  The future of transportation is driven by the use of artificial intelligence
to improve living and transportation. This paper presents a neural
network-based system for driver identification using data collected by a
smartphone. This system identifies the driver automatically, reliably and in
real-time without the need for facial recognition and also does not violate
privacy. The system architecture consists of three modules data collection,
preprocessing and identification. In the data collection module, the data of
the accelerometer and gyroscope sensors are collected using a smartphone. The
preprocessing module includes noise removal, data cleaning, and segmentation.
In this module, lost values will be retrieved and data of stopped vehicle will
be deleted. Finally, effective statistical properties are extracted from
data-windows. In the identification module, machine learning algorithms are
used to identify drivers' patterns. According to experiments, the best
algorithm for driver identification is MLP with a maximum accuracy of 96%. This
solution can be used in future transportation to develop driver-based insurance
systems as well as the development of systems used to apply penalties and
incentives.
