison sites.
State 
Category 
Site 
Period 
AADT 
Duration (year) 
TOT 
FI 
Angle Plus 
FL 
Treatment 
U.S. 27/Griffin Rd. 
Before 
17,225 
4.2 
12.0 
2.0 
4.0 
After 
19,350 
2.2 
13.0 
1.0 
3.0 

Comparison 
NW138 
Before 
41,875 
4.2 
67 
23.0 
31.0 

After 
34,500 
2.3 
38 
10.0 
14.0 

Treatment 
U.S. 27/Johnson Rd. 
Before 
17,225 
4.2 
16.0 
2.0 
3.0 

After 
19,200 
2.3 
10.0 
0.0 
0.0 

Comparison 
NW138 
Before 
41,875 
4.2 
67 
23.0 
31.0 

After 
34,500 
2.3 
38 
10.0 
14.0 

Treatment 
U.S. 27/Pines Blvd. 
Before 
19,925 
4.2 
19.0 
2.0 
6.0 

After 
18,600 
2.3 
11.0 
0.0 
0.0 

Comparison 
SR997/Krome Ave. 
Before 
23,000 
4.2 
33.0 
15.0 
19.0 

After 
19,150 
2.3 
12.0 
3.0 
5.0 

IL 
Treatment 
U.S. 24/Main St. 
Before 
9,730 
5.0 
20.0 
3.0 
10.0 
After 
11,150 
2.0 
9.0 
2.0 
5.0 

U.S. 24/Cummings Ln. 
Before 
12,700 
5.0 
30.0 
6.0 
16.0 

After 
14,025 
2.0 
15.0 
2.0 
11.0 

Comparison 
IL29/Rench Rd. 
Before 
21,100 
5.0 
17.0 
5.0 
14.0 

After 
21,100 
2.0 
14.0 
3.0 
14.0 

LA 
Treatment 
LA 3235/LA 3162 
Before 
6,700 
3.8 
7.0 
2.0 
7.0 
After 
6,700 
2.0 
3.0 
0.0 
2.0 

Comparison 
LA20Mel 
Before 
10,833 
3.8 
18.0 
3.0 
16.0 

After 
13,067 
2.0 
3.0 
0.0 
3.0 

TX 
Treatment 
U.S. 84/ Speegleville Rd. 
Before 
22,250 
3.8 
9.0 
4.0 
5.0 
After 
22,220 
2.2 
7.0 
3.0 
3.0 

Comparison 
Lp340 
Before 
5,310 
3.8 
15.0 
7.0 
15.0 

After 
5,310 
2.2 
4.0 
1.0 
4.0 

Treatment 
U.S. 281/E. Borgfeld Dr. 
Before 
30,500 
1.5 
33.0 
16.0 
32.0 

After 
31,750 
2.0 
40.0 
19.0 
40.0 

Comparison 
N/A 
Before 
N/A 
N/A 
N/A 
N/A 
N/A 

After 
N/A 
N/A 
N/A 
N/A 
N/A 
AADT = annual average daily traffic.
N/A = not applicable.
Analysis Results
Safety performance functions (SPF) are used to develop a relationship between the number of crashes and daily traffic data. This study develops the SPFs with the crash and traffic data in the before period. Table 39 shows the summary of statistics for these crashes and annual average daily traffic (AADT). The number of observations is not the number of intersections independent spatially but the number of intersections independent temporally (e.g., a year). As a result, an intersection may produce several data points depending on the number of years of data collected.
Table 39. Summary statistics of crashes and traffic flow.
Variable 
Minimum 
Maximum 
Mean 
Standard Deviation 
Sum 
TOT 
0 
19 
4.19 
3.45 
226 
FI 
0 
9 
0.87 
1.61 
47 
Angle Plus RE 
0 
18 
2.15 
3.21 
116 
AADT 
5,310 
31,000 
16,425.19 
6,137.97 
— 
—No data.
The Poissongamma (i.e., negative binomial) model is the most common type of model used by transportation safety analysts for modeling traffic crashes. This model is preferred over other mixedPoisson models because the gamma distribution is the conjugate of the Poisson distribution. The Poissongamma model has the following model structure: the number of crashes Y_{it} for a particular i^{th} site and time period t when conditional on its mean µ_{it} Poisson distributed and independent over all sites and time periods, as shown in figure 30
Figure 30. Equation. Number of crashes based on the Poissongamma model.
Where:
i = 1, 2, …, i, and t = 1, 2, …, t
The mean of the Poisson is structured as shown in figure 31:
Figure 31. Equation. Mean of the number of crashes.
Where:
f = function of the covariates (X).
β = vector of unknown coefficients.
e_{it} is the model error independent of all the covariates.
The functional form used for the model in this study is as shown in figure 32:
Figure 32. Equation. Functional form of safety performance.
Where:
Y = number of years of crash data.
β_{i} = a vector of unknown coefficients (to be estimated) (i = 0,1).
Table 40 summarizes the estimation results for all three crash types. In general, the sign and magnitude of the regression coefficients in table 40 are logical and consistent with previous research findings.
Table 40. Parameter estimation for SPF.
Parameter 
Inferred Effect of… 
TOT 
FI 
Angle Plus RE 

Value 
tstatistic 
Value 
tstatistic 
Value 
tstatistic 

β_{0} 
Intercept 
6.8143 
3.0 
11.4937 
2.4 
4.9924 
1.4 
β_{1} 
AADT 
0.8667 
3.7 
1.1868 
2.4 
0.6160 
1.7 
α 
Dispersion parameter 
0.2286 
2.6 
1.1365 
2.2 
1.1508 
3.4 
AIC 
Akaike information criterion 
254.6 
143.5 
218.9 
The coefficients in table 40 were combined with the equation in figure 32 to obtain the crash mean of each crash type:
The form of each model is shown in figure 33, figure 34, and figure 35:
Figure 33. Equation. SPF for TOT crashes.
Figure 34. Equation. SPF for FI crashes.
Figure 35. Equation. SPF for angle plus RE crashes.
Table 41 presents the average safety effect of DCS based on crash data. The average safety effect of DCS is known from the unbiased estimate of index of effectiveness (). If this value is less than 1, then DCS has a positive effect on safety (improves safety). The analysis results suggest that DCS has no effect on TOT and FI crashes and produces a reduction of 9 percent for angle plus RE crashes. The standard deviation of this estimate of average safety effect is 15percent, so at a 95percent confidence level, the result is not significant. This result can be attributed to the small sample size. Achieving a significant result at the 5percent level would require a larger number of treated sites, a longer period of crash data collection, or both.
Table 41. Average safety effect of DCS based on crash data.
Measure 
Description 
TOT 
FI 
Angle Plus RE 

Number of crashes observed during the after period^{1} 
108.0 
30.0 
66.0 

Expected number of crashes during after period had red light cameras not been installed 
107.6 
29.0 
71.6 
Var () 
Variance of 
84.22 
29.73 
65.93 
Unbiased estimate of index of effectiveness 
1.00 
1.00 
0.91 

σ () 
Standard error of 
0.13 
0.25 
0.15 
100 (  1) 
Percent increase in the number of crashes^{1} 
0 
0 
9 
( θ_{lower’ }θ_{upper}) 
95 percent confidence interval for θ 
(0.75, 1.25) 
(0.50, 1.49) 
(0.61, 1.20) 
^{1}A negative value represents a decrease, while a positive value represents an increase in crashes.