• S. Revathi
• Sanjay Bitra
• Saba Zehra Syed

The taxi administration sector has been booming in recent years, and it is expected to continue to grow shortly. Cab drivers must decide where to stop for passengers so that they can pick someone within a limited waiting time. Travelers also like a quick cab service when they need it.The taxi administration's control center determines the most congested area of focus.Cabs were spread over scattered demography in the current framework,over a larger area without a time-based inhabited zone such as an airport,commercial zones, school zones, train stops, and so forth.Effective taxi assignments can assist drivers and passengers in reducing the time it takes to locate one another. Future interest can be predicted in the proposed framework using a Recurrent Neural Network-based model that can be developed with historical data. By resolving taxi accessibility, it will be able to service more clients in a brief period. The data collection includes the taxi's GPS location as well as various taxi characteristics such as slanted edge, pickup spot, and so on. This model is used to predict interest in various areas of the city over a given time frame.

taxi-passenger demand,Global Positioning System (GPS) data, Recurrent Neural Networks.

"Using Recurrent Neural Network for Taxi Demand Prediction ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 11, page no.d54-d60, November-2021, Available :http://www.jetir.org/papers/JETIR2111308.pdf

"Using Recurrent Neural Network for Taxi Demand Prediction ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 11, page no. ppd54-d60, November-2021, Available at : http://www.jetir.org/papers/JETIR2111308.pdf