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.

Abstract: State of the art approaches for training chained neural network models for delay prediction train the data models using only real data and not predicted data. Such models when used in a chained way leads to worse results as they are not exposed to predicted data during training. This leads to the model prediction errors showing sharp increase as the models tries to predict for subsequent stations past the immediate station. Embodiments disclosed herein provide a method and system for training of chained neural networks for delay prediction in transit networks. In this approach, a chained neural network model used by the system is trained such that data containing a mix of real data and predicted data is used for training each data model in a sequence of data models in the chained neural network model.

Abstract: An Extended Kalman filter (EKF) is a general nonlinear version of the Kalman filter and an approximate inference solution which uses a linearized approximation performed dynamically at each step and followed by linear KF application. Extended Kalman Filter involves dynamic computation of the partial derivatives of the non-linear functions system maps with respect to the input or current state. Existing approaches have failed to perform recursive computations efficiently and exactly for such scenarios. Embodiments of the present disclosure efficient forward and backward recursion-based approaches wherein a forward pass is executed through a feed-forward network (FFN) to compute a value that serves as an input to jth node at a layer l from a plurality of network layers of the FFN and partial derivatives are estimated for each node associated with various network layers in the FFN. The feed-forward network is used as state and/or observation equation in the EKF.

Abstract: State of the art approaches for training chained neural network models for delay prediction train the data models using only real data and not predicted data. Such models when used in a chained way leads to worse results as they are not exposed to predicted data during training. This leads to the model prediction errors showing sharp increase as the models tries to predict for subsequent stations past the immediate station. Embodiments disclosed herein provide a method and system for training of chained neural networks for delay prediction in transit networks. In this approach, a chained neural network model used by the system is trained such that data containing a mix of real data and predicted data is used for training each data model in a sequence of data models in the chained neural network model.

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.

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.

Abstract: An Extended Kalman filter (EKF) is a general nonlinear version of the Kalman filter and an approximate inference solution which uses a linearized approximation performed dynamically at each step and followed by linear KF application. Extended Kalman Filter involves dynamic computation of the partial derivatives of the non-linear functions system maps with respect to the input or current state. Existing approaches have failed to perform recursive computations efficiently and exactly for such scenarios. Embodiments of the present disclosure efficient forward and backward recursion-based approaches wherein a forward pass is executed through a feed-forward network (FFN) to compute a value that serves as an input to jth node at a layer l from a plurality of network layers of the FFN and partial derivatives are estimated for each node associated with various network layers in the FFN. The feed-forward network is used as state and/or observation equation in the EKF.

Abstract: Accurate estimation of the trajectory of a vehicle by selecting optimal number of GPS data points and a shortest path technique applied for estimation is important and crucial. Method and system for estimating a trajectory from GPS data points is described. The method disclosed utilizes a plurality of GPS data points of a vehicle, an existing road map and a set of equal time intervals obtained by dividing an elapsed time during movement of the vehicle. Each GPS data point is associated to a time interval and a set of candidate points are mapped to each GPS data point correspondingly. A set of possible paths are determined between the set of candidate points in each time interval to estimate the trajectory of the vehicle using one of a shortest path technique and an edit distance technique.

Abstract: Accurate estimation of the trajectory of a vehicle by selecting optimal number of GPS data points and a shortest path technique applied for estimation is important and crucial. Method and system for estimating a trajectory from GPS data points is described. The method disclosed utilizes a plurality of GPS data points of a vehicle, an existing road map and a set of equal time intervals obtained by dividing an elapsed time during movement of the vehicle. Each GPS data point is associated to a time interval and a set of candidate points are mapped to each GPS data point correspondingly. A set of possible paths are determined between the set of candidate points in each time interval to estimate the trajectory of the vehicle using one of a shortest path technique and an edit distance technique.