By now, we've all used a mapping app -- like Apple Maps, Google Maps and Waze – to find the best way from point A to point B. A primary attraction of the apps is that they can filter traffic conditions in real time and provide the quickest route to your destination, which may not always be the most direct. Many times, the apps direct drivers onto residential side streets not designed for through traffic or additional volume.
 
For traffic engineers, this can be a big headache. The traffic grid is designed primarily to move large numbers of vehicles as safely as possible. Design choices are made based on expected traffic under normal conditions. The mapping apps, on the other hand, serve the individual driver's desire for flexibility and convenience based on real time conditions in the moment. The more often actual conditions diverge from what is expected, the more drivers are sent along unexpected pathways.
 
Traffic engineers also worry about safety, as through drivers are statistically more likely to exceed speed limits on residential streets than local drivers. And, according to the AAA, the chances of a pedestrian fatality in a collision increase dramatically as the vehicle's speed increases above 25 mph.
 
Traffic engineers now try to deduce how the apps apply their algorithms and influence traffic patterns to force the apps to achieve the engineers' desired outcomes. That could explain the new barrier, no-left-turn sign or lower speed limit in your neighborhood. By increasing drive time through the neighborhood, the engineers hope to force the apps to recommend arterial roads instead. Studies have shown that increasing travel time on a cut-through route by only 2-3 minutes will push the apps back to the arterials. But then traffic will increase on the arterial, slowing drive times and sending drivers back onto neighborhood streets. Also, some users prefer to believe their app over their own eyes, making that prohibited left turn anyways because the app hasn't yet incorporated the new restriction, so it instructs the driver to turn.
 
The result is a game of cat-and-mouse that won't end until the apps incorporate concerns other than individual driver convenience into their algorithms. You may be seeing this already, as apps like Waze warn drivers when they are exceeding the posted speed limit. The same phenomenon has occurred in other sharing-economy services like Uber, Lyft and Airbnb, which have over time moved from purely serving user convenience to also incorporating some elements of the public good. For example, ride finding apps now offer a way to locate a handicapped accessible vehicle, and Airbnb has features to increase compliance with public lodging regulations.
 
And it's not all bad between the engineers and the apps – most apps make their driver data available to engineers, providing a wealth of information on actual driver behavior that will help better design the traffic networks of the future.