  Due to increasing urban population and growing number of motor vehicles,
traffic congestion is becoming a major problem of the 21st century. One of the
main reasons behind traffic congestion is accidents which can not only result
in casualties and losses for the participants, but also in wasted and lost time
for the others that are stuck behind the wheels. Early detection of an accident
can save lives, provides quicker road openings, hence decreases wasted time and
resources, and increases efficiency. In this study, we propose a preliminary
real-time autonomous accident-detection system based on computational
intelligence techniques. Istanbul City traffic-flow data for the year 2015 from
various sensor locations are populated using big data processing methodologies.
The extracted features are then fed into a nearest neighbor model, a regression
tree, and a feed-forward neural network model. For the output, the possibility
of an occurrence of an accident is predicted. The results indicate that even
though the number of false alarms dominates the real accident cases, the system
can still provide useful information that can be used for status verification
and early reaction to possible accidents.
