CMF / CRF Details
CMF ID: 2427
Implement automated red light running enforcement cameras (RLR-related crashes)
Description: RLR enforcement cameras
Prior Condition: no RLR enforcement cameras
Category: Advanced technology and ITS
Study: Evaluating the Effectiveness of an Automated Red Light Running Enforcement Program in Iowa Using a Bayesian Analysis, Hallmark et al., 2010
Star Quality Rating: | [View score details] |
Rating Points Total: | 35 |
Crash Modification Factor (CMF) | |
---|---|
Value: | 0.6 |
Adjusted Standard Error: | |
Unadjusted Standard Error: |
Crash Reduction Factor (CRF) | |
---|---|
Value: | 40 (This value indicates a decrease in crashes) |
Adjusted Standard Error: | |
Unadjusted Standard Error: |
Applicability | |
---|---|
Crash Type: | All |
Crash Severity: | All |
Roadway Types: | Principal Arterial Other |
Street Type: | |
Minimum Number of Lanes: | |
Maximum Number of Lanes: | |
Number of Lanes Direction: | |
Number of Lanes Comment: | |
Crash Weather: | Not specified |
Road Division Type: | |
Minimum Speed Limit: | |
Maximum Speed Limit: | |
Speed Unit: | |
Speed Limit Comment: | |
Area Type: | |
Traffic Volume: | |
Average Traffic Volume: | |
Time of Day: | All |
If countermeasure is intersection-based | |
Intersection Type: | |
Intersection Geometry: | |
Traffic Control: | Signalized |
Major Road Traffic Volume: | |
Minor Road Traffic Volume: | |
Average Major Road Volume : | |
Average Minor Road Volume : |
Development Details | |
---|---|
Date Range of Data Used: | 2001 to 2006 |
Municipality: | Davenport |
State: | IA |
Country: | |
Type of Methodology Used: | Before/after using empirical Bayes or full Bayes |
Sample Size (site-years): | 12 site-years before, 8 site-years after |