|
Abstract:
|
The crime problems become critical issues
for national security especially the security of border
and intelligent transportation systems (ITSs). These
affect the economy, investment, tourism, and society.
As a result, the automatic suspect vehicle detection
emerges as one of effective tools to tackle the problems.
However, the traditional process normally uses criminal
vehicle data in blacklist comparing with vehicle data
gathering from various sensors. This comparison is not
effective and accurate that might be from not up-todate
data in the blacklist. Sometimes the blacklist is
not available. This paper proposes the criminal behavior
analysis method to detect suspect vehicles that are
potentially involved in criminal activity. It must not rely
on the blacklist. The analysis is conditional on journey
path and the involvement of criminal activities. In additional,
public officials believe that the suspect vehicle
will choose the journey path without a checkpoint.
Therefore, we used the journey path analysis techniques
together with the association rule mining to analyze
such criminal behavior. From extensive experiments,
the results show that the proposed method can increase
the suspect detection accuracy rate 17.24% beyond the
traditional counterpart. |