Probabilistic data-driven approach for real-time screening of freeway traffic data

Freeway traffic surveillance systems currently collect large amounts of traffic data, sometimes a few gigabytes per day, to support various critical traffic management center functions such as incident detection, travel time and delay estimation, and congestion management. Reliable traffic information, however, requires applying quality control measures to the collected traffic data before archiving, dissemination to the public, or use in relevant applications. This paper presents a probabilistic data-driven methodology for real-time screening of freeway loop detector data. Two complementary approaches were developed to detect abrupt temporal changes in the traffic parameters, as well as possible inconsistencies among each pair of the three traffic parameters. A real-time data screening algorithm was devised to operate in three steps. An illustrative example is presented to explain how the algorithm can be applied to real-time data screening and how observations can be diagnosed for the most likely erroneous parameters.