  Monitoring the dynamics of traffic in major corridors can provide invaluable
insight for traffic planning purposes. An important requirement for this
monitoring is the availability of methods to automatically detect major traffic
events and to annotate the abundance of travel data. This paper introduces a
machine learning based approach for reliable detection and characterization of
highway traffic congestion events from hundreds of hours of traffic speed data.
Indeed, the proposed approach is a generic approach for detection of changes in
any given time series, which is the wireless traffic sensor data in the present
study. The speed data is initially time-windowed by a ten-hour long sliding
window and fed into three Neural Networks that are used to detect the existence
and duration of congestion events (slowdowns) in each window. The sliding
window captures each slowdown event multiple times and results in increased
confidence in congestion detection. The training and parameter tuning are
performed on 17,483 hours of data that includes 168 slowdown events. This data
is collected and labeled as part of the ongoing probe data validation studies
at the Center for Advanced Transportation Technologies (CATT) at the University
of Maryland. The Neural networks are carefully trained to reduce the chances of
over-fitting to the training data. The experimental results show that this
approach is able to successfully detect most of the congestion events, while
significantly outperforming a heuristic rule-based approach. Moreover, the
proposed approach is shown to be more accurate in estimation of the start-time
and end-time of the congestion events.
