Method and system for delay prediction for scheduled public transport using multi architectural deep learning

Abstract

The present disclosure provides a system and method for delay prediction for scheduled public transport. A multi-architectural deep learning approach has been used to predict the delays of a queried vehicle in the scheduled public transport. For this, historical operational data is transformed into temporal, and spatiotemporal data. While, the spatial data is obtained from geographical information. The system uses different combinations of neural networks architectures. A regressor model uses three separate kinds of architecture. One component is the Fully Connected Neural Network (FCNN), which is good at learning from static features, the second is the Long Short Term Memory (LSTM) network which is good at learning from temporal features, and the third is the 3D Convolutional Neural Network (3DCNN) which is good at learning from spatiotemporal features. Learned encoding from each are fed to another FCNN to produce the predicted delay value.