This story is a staff report from The Paper.

Mayor Tim Keller announced Tuesday that the City’s Automated Speed Enforcement program has expanded into more locations around the city, with speed monitoring devices added along the Lead/Coal corridor and on Unser. The three new devices bring the system up to six cameras total. Four additional fixed devices are in the works.

“We’re continuing to expand this program into places where we know speeding has taken a huge toll on quality of life, and made people feel unsafe in their own neighborhoods. These three new locations are a direct reflection of what these communities need and have been asking for, and everyone needs to be held accountable,” said Keller.

The top speeds posted for high traffic areas around the city are below:

Eastbound Gibson Blvd. (119.89 MPH)

Westbound Gibson Blvd. (107.82 MPH)

Northbound Unser Blvd. (90.14 MPH)

Westbound Montgomery Blvd. (131.08 MPH)

Westbound Lead Ave. (70.23 MPH)

Eastbound Coal Ave. (77.84 MPH)

“The blatant disregard for speed limits across the city continues to show the importance of Automated Speed Enforcement,” said Lieutenant Nick Wheeler, APD Motors Unit. 

To continue aggressive traffic enforcement, APD has also created an Aggressive Driving Unit which will begin next week. The Unit, comprised of two detectives will be responsible for following up on aggressive driving and road rage incidents and hit and run crash investigations. The unit will contact registered vehicle owners, issuing criminal summons for offenders and will make arrests. The detectives will also review data from automated speed systems to assist with live enforcement targeting the most egregious speeds.

Starting next Monday, a link will be live where the public can submit photos, video and any other evidence pertaining to the above stated incidents that can assist with these traffic related investigations.

Over the coming months, the city hopes to add additional locations until the system has 10 devices in place. Locations for the speed cameras are identified using speed and injury data, as well as feedback from the community.