This paper proposes and assesses the effectiveness of monitoring vehicular traffic anomalies using
microscopic traffic variables, namely relative speed and inter-vehicle spacing. We present an
algorithm that detects transient changes in traffic patterns using microscopic traffic variables. In
particular, we show that when applied to real-world scenarios, our algorithm can use the variance
of statistics of relative speed to detect traffic anomalies and precursors to non-recurring traffic congestion.
The performance of the proposed algorithm is also assessed using a microscopic traffic
simulation environment, where we show that with minimum prior knowledge, the proposed algorithm
has comparable performance to an ideally placed loop detector monitoring the standard
deviation of speed. The algorithm also performs very well even when the microscopic traffic variables
are available only from a fraction of the complete population of vehicles.