Transportation mode detection using mobile phones and GIS information

By Leon Stenneth, Ouri Wolfson, Philip S. Yu, Bo Xu. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2011

487 citations recorded by the source corpus.

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Inferring human mobility patterns from taxicab location traces (2013, background): “We note that this approach does not apply to detection of personal trips [19], where the movement patterns are significantly different from that of the taxicabs.”
A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “[106] propose a transportation mode detection framework by integrating collected GPS information and knowledge of the underlying transportation network, where the transportation network information include real-time bus locations, spatial rail and spatial bus stop information.”
Accelerometer-based transportation mode detection on smartphones (2013, background): “Recent work has focused on decreasing energy consumption by requiring only sparse GPS data [3], introducing more effective graph-based postprocessing techniques [36], and improving the detection accuracy by fusing in external information on the real-time location of the transportation vehicles [30].”
Inferring transportation modes from GPS trajectories using a convolutional neural network (2018, methodology): “For example, Stenneth et al. (2011) exploited the GPS and GIS information for building their mode detection scheme while (Feng and Timmermans, 2013) combined GPS and accelerometer data to generate a model that outperforms GPS-only information.”