To investigate different traffic modes for resident’s travel trajectories

a classification model was constructed based on Light Gradient Boosting Machine (LightGBM) to categorize transportation modes according to resident’s GPS trajectories. First

basic trajectory features were extracted

and then more features were obtained using geographic information of public transit network (i.e.

Fréchet distance). Subsequently

the features were normalized and screened by the decision tree model. Finally

the screened features were trained and predicted by the model

and a stable prediction result was attained with a five-fold cross-validation method. Results show that geographic information of public transit network could optimize the model’s prediction accuracy. The proposed GPS trajectory recognition method achieved an accuracy of about 90%

which is superior to other machine learning classification models.