Google Maps, Waze, Apple Maps, TomTom, HERE, and Sygic software are all Navigation Apps that are supposed to help us get to a specific location faster, safer, and more conveniently.
While many have been updated with a variety of features such as voice command support, offline maps, and location sharing options, these navigation apps all follow the same basic formula.
And their goal is to simply get drivers to a specific destination by providing them with a route that meets a set of criteria. Most of the time, these requirements boil down to finding the quickest route to a specific address, so these apps must not only generate a route to a specific point, but also ensure that it is the quickest option.
Historically, navigation solutions have progressed from simple software installed on GPS units without Internet access to more advanced devices and smartphones that receive all of their data from a remote server.
And the power of crowdsourcing, as in the case of Waze, is the magic ingredient that significantly improves accuracy, resulting in a route that is as close to the driver's expectations as possible.
But, at the end of the day, no navigation app is 100 percent accurate all of the time. And if you use them on a regular basis, you're probably aware that these apps will occasionally route you into heavy traffic, significantly increasing your travel time.
It's crucial to understand how these navigation apps provide traffic estimates in the first place to understand why this can happen at any time.
This method, of course, had lower accuracy because the traffic patterns were based on historical data rather than real-time information.
Google updated its systems in 2009 to rely on crowdsourcing as well, collecting anonymous data from other Google Maps-enabled devices to generate similar traffic patterns. In other words, Google simply used sensors installed on Android mobile devices to determine how fast cars were moving on each section of the road, and then generated routes using a complex algorithm.
Because the pool of data it received was much larger, Google's approach significantly improved the accuracy of the ETAs. Essentially, every Android device running Google Maps became a data source, and the more phones on the road, the more information Google received.
When Google bought Waze in 2013, the crowdsourcing approach got a major makeover. Waze, as you probably already know, takes a similar approach, but this time it relies on its large community for data. Users are the ones who report traffic jams, accidents, speed traps, and other issues, and the accuracy is greatly improved once again.
So, offline GPS maps had the lowest accuracy, whereas Google's crowdsourcing model significantly improved it by collecting data from various devices. The accuracy was eventually improved by allowing users to contribute manually with their own reports.
Google Maps currently employs a combination of the above-mentioned concepts. The navigation app uses both historical traffic patterns and real-time traffic data from Android phones, allowing Google to combine the two to provide the most accurate ETA possible.
Machine learning technology aids in the estimation of arrival time by attempting to predict how long it will take to arrive at a destination simply by guessing how traffic patterns will change while driving based on historical data.
So, given how far software has progressed, how come these apps are still not 100 percent accurate?
This method of combining historical traffic patterns with current conditions has two sides.
To begin with, the system can only make educated guesses about changing traffic conditions by estimating possible delays caused by traffic jams (the number of cars on the road and their average speed at a given time of day), but it cannot account for unforeseeable events such as accidents. Then, while collecting data from Android phones should theoretically be a faultless system, it's incredibly easy to break it down.
Last year, Berlin artist Simon Weckert created a fictitious traffic jam using no less than 99 Android smartphones running Google Maps. The navigation app was almost immediately updated with incorrect information, and drivers were eventually advised to avoid the street where the 99 phones were "parked," simply because the area was believed to be congested.
Overall, the system that makes these apps so accurate in the first place is the main reason they aren't always accurate.
Without a doubt, the parent companies behind them will continue to put more emphasis on this front, but it will be nearly impossible to achieve 100 percent accuracy as long as there is no way to predict when unpredictable events like accidents may occur.
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SOURCE: Yahoo News
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