Method and system for adversarial multi-architecture based delay prediction in scheduled transportation networks

A system comprising:

• at least one memory storing programmed instructions;
• one or more Input/Output (I/O) interfaces;
• and one or more hardware processors operatively coupled to the at least one memory, wherein the one or more hardware processors are configured by the programmed instructions to:
• receive a user query with respect to an expected delay of a target vehicle pertaining to a scheduled transportation network, in at least one target station, wherein the scheduled transportation networks include a plurality of stations and a plurality of vehicles;
• obtain a real time data associated with the user query in a predefined horizon using a real time data acquisition system;
• extract a plurality of spatial features based on the real time data, the plurality of stations in the scheduled transportation networks, the plurality of vehicles in the scheduled transportation networks, and the predefined horizon, using a feature extraction technique;
• extract a plurality of temporal features based on the real time data, the plurality of stations, and the predefined horizon, using the feature extraction technique, wherein the plurality of temporal features are segmented based on a plurality of vehicle categories, wherein the plurality of vehicle categories comprises a route based vehicle, a trip based vehicle, and a current vehicle;
• extract a plurality of spatiotemporal features based on the plurality of stations, the plurality of vehicles, and the real time data, using the feature extraction technique;
• and
• predict the expected delay of the target vehicle in at least one target station based on the spatial features, the temporal features and the spatiotemporal features using a trained adversarial regression model, wherein the trained adversarial regression model comprises a critic network and a regressor network, wherein the regressor network is trained using a plurality of regression architectures, wherein the plurality of regression architectures are trained simultaneously and, wherein one architecture with a minimum Mean Absolute Error (MAE) is selected from among the plurality of regression architectures is selected for the expected delay prediction.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving a user query with respect to an expected delay of a target vehicle pertaining to a scheduled transportation network, in at least one target station, wherein the scheduled transportation networks include a plurality of stations and a plurality of vehicles;
• obtaining a real time data associated with the user query in a predefined horizon using a real time data acquisition system;
• extracting a plurality of spatial features based on the real time data, the plurality of stations in the scheduled transportation networks, the plurality of vehicles in the scheduled transportation networks, and the predefined horizon, using a feature extraction technique;
• extracting a plurality of temporal features based on the real time data, the plurality of stations, and the predefined horizon, using the feature extraction technique, wherein the plurality of temporal features are segmented based on a plurality of vehicle categories, wherein the plurality of vehicle categories comprises a route based vehicle, a trip based vehicle, and a current vehicle;
• extracting a plurality of spatiotemporal features based on the plurality of stations, the plurality of vehicles, and the real time data, using the feature extraction technique;
• and
• predicting the expected delay of the target vehicle in at least one target station based on the spatial features, the temporal features and the spatiotemporal features using a trained adversarial regression model, wherein the trained adversarial regression model comprises a critic network and a regressor network, wherein the regressor network is trained using a plurality of regression architectures, wherein the plurality of regression architectures are trained simultaneously and, wherein one architecture with a minimum Mean Absolute Error (MAE) is selected from among the plurality of regression architectures is selected for the expected delay prediction.