  Identifying the distribution of users' transportation modes is an essential
part of travel demand analysis and transportation planning. With the advent of
ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach
for inferring commuters' mobility mode(s) is to leverage their GPS
trajectories. A majority of studies have proposed mode inference models based
on hand-crafted features and traditional machine learning algorithms. However,
manual features engender some major drawbacks including vulnerability to
traffic and environmental conditions as well as possessing human's bias in
creating efficient features. One way to overcome these issues is by utilizing
Convolutional Neural Network (CNN) schemes that are capable of automatically
driving high-level features from the raw input. Accordingly, in this paper, we
take advantage of CNN architectures so as to predict travel modes based on only
raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving,
and train. Our key contribution is designing the layout of the CNN's input
layer in such a way that not only is adaptable with the CNN schemes but
represents fundamental motion characteristics of a moving object including
speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the
quality of GPS logs through several data preprocessing steps. Using the clean
input layer, a variety of CNN configurations are evaluated to achieve the best
CNN architecture. The highest accuracy of 84.8% has been achieved through the
ensemble of the best CNN configuration. In this research, we contrast our
methodology with traditional machine learning algorithms as well as the seminal
and most related studies to demonstrate the superiority of our framework.
