Traffic incidents which commonly result fromtraffic accidents, anomalous construction events and inclementweather can cause a wide range of negative impacts on urbanroad networks. Developing a high efficiency and transferabletraffic incident detection system plays an important role insolving the imbalance caused by traffic incidents betweentraffic demand and capacity. However, the existing literatureon transferability of traffic incident detection is rather limited.The objective of this paper is to provide an accurateand transferable incident detection approach based on therelationship between traffic variables and observed trafficincidents, in particular at a network level. We propose a deeplearning based method which has been calibrated using partof the collected traffic variables and the pre-assigned trafficincidents and then tested against the rest of the dataset. Theproposed method is compared to other benchmarks commonlyused in traffic incident detection, in terms of detection rate, falsepositive rate, f-measurement and detection time. The resultsindicate that the proposed method is significantly promising fortraffic incident detection with high accuracy and transferabilitycompared to the more widely used techniques in the literature.