Transportation Mode Identification

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6150970/#CR28

💼 porté par

An open-source tool to identify active travel from hip-worn accelerometer, GPS and GIS data. We present a generalizable tool that identifies time spent stationary and time spent walking with very high precision, time spent in trains or vehicles with good precision, and time spent cycling with moderate precisionIn studies where both accelerometer and GPS data are available this tool complements analyses of physical activity, showing whether differences in PA may be explained by differences in travel mode. All code necessary to replicate, fit and predict to other datasets is provided to facilitate use by other researchers.

In summary we have developed a method to identify travel modes from accelerometer, GPS and GIS data for the ENABLE London study, which successfully predicts over 90% of points tested in a range of contexts. This method can be of use to complement existing analyses of physical activity, and assess active travel alongside physical activity. All code necessary to replicate the analysis, apply the method to other data-sets or predict from our models to other data-sets are provided, to facilitate usage by other researchers.

Mode Id : https://github.com/dprocter/modeid

Organizations using or interested in using the resource: Kisio, Cityway, Nokia

Contributor(s):

Tags: mode identification

Categories: Logiciel, Données

Theme: Traces de mobilité et des données associées, Logiciel Libre, Collectivité

Referent: Patrick Gendre

Challenge: Augmenter les connaissances partagées en cartographie et usages des véhicules et réseaux de transports

Key people to solicit: Patrick Gendre

Wealth sought:

Required skills:

Community of interest: Communauté autour des traces de mobilité et des données associées