System

A system comprising:

• circuitry configured to:
• receive, from a plurality of edge devices communicating over a packet-switched network, time-series sensor data structured as a multidimensional tensor having dimensions N by M, where N represents a number of observation timestamps and M represents a number of feature channels, the time-series sensor data comprising a temporal duration vector of float values in seconds, a device-count value of integer type, a speed-change vector, and a sudden-deceleration count;
• preprocess the time-series sensor data by applying anomaly detection to identify outlier values and noise removal to filter the time-series sensor data;
• generate, by inputting the preprocessed time-series sensor data into a convolutional neural network or a recurrent neural network trained using error backpropagation with a cross-entropy loss function, a classification output comprising a binary detection label, a continuous severity score in a range of 0.0 to 1.0, and a predicted occurrence time;
• generate, by inputting historical navigation data comprising GPS coordinate sequences, speed time-series vectors, day-of-week labels, time labels, and weather condition data into a Transformer-based time-series prediction model, optimal path data comprising a path identifier, an estimated traversal duration, a congestion score, and a recommendation reason;
• generate, by inputting event image tensors of dimension 224 by 224 by 3, location coordinate pairs, and structured reference data into an image recognition model comprising a ResNet architecture, and applying a precedent matching algorithm comprising a similarity calculation to the structured reference data, attribution scores for a plurality of entities and a reference identifier corresponding to a matching reference entry;
• and
• transmit the classification output, the optimal path data, and the attribution scores to at least one of the plurality of edge devices via the packet-switched network.

A method performed by circuitry of a system, comprising:

• receiving, from a plurality of edge devices communicating over a packet-switched network, time-series sensor data comprising a temporal duration vector and a device-count value indicating a number of peer edge devices detected within a predetermined spatial range;
• generating, by inputting the time-series sensor data into a first neural network comprising a convolutional neural network or a recurrent neural network, a classification output comprising a binary detection label and a continuous severity score;
• generating, by inputting historical navigation data into a trained time-series prediction model comprising a Transformer-based architecture, optimal path data comprising a path identifier, an estimated traversal duration, and a congestion score;
• generating, by inputting event image tensors and structured reference data into an image recognition model, attribution scores for a plurality of entities associated with an event;
• and
• transmitting the classification output, the optimal path data, and the attribution scores to at least one of the plurality of edge devices via the packet-switched network.