| # 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. |