A Ride-Hailing Company Supply Demand Prediction Using Recurrent Neural Networks, GRU and LSTM

In today's fast-paced and dynamic market, the ability to predict supply and demand accurately is a critical aspect of a business's success. Companies that can anticipate their customers' needs and efficiently allocate resources to meet them can gain a competitive advantage. With the advent of advanced data analytics and machine learning algorithms, businesses can now leverage large datasets to make informed decisions about their operations. Snapp is a ride-hailing company that operates in Tehran, Iran. The company has a large fleet of vehicles and a large number of users. In order to ensure that there are enough vehicles available to meet the demand, Snapp needs to be able to predict the demand for rides in different parts of the city. Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs) are all powerful types of neural networks designed to handle sequential data. RNNs are a type of artificial neural network designed to recognize patterns in sequences of data, such as time series or text. They are known for their 'memory' function, which can carry information across lengthy sequences, thus making them suitable for tasks such as predicting time series data, speech recognition, or machine translation. In this research, we collect historical data on Snapp Inc.'s ride-hailing services, including pickup and drop-off locations, dates, times, and the number of ride requests in each region. We preprocess the data, train the models, and evaluate their performance using various metrics, including Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).