Novel traffic congestion detection algorithms for smart city applications

Summary Traffic congestion detection (TCD) techniques are becoming a critical component of traffic management systems. They can be considered pre‐step to address jam problems, providing useful input for systems predict and avoid congestions' unwanted effects. In this research article, two novel real‐time algorithms proposed, the TCD algorithm ensemble based (EB‐TCD) algorithm. detect on one feature (i.e., speed, occupancy, or flow). first cleans preprocesses data. After, it computes absolute derivative each sample in determine its anomaly score. Then, likelihood is computed classify as an normal sample. On other hand, EB‐TCD utilizes information contained multiple features parallel by proposing technique combine scores coming out from different unified score separately. votes not (from corresponding feature's point view). weight determined studying dissimilarity between their scores. These weights show how much explains behavior. Finally, results combined form The proposed statistical‐based able changes patterns while mitigating effect noise Moreover, algorithms' parameters were tuned tailored fast detection, which crucial since accelerates decision‐making. computational complexity analysis has shown simplicity algorithms. Furthermore, evaluation have that outperform widespread methods literature terms false alarm rate time keeping at same high level.