  Events deviating from normal traffic patterns in driving, anomalies, such as
aggressive driving or bumpy roads, may harm delivery efficiency for
transportation and logistics (T&L) business. Thus, detecting anomalies in
driving is critical for the T&L industry. So far numerous researches have used
vehicle sensor data to identify anomalies. Most previous works captured
anomalies by using deep learning or machine learning algorithms, which require
prior training processes and huge computational costs. This study proposes a
method namely Anomaly Detection in Driving by Cluster Analysis Twice (ADDCAT)
which clusters the processed sensor data in different physical properties. An
event is said to be an anomaly if it never fits with the major cluster, which
is considered as the pattern of normality in driving. This method provides a
way to detect anomalies in driving with no prior training processes and huge
computational costs needed. This paper validated the performance of the method
on an open dataset.
