1. An Impact Assessment of Intersection Design and Operational Elements on Red-light Running
Canadian Associate of Road Safety Professionals Conference
May 2019
Bob Henderson, CET, LEL
Pedram Izadpanah, Ph.D., P.Eng

2. Background Angle collisions account for:
~10% of all collisions at traffic signals in the Region of Waterloo
More severe and fatal injuries compared to other collision types

3. Study Objective Identify geometric and operational factors that impact
Chargeable Incident
An event whereby a red-light camera determines that a red-light violation occurred
Average Intrusion Time
The time an incident occurs after the onset of a red-light indication
The higher the number, the greater likelihood of a more severe angle collision occurring
May be a key indicator that drivers are not detecting the presence of an intersection

4. Study Design 16 signalized intersections with red-light cameras
Red light camera data from
Categorize intersections by geometrical elements
Presence of a median
No Approach Median: 7 Intersections
Approach median: 9 Intersections
Crossing width
Ranging from 14 m to 47 m
Approach Lanes
2, 3, and 4 lanes
Number of legs
4-legged and 3-legged

5. Study Design Categorize intersections by operational elements
Posted speed limit
Ranging from 50 km/h to 70 km/h
Fixed vs. Semi-actuated signal operation
One-way versus two-way operation
Number of signal heads
2, 3, or 4 signal heads
Lateral Signal head spacing
Ranging from 6 m to 18 m

6. Study Design Categorize intersections by operational elements
All-red Phase
Ranging from 2.0 Sec to 2.6 Sec
Amber Phase
Ranging from 3.7 Sec to 4.2 Sec
Average Annual Daily Traffic (AADT)
Ranging from 1,855 veh/day to 19,152 veh/day
Important Note:
All intersections assessed were operating with pedestrian countdown signals (PCS) during the study period
A previous Region of Waterloo study found that PCS reduced red-light running by 40%

7. Study Methodology Descriptive Analysis Statistical Modelling
Chargeable Incident
Average Intrusion Time

8. Descriptive Analysis

9. Descriptive Analysis

10. Descriptive Analysis

11. Chargeable Incident Model
CI=∝ × AADT β1×W β2×e β3 × Leg×e β4×Median
Where: CI
Chargeable Incidents per Month
AADT W
Crossing Width (m)
Leg
1 if the intersection is 4-legged and 0 otherwise
Median
1 if the approach with RLC has a median and 0 otherwise
Chargeable Incidents
Increase as AADT increases (exposure)
Increase as crossing width increases
Increase at 3-legged versus 4-legged intersections (less conspicuous?)
Decrease with the presence of medians (visual cue?)
Ln(∝)
𝛽 1
𝛽 2
𝛽 3
𝛽 4
Coefficient Estimates
p-value
< 2e-16
Generalized Linear Model (GLM) with a Negative Binomial (NB) error structure
Over dispersion parameter:
AIC:

12. Average Intrusion Time Model
AvgInt =𝑎+ 𝑏 1 ×𝐴𝐴𝐷𝑇+ 𝑏 2 ×𝐿𝑒𝑔+ 𝑏 3 ×𝑂𝑛𝑒−𝑊𝑎𝑦
Where:
AvgInt
Average Intrusion Time per Month (Sec)
AADT
Leg
1 if the intersection is 4-legged and 0 otherwise
One-Way
1 if the approach with RLC is a one-way street and 0 otherwise a
𝑏 1
𝑏 2
𝑏 3
Coefficient Estimates
6.843
2.4738
3.236
p-value
8.61e-14
< 2e-16
Adjusted R-squared:

13. Conclusions
Locations with higher volume experience larger frequency of red light running but fewer instances of hazardous situation with large intrusion time
3-legged intersections experience more chargeable incidents compared to 4-legged intersections but the average intrusion time is higher at 4-legged intersections

14. Conclusions
Presence of an approach median reduces the frequency of red light running
Presence of an approach median reduces intrusion time of red light running although not statistically significant at a 95% confidence interval

15. Conclusions One-way operation increases the intrusion time
One-way operation increases the frequency of red light running although not statistically significant at a 95% confidence interval

16. Conclusions The results of this study can be used to:
Improve intersections with higher potential for red light running especially those with larger intrusion time; and
Select intersections that would benefit most from the deployment of red-light cameras

17. Limitations Only 16 sites evaluated
Potential to expand study to include red-light camera sites from across Ontario.

18. Acknowledgements Pedram Izadpanah, Ph.D., P.Eng
Modelling, Statistical Analysis
Jeffery Catlin, City of Toronto
Data production

19. Thank you!