# 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.