  Prior art in traffic incident detection relies on high sensor coverage and is
primarily based on decision-tree and random forest models that have limited
representation capacity and, as a result, cannot detect incidents with high
accuracy. This paper presents IncidentNet - a novel approach for classifying,
localizing, and estimating the severity of traffic incidents using deep
learning models trained on data captured from sparsely placed sensors in urban
environments. Our model works on microscopic traffic data that can be collected
using cameras installed at traffic intersections. Due to the unavailability of
datasets that provide microscopic traffic details and traffic incident details
simultaneously, we also present a methodology to generate a synthetic
microscopic traffic dataset that matches given macroscopic traffic data.
IncidentNet achieves a traffic incident detection rate of 98%, with false alarm
rates of less than 7% in 197 seconds on average in urban environments with
cameras on less than 20% of the traffic intersections.
