Incident detection is a key component in real-time traffic management systems that allows efficient response plan generation and decision making by means of risk alerts at critical affected sections in the network. State-of-the-art incident detection techniques traditionally require: i) good quality data from closely located sensor pairs, ii) a minimum of two reliable measurements from the flow- occupancy-speed triad, and iii) supervised adjustment of thresholds that will trigger anomalous traffic states. Despite such requirements may be reasonably achieved in simulated scenarios, real-time downstream applications rarely work under such ideal conditions and must deal with low reliability data, missing measurements, and scarcity of curated incident labelled datasets, among other challenges. This paper proposes an unsupervised technique based on univariate timeseries anomaly detection for computationally efficient incident detection in real-world scenarios. Such technique is proved to successfully work when only flow measurements are available, and to dynamically adjust thresholds that adapt to changes in the supply. Moreover, results show good performance with low-reliability and missing data
