How Machine Learning shaped Google Maps that we know today.
In the ever-growing age of information technology, machine learning and deep learning are becoming more and more prominent in terms of data analytics. For higher accuracy and precision, Data is required in large quantities. To collect this data billions of sensors are continuously working for this sole purpose, eventually involving the Internet of things.
Many of the major products in the market are dependent on machine learning and IoT devices. ‘Google maps’ is one of the navigation application that requires a lot of data and smart algorithm techniques to work accurately.
Google Maps Data Provides Navigational Advantage
‘Google Maps’ extensive data allows it to offer better directions than most of the other Navigation systems prevalent so far.
The survey suggests that people primarily use it as their preferred navigation app because they believe it offers better directions (25%).
In the majority of countries, google maps is the default feature that is helping people and industries to navigate through their paths.
Prominent features like satellite imagery, street maps, 360° interactive panoramic views of streets, real-time traffic conditions, and route planning for traveling by foot, car, bicycle, and air (in beta), or public transportation, etc. are fulfilling numerous and varying needs of users in a very effective manner.
All these features are being developed using machine learning algorithms. So let us have a closer look at how these features provide accurate results and improving the quality of service.
Multi-Stop Route Optimization feature of google maps uses the three major shortest path algorithms: Bellman Ford’s Algorithm, Dijkstra’s Algorithm, and Floyd–Warshall’s Algorithm and trace the shortest path providing the user the information for the best route for a multi-stop journey. Very beneficial to the businesses involved in very large scale logistics operations. They can very easily manage multiple deliveries at different points, saving a lot of time and money.
Real Time Traffic Conditions
This feature is an addition to the route navigation feature. To make it more effective google provides real-time traffic conditions. It uses the historical data of road traffic and the active mobile phones available on that particular route to identify the traffic. ‘Waze’ a subsidiary of Google added additional parameters like road accidents, police check posts, etc. to the real-time traffic control feature providing an even more optimal path to the user.
Sensors present in smartphones provide more relevant data like live location etc helped maps to identify the speed of the user, destined location.
With all the collected data, it is just simple mathematics to calculate the time and predict the shortest path to reach the destination with help of a machine learning model.
Now the question will arise that how google map collects such a large amount of geographic data?
‘Keyhole’ another subsidiary of Google performs this task. It collects satellite images at different positions. This huge database embraces Google Maps with an additional zoom-in and zoom-in feature.
Again it uses Deep learning to process this massive information.
Apart from that Google also uses its own special purpose vehicles that are equipped with mapping sensors and run on the roads continuously with the sole purpose of collecting navigational data on roads.
They have driven up to five million miles till now. Each drive generates two kinds of data: First is the actual route the cars have traversed and the other is Photos. With this google provides another feature that is street view.
The significant thing about the photographs in Street View is that Google can run algorithms that extract the traffic signs and can even paste them onto the deep map within their Atlas tool.
Google also collects the data from various users themselves such as images of nearby places and asks multiple people to identify and confirm them for enhancing the street view.
So far, it appears that machine learning will be continuously playing an active role in future developments yet to take place in shaping the way we know the world and the way we live in it.
