           Departamento de Engenharia de Transportes Universidade Federal do Ceará    Department of Civil and Environmental Engineering The University of Tennessee Knoxville, TN, EUA    !  Department of Industrial and Systems Engineering Rutgers University, NJ, EUA  Freeway automatic incident detection (AID) has been extensively investigated over the last four decades. However, a recent nationwide survey in the United States concluded that the implementation of AID algorithms in traffic management centers is still very limited. The main reasons for this discrepancy are high false alarm rates and calibration complexity. This paper presents a self+learning, transferable algorithm that requires no calibration. The dynamic thresholds of the proposed algorithm are based on historical data of traffic, thus accounting for typical variations of traffic throughout the day to reduce false alarms rate. The proposed model performed better than existing algorithms found in the literature. " Detecção automática de incidentes em rodovias tem sido extensivamente investigada nas últimas quatro décadas. Contudo, uma pesquisa recente realizada nos EUA concluiu que a implantação desses algoritmos em centros de controle de tráfego ainda é bastante limitada. A principal razão para esta discrepância são as altas taxas de alarmes falsos e a complexidade de calibração dos algoritmos. Este artigo apresenta um algoritmo livre de calibração que pode ser aplicado em qualquer localidade. Os limites de decisão dinâmicos do algoritmo proposto são baseados nos dados históricos de tráfego, incorporando assim as variações típicas do fluxo ao longo do dia para reduzir os alarmes falsos. O modelo proposto obteve melhores resultados do que os algoritmos encontrados atualmente na literatura. # " Numerous automatic incident detection (AID) algorithms have been proposed in the literature over the last forty years. A myriad of algorithms of varied complexity, data requirements, and efficiency have been published in the literature. However, for desired levels of detection rate (DR), those algorithms yield unacceptably high false alarm rates (FARs) when implemented in the real world. In addition, AID studies verified with real data have been primarily based on computationally sophisticated methods whose extensive calibration and training efforts may discourage wide deployment by traffic management center (TMC) personnel. The fact that these models are typically configured to perform under very specific operational conditions for which they were calibrated makes their implementation not only difficult, but also inefficient when the operational condition drifts from the assumed norm. These problems have kept AID algorithms from being widely implemented, as it was found by a nationwide survey conducted in the United States involving 32 TMC (William and Guin, 2007), where it was concluded that only 12.5% of the centers claimed to have been using a fully functional AID algorithm. Another major problem of existing freeway AID models is universality (or transferability) (Abdulhai and Ritchie, 1999), which is the model’s ability to perform satisfactorily at different traffic scenarios/conditions with little or no recalibration efforts. The vast majority of the AID algorithms found in the literature are based on static (fixed) thresholds values for