Real-Time Taxi-Passenger Prediction with L-CNN

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Aim:

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

Existing System:

TAXI drivers need to decide where to wait for passengers in order to pick up someone as soon as possible. Passengers also prefer to quickly find a taxi whenever they are ready for pickup. The control center of the taxi service decides the busy area to be concentrated. Sometimes the taxi were scattered across the larger area missing the time based busy area like Airport, Business area, school area, Train stations etc,.

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. A Recurrent Neural Networks (R-CNN) based model is trained with given history data. This model is used to predict the demand in different areas of the city.