Optimizing Urban Traffic Patterns in the Age of Navigation Apps
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It’s 3:45pm and I’m a tech worker looking for the quickest path out the door to get home after a full six-hour day. Yes, occasionally I’ll work at home, but I need to try to beat the Friday traffic from the office today.
I’ve already opened the WAZE app and it’s selecting my quickest path as a single occupancy driver. It has me arriving home by 4:45pm, which is enough time for me to finish my work day and then spend the next 12 hours trying to beat my personal best at Fortnite.
From the insight of a user, the traffic app, WAZE in this example, is just a tool to help navigate the daily routine of a commute from work to home. Though unavailable a few years ago, it’s now a game changer – not only for drivers, but also for traffic engineers. Consumers a decade ago used the printed Thomas Guide or purchased navigational devices in order to navigate. Now, it’s as easy as clicking an app on your cell phone.
Navigation Apps Are a Game-Changer for Traffic Engineers
For City, County, and State agency staff and engineers, agency personnel are confronting cell phone navigational applications that choose the quickest routes based on calculated pathways (algorithms), not based on the classifications for the roadway use. The roadways are classified to balance access, speed and traffic volumes. What has occurred as a result of these navigation apps is a significant increase in cut-through traffic, speeding on side streets, and, for traffic engineers, greater use of roadways not intended for higher volumes.
Traffic engineers typically classify roads as follows:
Have limited access; entry only at onramps and no access at mid points. These roads are intended for higher speed limits.
Usually handle higher volumes of vehicles. You might experience these as 5 – 7 lane roads with signalized intersections or roundabouts.
Local / Residential Streets
Are generally two-lane roadways that may or may not include lane markings. Typically, there will be parking along these roadways. These have higher access (more driveways) and lower volumes and speeds.
Navigation apps have turned this planned classification system (engineers’ Zen state) on its head. The algorithms in the apps choose the quickest path through existing traffic, such as when backups occur on the freeway or a major arterial. This opens up the increase in cut-through traffic in neighborhoods, followed by phone complaints into transit agencies.
DKS Associates assisted a client with the issue described above. We collected information from the roadway, as well as from both Google and WAZE applications. Utilizing data from these sources, DKS developed an analytical approach to looking at this issue. We use calculated travel times from WAZE, Google, and other collected data, analyze the data, implement changes, and then calculate travel times / speeds based on the implemented change to verify if they’re making a difference. Then repeated the process.
Our team developed a staged process to meet the following agency-defined goals:
- Implement solutions that could be removed easily (less expensive) if they didn’t work out
- Reduce speeds along roadways
- Reduce cut-through volumes on local streets and shift volumes onto arterials
- Improve safety
Our team held an open house to share information with the public. The initial roll-out included LED traffic speed signs (quickly alerting drivers to slow down if they exceeded the speed limit), coordination with other agencies to let them know our intentions, and adjusting freeway ramp metering rates to slightly increase travel times of cut-through traffic to maximize use of adjacent larger arterial roadways to access the freeway system. Additional restrictions on left-turn movements during the peak hour have also helped to either increase path length (also helping to add time to the calculated algorithm) or restrict access and thereby increase travel path distance. Enforcement is another element that has also helped to provide additional reinforcement for illegal left turn movements and speeding.
A story from one of the client’s traffic officers is worth sharing. The officer was in his patrol car across an intersection looking at a driver signaling to make a left turn. The driver, who was approaching the intersection from a side street, saw the peak hour left-turn restriction and must have been aware that he should not make the left turn onto the arterial. The driver looked up at the police officer, looked down at their phone, looked back up at the officer – this time the officer made eye contact with a gesture of his head signaling ‘no don’t do it,’ driver looked back down to the phone and made the illegal left turn. His defense was that the app told him to make the left turn. Unfortunately, an app telling you the quickest path is not a legitimate defense for making an illegal turn.
We have recently begun discussions with WAZE and coordinated with their engineers to add peak hour left-turn restrictions, as well as future construction projects and the associated traffic disruptions, to the algorithms in the app.
Our success in this project created the benefits of slowing down vehicle speeds on residential roads, reduced the volumes of cut-through traffic, and improved the use of primary arterials for shorter travel times to the freeway. I don’t think we outsmarted WAZE, we just found a way to work within a new traffic paradigm. I’m available to talk with any traffic app developers who would appreciate a different perspective and a lively discussion. I can be reached at 206-436-0556 or email@example.com.
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