Abstract: The present invention discloses a method for identifying travel classification based on smartphone travel surveys. The method takes individual volunteers as the object, GPS information collected by smartphones and instant recall verification features of respondents as the data source. First, travel features, individual features, and family features are determined by GPS data and questionnaire information of volunteers. Then, a training data set is determined by the equal proportion method. Finally, an artificial neural network combined with a particle swarm optimization algorithm is used to detect travel classification from the survey data. The method described in the invention can realize automatic identification of six travel classifications. It is conducive to replace the traditional resident travel survey with the advanced survey method under the environment of big data. At the same time, it provides data foundation for urban management and planning.

Abstract: The present invention discloses a method for identifying travel classification based on smartphone travel surveys. The method takes individual volunteers as the object, GPS information collected by smartphones and instant recall verification features of respondents as the data source. First, travel features, individual features, and family features are determined by GPS data and questionnaire information of volunteers. Then, a training data set is determined by the equal proportion method. Finally, an artificial neural network combined with a particle swarm optimization algorithm is used to detect travel classification from the survey data. The method described in the invention can realize automatic identification of six travel classifications. It is conducive to replace the traditional resident travel survey with the advanced survey method under the environment of big data. At the same time, it provides data foundation for urban management and planning.