Trip destination recognition based on individual memory effect and distance effect
A new travel destination recognition model was established through analyzing individual travel track data,mining the characteristics of individual travel history memory and the distance between individual location and potential destination.The model was tested using 62 880 trips of 200 anonymous individuals in Hangzhou.The location based service(LBS)data was preprocessed,the segmented travel data fragments were extracted for the purpose of activity,and the meshed individual historical destination set was obtained by GeoHash grid coding method.The training set and test set were constructed by using the random missing individual travel history track data,and the parameters of the model were calibrated by nonlinear least square method.Results show that the proposed model improves the recognition accuracy of travel destination.Comparing the recall rate,discount cumulative return and F1 score of different models,the proposed model was better than the Markov model,decision tree model and random forest model.The robustness of the proposed model was verified by the sensitivity analysis of data missing rate.
NETL
