Objectives With the development of urbanization, people's travel behaviors have diversified. An in-depth understanding of human behavior and the modeling and prediction of individual travel behaviors are helpful in explaining several complex socio-economic phenomena, and are important in offering location-based services, transportation planning, and public safety. Individual travel behavior prediction is based on a deep understanding of human activity characteristics. In the era of mobile Internet, the online behavior of cyberspace is inseparable from the travel behavior of real space.
Methods This paper integrates individuals' mobile phone tracking data and Internet traffic data, and constructs a multi-model fusion model of individual travel location prediction on Markov and machine learning methods. Considering the classification probability of prediction results, an adaptive fusion strategy based on frequency distribution graph is proposed. The prediction results of Markov model and machine learning multi-classification model are merged together to obtain the final mobile phone user travel location prediction result.
Results This paper performs individual travel location prediction experiments on the basis of multi-source data. And the experiments show that the correct rate of the first result and the top three results of the multi-model fusion location prediction model based on histogram is respectively 74.59% and 94.19%, higher than the prediction accuracy of the basic model with the highest accuracy and the vote strategy.
Conclusions Under the prediction time granularity of 30 minutes, the individual travel location prediction is better.
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