Monitoring Motorist Compliance in an I/M Program – A Novel Approach of Using Speed Camera Citations Data Abdullahi A. Asimalowo & Charles E. Williams Monitoring and Assessment Branch, Air Quality Division District of Columbia Department of Energy and Environment Washington, DC

Disclaimer This presentation and the analysis therein, do not necessarily represent or express the opinion of the Department of Energy and Environment or the District of Columbia Government.

Background Vehicle inspection and maintenance (I/M) program helps identify high emitting vehicles that require repair, helping in improved air quality – Mandatory, based on air quality classification – population and/or – geographic location The 1990 Clean Air Act Amendments make I/M mandatory for states and jurisdictions in non-attainment status This study is a survey component of I/M program compliance

Location: District of Columbia The study was carried out for the District of Columbia (DC). DC covers 61 square miles area and is bounded in the north, northwest and east by the State of Maryland and in the south and southwest by the Commonwealth of Virginia.

Study Methods Three different datasets used: – Speed limit and traffic light camera photo tickets issued in 2015 – Vehicle registration database – Parking lot surveys (shopping malls, garages, public parking areas, etc.)

Findings: CY2015 Photo Tickets Data Table 1: Non-Compliance within Year Range Sample size, n=37,269 CI=95% Model YearSample Size Non- Compliance% Pre % % 1996-up % TOTAL %

Findings: CY2015 Photo Tickets Data Model YearSample SizeNon-Compliance% Pre % % 1996-up % TOTAL % Table 2: Non-Compliance relative to Sample population n=37,269 CI=95%

Findings: Vehicle Registration Data Model YearRange SumNon-Compliance% Within Range Pre % % % TOTAL % Table 3: 2013 Non-Compliance within Year Range

Findings: Vehicle Registration Data Table 4: 2014 Non-Compliance within Year Range Model YearRange SumNon-Compliance% Within Range Pre % % % TOTAL %

Findings: Vehicle Registration Data Table 5: 2015 Non-Compliance within Year Range Model YearRange SumNon-Compliance% Within Range TOTAL

Findings: 2015 Parking Lot Survey Table 6: Non-Compliance within Year Range Model YearRange SumNon-Compliance% Within Range Pre TOTAL n=2012; margin of error=±2.2

Discussions

Discussions: Photo Violations Photo violation tickets represents randomized survey >30,000 sample size; >10% of total registered vehicle within DC This sample size provides a good representation with minimal error margin (±0.5) in compliance rate estimate Sample size is well distributed and substantial as this is drawn from all loci in DC

Discussions: Parking Lot Survey Results vary with sample size Sample size in the hundreds gives a higher non-compliance rate (9% to 12%) and a higher error margin, compared to larger sample size More time and labor demanding As sample size increases, compliance increases and non-compliance decreases Validates use of photo enforced tickets, which provides a larger sample size, lower error margin and hence, greater accuracy

Discussions: Registered Vehicles Status Non-compliance rate is high, using this method (11 to 15%) Margin of error is lower compared to the other methods (0.04 or less); This was the lowest of the study, aside photo tickets data Registration data contain records of vehicles no longer in use

Conclusions Amongst all three (3) methods, parking lots survey and the photo ticket results were close (between 2% and 3%) Smaller sample size is associated with a higher error margin Some vehicles that are no longer in use are still in registration database. This can influence the result from using this method Road user bias is not taken into consideration, especially if they have violated one or more rules on the same vehicle; or have restrictions on their license

Conclusions Photo enforced violations allows for greater sample size, but do not take into consideration, road user biases Photo tickets and parking lot provides proof of vehicles actively in use Camera distribution covers all sections of the city and hence provide greater opportunity for data gathering Data gathering for I/M survey should not be devoid of sampling design and associated biases