The flash has caught all of us, especially the one in the K Street tunnel. It’s almost like an old acquaintance you have to see but dread. Your foot frantically pumping on the breaks back and forth as you look back hoping to make it by without a flash. Whether red-light cameras and speeding cameras are all about money or safety continues to be a hotly debated issue.

However, there’s no question that these automated speed and red light enforcement cameras continue to generate significant revenue for many American cities. In 2014, speed camera tickets in New York City generated $16.9 million for the city coffers. That’s an average of $46,301 a day from automated speed camera tickets (let’s not forget police issue many additional tickets) alone. The District of Columbia installed its first red light camera in 1999 and first stationary speed camera in 2005. According to the Metropolitan Police Department’s website, the Department identifies speed camera enforcement zones based on things such as recent speed-related fatalities and areas with vulnerable populations such as school zones.

There’s no question D.C. has enjoyed in the fruits delivered by these cameras. In April 2015, Triple AAA reported that the city had collected nearly $357 million in speed-camera ticket revenue alone. However, the glory days may be coming to an end. Last year, the city reported a 55% drop in speed camera ticket revenue last year. Why this may be the case is a debated issue and one that peaked the District Ninja team’s interest as we’ve all paid our fare share of these tickets ourselves.

First, we wanted to see the annual trends in order to get an idea of things such as whether the revenue per year was consistent or experienced dramtatic swings.

2009-15 Annual Totals

This first visual represents the total amount of revenue collected each year for traffic camera tickets as reported by DC Vision Zero Data

You can immediately see that the swings in revenue over the last six years have been pretty dramatic.For example,the totals from 2009 to 2011 ($120,887,899)combined are only approx $3 million more than 2012 ($117,240,649) alone. 2012 is especially large when you consider that one season (Summer or Fall) of 2012 is equal to more than all of 2011. 2012 brought in 1.75x the revenue than 2015. As you may recall, some of these large shifts has been attributed to the city’s broken cameras.  It’s also interesting to see 2013 and 2014 seem rather steady at $33 million range yet revenue doubled in 2015. This can’t all be attributed to a few months missing (see Ninja Notes for more info) As the next visual shows the monthly revenue for each year not only swung dramatically, but months in general also prove to bring in considerably different amounts of revenue.

 

From these month by month breakdowns  we notice a few trends immediately. First, in 2009, monthly revenue stayed between just under $3 million and just over $4 million consistently across the year. 2011 is similarly flat, hovering between just under $1 million and just over $2 million with the notable exception of October coming in at a whopping $9 million. 2010 seems to be the only year with a reasonably spike in summer months only. You’ll notice that several months are missing and this is due to missing data from the Vision Zero Dataset (more info below). Finally, we leveraged D3 to give us a better look at some of the specific streets and patterns within DC.

2009-15 Detailed Map

 

This interactive map above allows you to select each year and month and see the specific streets with a total number of tickets per street. You can mouse over each colored street to see exactly how many tickets were issued.  Pay close attention to the thickness of the lines as those are a “top down” indicator that there are more tickets issued on that street. You can also click “animate” and have the map cycle through all of the months and years to see the growth and change in tickets over time. As you can see below: K Street, 295, and New York Ave are among the highest ticketed streets over all time.

Lastly, we put together a quick table so you could see the top 15 streets/interstate that brought in camera ticket revenue from 2009-2015.

 

Top15Streets

 

 

 

 

 

 

 

 

 

Ninja Notes

All data was gathered from DC OCTO’s Vision Zero Data Set. Streets were determined by correlating each streetsegid with the DC OCTO Street Segment Dataset. For purposes of data toals we leveraged only fines listed as collected by Vision Zero as opposed to fines issued. Additionally only tickets marked as “Camera tickets” by the dataset were calculated. Several months were missing from DC’s open dataset release which is highlighted in the monthly visual. Months with no data were missing whereas months with 0 dollars were simply months with no camera tickets issued that were collected.

Vision Zero: A Closer Look at DC’s Traffic Cameras
Tagged on:                         

One thought on “Vision Zero: A Closer Look at DC’s Traffic Cameras

  • April 27, 2016 at 9:40 am
    Permalink

    Thanks for taking the time to provide your analysis of the DC traffic light enforcement data! A few questions/thoughts come to mind.

    How much of an impact on the fluctuations in revenue can be attributed to the introduction of new cameras? With that said, is there a way to determine how long after the introduction of a new camera does it take for the driving population to adapt their driving habits accordingly, thereby normalizing the expected revenues over time?

    Also, is there available registration data that could be layered with this dataset to determine the financial burden faced by the local community members versus commuters? Given the locations of the majority of the top 15 streets generating revenue, the extent of the local financial burden could have a ripple effect adding additional burden to families already facing determinant of health disparities. Your data presentation elicits very interesting public health questions.

    Cheers!

    Reply

Leave a Reply

Your email address will not be published. Required fields are marked *