Why Navigation Apps, Working Properly, Can Make Traffic Flows Worse — And What To Do About It

(Mis)Uses of Technology

from the commons-is-our-friend dept

Techdirt has just written about how advanced digital technology can be used for less-than-benign purposes, simply because it is a tool that can be applied in both good and bad ways. A fascinating analysis by Jane Macfarlane in IEEE Spectrum explores something similar: how new technology being used as designed, and with only the best intentions, can nonetheless give rise to potentially serious problems. The article is about how the increasingly-popular navigation apps like Waze, Apple Maps, and Google Maps are “causing chaos?“:

City planners around the world have predicted traffic on the basis of residential density, anticipating that a certain amount of real-time changes will be necessary in particular circumstances. To handle those changes, they have installed tools like stoplights and metering lights, embedded loop sensors, variable message signs, radio transmissions, and dial-in messaging systems. For particularly tricky situations — an obstruction, event, or emergency — city managers sometimes dispatch a human being to direct traffic.

But now online navigation apps are in charge, and they’re causing more problems than they solve. The apps are typically optimized to keep an individual driver’s travel time as short as possible; they don’t care whether the residential streets can absorb the traffic or whether motorists who show up in unexpected places may compromise safety.

One of the problems is that navigation apps use fairly crude models when working out the best routes. Often, they fail to take into account local details. Macfarlane’s article mentions things like extremely steep inclines, and roads where schools are located or that have a larger-than-usual number of pedestrians milling around. Another issue is that navigation apps are selfish — they don’t care if they cause knock-on problems for other drivers elsewhere:

Consider cars crossing a thoroughfare without the benefit of a signal light. Perhaps the car on the smaller road has a stop sign. Likely, it was designed as a two-way stop because traffic on the larger road was typically light enough that the wait to cross was comfortably short. Add cars to that larger road, however, and breaks in the traffic become few, causing the line of cars waiting at the stop sign to flow onto neighboring streets. If you’re in the car on the larger road, you may be zipping along to your destination. But if you?re on the smaller road, you may have to wait a very long time to cross. And if the apps direct more and more cars to these neighborhood roads, as may happen when a nearby highway is experiencing abnormal delays, the backups build and the likelihood of accidents increases.

Things are made worse because rival services not only don’t share traffic data with each other — leading to incomplete knowledge of flows, and sub-optimal route recommendations — they also don’t share their data with city transportation engineers who are trying to optimize traffic flows and minimize danger for everyone. That’s a big problem because the older models used by the authorities to keep everyone safe made assumptions about road use that was based on static factors like nearby population density. Those no longer hold when navigation apps introduce new dynamics — for example, by sending lots of traffic down residential streets that are normally quiet. Transportation engineers may therefore make decisions about traffic control based on erroneous assumptions that exacerbate problems, rather than relieve them. As MacFarlane points out, these knock-on effects of navigation apps have led neighborhoods and citizens to fight back against the unexpected and unwanted traffic flows:

In the early days of the problem, around 2014, residents would try to fool the applications into believing there were accidents tying up traffic in their neighborhood by logging fake incidents into the app. Then some neighborhoods convinced their towns to install speed bumps, slowing down the traffic and giving a route a longer base travel time.

A town in New Jersey, Leonia, simply closed many of its streets to through traffic during commute hours, levying heavy fines for nonresident drivers. Neighboring towns followed suit. And all faced the unintended consequence of their local businesses now losing customers who couldn?t get through the town at those hours.

These are clearly unsatisfactory ways of addressing the new problems otherwise benign navigation apps are causing. As the article notes, the way forward is for all the services in this sector to share their information with each other, and with city governments. This would provide more accurate data, and allow optimal routes to be calculated for everyone. Crucially, it would allow city transportation engineers to work with navigation apps, rather than trying to respond to the barely-understood patterns they cause.

Of course, there are important privacy issues that must be addressed. This could be achieved by combining individual data points into aggregated flows, perhaps with some obfuscating random elements added as well. Another issue is that larger navigation services might be unwilling to share their data with smaller rivals. One incentive for them is that doing so would be zero-cost way to improve the outcome for their users, not least by allowing better overall coordination. Another is that big Internet services are already being portrayed as greedy and selfish by many. Helping to create a traffic data commons for the public good would not only make them popular with their users, but would also provide them with some respite from their critics.

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Filed Under: navigation apps, traffic flows