urban_traffic_flow_predictor

Overview

This model is a time-series transformer designed to predict urban traffic density and flow rates. It leverages historical sensor data from major metropolitan intersections to provide hourly forecasts for the upcoming 24-hour period.

Model Architecture

• Architecture: Informer (ProbSparse Attention mechanism)
• Input: 7 days (168 hours) of historical traffic volume, weather data, and holiday markers.
• Output: 24-hour continuous traffic flow forecast.
• Efficiency: Designed for long-sequence time-series forecasting with O(L log L) complexity.

Intended Use

• Smart City Planning: Optimizing traffic light synchronization based on predicted surges.
• Navigation Services: Providing predictive routing to avoid anticipated congestion.
• Public Transport: Adjusting bus and rail frequency in response to predicted road density.

Limitations

• Unforeseen Events: Cannot predict traffic changes caused by sudden accidents or emergency road closures.
• Geographic Specificity: Performance may degrade if applied to rural areas with significantly different traffic patterns than the training cities.
• Data Quality: Requires consistent hourly inputs; missing sensor data can significantly impact forecast accuracy.