Short duration traffic flow prediction using kalman filtering

The research examined predicting short-duration traffic flow counts with the Kalman filtering technique (KFT), a computational method. Short-term prediction is an important tool for operation in management and transportation system. short-term value results can be used travel time estimation by route guidance advanced traveler information systems. Though KFT has been tested homogeneous traffic, its efficiency heterogeneous yet to investigated. was conducted on Mirpur Road Dhaka, near Sobhanbagh Mosque. stream contains mix of which implies uncertainty prediction. propositioned method executed Python using pykalman library. library mostly database modeling framework, addresses uncertainty. data derived from three-hour count vehicle. According Geometric Design Standards Manual published Roads Highways Division (RHD), Bangladesh 2005, translated into equivalent passenger car unit (PCU). PCU obtained five-minute aggregation then utilized as suggested model's dataset. model mean absolute percent error (MAPE) 14.62, indicating that forecast reasonably well. root square (RMSPE) shows 18.73% accuracy less than 25%; hence acceptable. developed R2 0.879, it explain 87.9 variability If were collected over more extended period time, could closer 1.0.