Fare-Pilot: A new AI app for Uber/Lyft Drivers


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Fare-Pilot: a new AI app for ride-share drivers

AI and machine learning are already being used in many applications and now its here for ride-share drivers. This article is a review of the Fare-Pilot app, and we hope after you read this, you will seriously consider downloading it and using it to improve your daily routine.

Fare-Pilot

This company was started up by ride-share drivers that came together to create a new app that provides up to date busy areas in cities that it is deployed in. Similar to Waze, this system uses drivers to generate feedback that provides up to date information and the machine learning algorithm extrapolates this data and sends projections and predictions on a map.

This small team of developers, 20 to be exact is constantly growing and seeking to expand its professional team, all the information about them can be found on their site: https://www.farepilot.com/about-us

The more users that use it, the cleverer it gets, and the more accurate its predictors will become. It is also connected to many online input sources, such as online ticket sales site, events sites, traffic sites and more. The data mining process constantly fills the machine with data that is supported and supplemented by the drivers.

The driver's initiative

When a driver drops off passengers they have a few options to choose from, and that all depends on where they are on the map. They can stay put, wait for more fares in a busy zone, they can drive to where they think is a busy zone or they can use destination filter, Uber Pool or Lyft Line or just drive to the nearest airport.

What Fare-Pilot does is help with this decision making process. Fare-Pilot provides the driver with three options that will most probably give a driver a ride within fifteen minutes. These results are based on the way the systems AI algorithm has analyzed all the local data and projected a possible scenario.

Drivers are an integral part of the process, and without their interactivity and support, the system would not succeed. It is actually refreshing to see how many drivers do partake in the initiative since it will provide an exceptional service that is great as an Uber/Lyft app add-on.

The Map

Fare-Pilot provides an interactive map that the driver can hover over and look at all the hotspots on the screen. If the driver is close to an emerging hotspot, colored orange or in a hotspot colored red, then they should stay in the area. If not, then choosing a direction will help them reach a ride. A driver can use this mapping system to set a direction and then use Uber's destination filter while driving to that destination. This will radically increase the chances of a pickup.

The Fare-Pilot hotspot is made up of lots of bits of information that collate together and create the hotspot zones on the maps. This information is bolstered by drivers input as well.

This is a useful map to have alongside the Uber and Lyft maps, comparing both to see if they match. Eventually, it will be possible to see, which company has the most accurate map of all.

Information Mining

By receiving online streams of data from apps such as google maps, Waze, weather and more, Fare-Pilot can create an astute traffic map with weather conditions built in. It calculates the traffic and also assesses whether traffic is due to surges. It can also predict surges based on the historical data and show where it is best to drive when other conditions can impede on the ride. Weather information is also important to ascertain when days will be more or less, for instance, rainy and extremely hot weather days generate more demand.

Events and Ticket Sales

The system is connected online gathering from many sources, one of which are tracking ticket sales and events, so that an accurate picture of how many people will be at any point in any given moment of time will add to the accuracy of the app.

Expense Savings

Another feature that is offered with Fare-Pilot is an expense calculator and evaluator that offers better deals and ideas for saving on expenses.

The system provides an in-app survey for your expenses. After you have inputted every detail asked, the app will analyze the data against local information and provide you with alternatives that will reduce your ride-share spending.

Machine Learning

When drivers reach destinations and predictions are correct, the driver is expected to press a thumbs up or thumbs down which directs the software to consider the implications of success and failure of its predictions. This interactivity is very important for the process to self-learn. Data mining and sourcing incoming streams of information will not provide a positive result without a drivers affirmation that the information provided was correct.

Limitations

Fare-Pilot currently offers it service in London and Los-Angeles only. These are pilot cities, that will provide a basis for predicting success as well as helping to develop and fine-tune the various features of the app before going global.

Bottom Line

The app is simple to use. The process it uses is understandable. It is designed by people that knew and know what is needed. It offers a service that complements Uber and Lyft but does not compete with them.

The software is compatible to both iPhone OS9 and above and to Android.

There is plenty of room to grow in, and also to realize that this kind of app will work for and against drivers. Once self-driving cars are in place, it won't be long before this app and apps like it are adapted to the self-driving cars AI, and they will work in cohesion, arranging the distribution of cars in a holistic and efficient manner.


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