Every city has at least one, and many have several: a street that has become a major route for people driving through town, but wasn’t designed to handle the kind of volume that it sees today. Stop lights every few hundred yards keep drivers at a standstill during busy times of day, and there aren’t enough lanes to accommodate everyone.
The presence of businesses along these routes makes it even more difficult for officials to come up with workable plans for making improvements. Retail businesses balk at the thought of lengthy construction that would cut off access to their stores, and worry about visibility after the project is complete. Sometimes, it takes a dramatic proof that a giant problem exists before the wheels of progress begin to turn.
In Tennessee, one professor has used GPS data as just such a dramatic proof. All you have to do is try to drive down Lamar Avenue in Memphis to see that it could use some improvement. But the professor was able to come up with a quantitative version of just how bad the traffic problem is on this street using GPS.
The information in the study comes from fleet GPS devices mounted on tractor trailers—the vehicles most adversely affected by Lamar Avenue’s congestion problems. According to the “crowdsourced” data from many trucks, the highest average speed on Lamar Avenue is a dismal 30 miles per hour.
These tractor trailer drivers would love for that number to be closer to that of a major highway, which would be twice as fast. Thanks to the professor’s work, this dream might become reality, although it would certainly be many years in the future. It takes a huge amount of time and money to negotiate land acquisition from existing owners; plan the road improvements themselves; and actually execute the project. But the process has to start somewhere, and in this case GPS data appears to be a major driving force as it shows officials the exact scope of the problem.
Using GPS data to analyze traffic patterns is an obvious, if unplanned, benefit of fleet GPS tracking. Some popular individual-user navigation apps already use “crowdsourcing,” gathering data from other drivers’ activity to determine how traffic is moving in given areas. On a larger scale, the approach could make it easier for researchers to identify problems without conducting expensive, time-consuming studies on the ground.