公开(公告)号：US20240112096A1

公开(公告)日：2024-04-04

申请号：US18455045

申请日：2023-08-24

Applicant: Tata Consultancy Services Limited

Inventor： ROHITH REGIKUMAR ,  PRIYANGA KASTHURIRAJAN ,  RAJESH JAYAPRAKASH ,  ARVIND RAMANUJAM

IPC: G06Q10/047 ,  G06N3/091

CPC classification number: G06Q10/047 ,  G06N3/091 ,  G06Q50/30

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.

公开(公告)号：US20230401442A1

公开(公告)日：2023-12-14

申请号：US18318380

申请日：2023-05-16

Applicant: Tata Consultancy Services Limited

Inventor： KRISHNAN SATHEESH ,  ROHITH REGIKUMAR ,  ARVIND RAMANUJAM ,  RAJESH JAYAPRAKASH

IPC: G06N3/08 ,  G06Q50/30 ,  G06F16/9537 ,  G06Q10/04

CPC classification number: G06N3/08 ,  G06Q50/30 ,  G06F16/9537 ,  G06Q10/04

Abstract: The present disclosure predicts a delay associated with a vehicle. Conventional methods are mainly mathematical based and machine learning based networks are not predicting delay accurately. Initially, the present disclosure Initially, the system receives a user query comprising an expected delay of a target vehicle in at least one target station. Further, a real time data associated with the user query in a predefined horizon is obtained. Further, a spatial feature vector, a temporal feature vector and spatiotemporal features are extracted based on the real time data using a feature extraction technique. Finally, the expected is predicted based on the plurality of features using a trained adversarial regression model, wherein the trained adversarial regression model comprises a critic network and a regressor network. The regressor network is trained with a plurality of architectures and a best architecture with minimum Mean Absolute Error (MAE) is selected for delay prediction.