Dynamic Demand Forecasting System for E-bikes as Last Mile Connectivity

Predicting bike user count throughout the day contributes to providing a prominent solution for maintaining required bike availability at stations before a requirement arises. The solution puts forward a bike demand prediction model based on a multivariate supervised LSTM deep learning algorithm that considers external weather parameters with the least amount of training data. The prediction model’s components include user-centric parameters such as city name, station name, and the number of forecasting days. Once the user hits the user-centric parameters, the model provides features such as forecasting dates, day parts, and bike user counts.