  Detecting travel modes from global navigation satellite system (GNSS)
trajectories is essential for understanding individual travel behavior and a
prerequisite for achieving sustainable transport systems. While studies have
acknowledged the benefits of incorporating geospatial context information into
travel mode detection models, few have summarized context modeling approaches
and analyzed the significance of these context features, hindering the
development of an efficient model. Here, we identify context representations
from related work and propose an analytical pipeline to assess the contribution
of geospatial context information for travel mode detection based on a random
forest model and the SHapley Additive exPlanation (SHAP) method. Through
experiments on a large-scale GNSS tracking dataset, we report that features
describing relationships with infrastructure networks, such as the distance to
the railway or road network, significantly contribute to the model's
prediction. Moreover, features related to the geospatial point entities help
identify public transport travel, but most land-use and land-cover features
barely contribute to the task. We finally reveal that geospatial contexts have
distinct contributions in identifying different travel modes, providing
insights into selecting appropriate context information and modeling
approaches. The results from this study enhance our understanding of the
relationship between movement and geospatial context and guide the
implementation of effective and efficient transport mode detection models.
