  The operation and management of intelligent transportation systems (ITS),
such as traffic monitoring, relies on real-time data aggregation of vehicular
traffic information, including vehicular types (e.g., cars, trucks, and buses),
in the critical roads and highways. While traditional approaches based on
vehicular-embedded GPS sensors or camera networks would either invade drivers'
privacy or require high deployment cost, this paper introduces a low-cost
method, namely SenseMag, to recognize the vehicular type using a pair of
non-invasive magnetic sensors deployed on the straight road section. SenseMag
filters out noises and segments received magnetic signals by the exact time
points that the vehicle arrives or departs from every sensor node. Further,
SenseMag adopts a hierarchical recognition model to first estimate the
speed/velocity, then identify the length of vehicle using the predicted speed,
sampling cycles, and the distance between the sensor nodes. With the vehicle
length identified and the temporal/spectral features extracted from the
magnetic signals, SenseMag classify the types of vehicles accordingly. Some
semi-automated learning techniques have been adopted for the design of filters,
features, and the choice of hyper-parameters. Extensive experiment based on
real-word field deployment (on the highways in Shenzhen, China) shows that
SenseMag significantly outperforms the existing methods in both classification
accuracy and the granularity of vehicle types (i.e., 7 types by SenseMag versus
4 types by the existing work in comparisons). To be specific, our field
experiment results validate that SenseMag is with at least $90\%$ vehicle type
classification accuracy and less than 5\% vehicle length classification error.
