Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks

Aim:

To predict the high demand need of pickup location for taxi services based on their previous history.

Synopsis:

The service industry is booming for the last couple of years and it is expected to grow in the near future. One of the important natures of the business is the serve the customer. To effectively utilize the resource at hand is the key factor. Businesses are using advanced technology to achieve this.

Proposed System:

Effective taxi dispatching can help both drivers and passengers to minimize the wait-time to find each other. Drivers do not have enough information about where passengers and other taxis are and intend to go. Therefore, a taxi center can organize the taxi fleet and efficiently distribute them according to the demand from the entire city. To build such a taxi center, an intelligent system that can predict the future demand throughout the city is required. Our system uses GPS location and other properties of the taxi like drop point, pickup point etc. to predict the future demand. An Recurrent Neural Networks (RNN) based model is trained with given history data. This model is used to predict the demand in different areas of the city.