  Transportation mode detection with personal devices has been investigated for
over ten years due to its importance in monitoring ones' activities,
understanding human mobility, and assisting traffic management. However, two
main limitations are still preventing it from large-scale deployments: high
power consumption, and the lack of high-volume and diverse labeled data. In
order to reduce power consumption, existing approaches are sampling using fewer
sensors and with lower frequency, which however lead to a lower accuracy. A
common way to obtain labeled data is recording the ground truth while
collecting data, but such method cannot apply to large-scale deployment due to
its inefficiency. To address these issues, we adopt a new low-frequency
sampling manner with a hierarchical transportation mode identification
algorithm and propose an offline data labeling approach with its manual and
automatic implementations. Through a real-world large-scale experiment and
comparison with related works, our sampling manner and algorithm are proved to
consume much less energy while achieving a competitive accuracy around 85%. The
new offline data labeling approach is also validated to be efficient and
effective in providing ground truth for model training and testing.
