  Identifying travelers' transportation modes is important in transportation
science and location-based services. It's appealing for researchers to leverage
GPS trajectory data to infer transportation modes with the popularity of
GPS-enabled devices, e.g., smart phones. Existing studies frame this problem as
classification task. The dominant two-stage studies divide the trip into
single-one mode segments first and then categorize these segments. The over
segmentation strategy and inevitable error propagation bring difficulties to
classification stage and make optimizing the whole system hard. The recent
one-stage works throw out trajectory segmentation entirely to avoid these by
directly conducting point-wise classification for the trip, whereas leaving
predictions dis-continuous. To solve above-mentioned problems, inspired by YOLO
and SSD in object detection, we propose to reframe change point detection and
segment classification as a unified regression task instead of the existing
classification task. We directly regress coordinates of change points and
classify associated segments. In this way, our method divides the trip into
segments under a supervised manner and leverage more contextual information,
obtaining predictions with high accuracy and continuity. Two frameworks,
TrajYOLO and TrajSSD, are proposed to solve the regression task and various
feature extraction backbones are exploited. Exhaustive experiments on GeoLife
dataset show that the proposed method has competitive overall identification
accuracy of 0.853 when distinguishing five modes: walk, bike, bus, car, train.
As for change point detection, our method increases precision at the cost of
drop in recall. All codes are available at
https://github.com/RadetzkyLi/TrajYOLO-SSD.
