Bayesian optimized trip chain identification based on mobile signaling data
The spatio-temporal characteristic of mobile signaling data was analyzed to mitigate the impact of spatio-temporal uncertainty in the location information of mobile signaling data on trip identification. Area of interest (AOI) and base station locations were incorporated based on the spatio-temporal threshold-based method for identifying stay points. A method for identifying stay points using a variable-parameter sliding window was proposed. A trip chain model was established,and Bayesian multi-objective optimization was employed to determine the best parameters. The dynamic adjustment of spatio-temporal thresholds was realized to enhance recognition accuracy. Volunteers were organized to collect real travel GPS data and travel information labels serving as validation data and compared with the results after applying the model to the corresponding mobile phone signaling data in order to validate the effectiveness of above-mentioned method. The research results indicate that there are characteristic differences in the sampling of mobile signaling data between mobile and stationary states. The proposed method show reduced errors and improved recognition rates in terms of both generalization and optimal performance compared with the benchmark methods. There is an improvement ranging from 3％ to 26％ especially in recognition rate compared to other state-of-the-art algorithms.
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