Travel purpose identification method based on mobile phone signaling data

Abstract

The invention discloses a travel purpose identification method based on mobile phone signaling data, and the method comprises the steps: carrying out map matching of the signaling data, starting from the identification of a stop point, carrying out the identification of the stop point based on an ST-DBSCAN space-time density clustering algorithm, and combining with a heuristic algorithm. Parameters of a spatio-temporal clustering algorithm are mined depending on user mobile phone signaling data with labels, meanwhile, speed characteristics of travelers are considered, and the fineness of stay point recognition is improved. Trajectory spatial-temporal features, personal attributes and traffic facility built environment features of user travel are obtained through feature extraction, and the features are abstracted as nodes. A directed arc is obtained through a constraint-based Bayesian network structure learning algorithm, Bayesian network modeling is preliminarily completed, and a Bayesian network probability model is perfected through a rule heuristic modeling method by taking a travel purpose and commuting characteristics as deductive reasoning objects. When the travel purpose identification is carried out, the travel characteristics are obtained through the mobile phone signaling data of the user, and the travel purpose probability result of the traveler can be obtained.

Description

technical field [0001] The invention relates to the field of electronic technology and the field of communication, in particular to a multi-mode travel purpose identification method based on mobile phone signaling data. Background technique [0002] Travel purpose prediction is one of the fundamental problems in the field of transportation planning. With the continuous development of Internet technology, travel purpose prediction is very important for the development of emerging transportation systems (such as MaaS, carpooling, etc.). The development of mobile devices and wireless communication technology has greatly facilitated the acquisition of residents’ travel data. The huge number of mobile phone users has brought nearly a full sample of residents’ travel data and can track the location information of travelers in real time. The location information is the location information of the base station communicating with the user. How to use low-cost communication data to ...

AI Technical Summary

Problems solved by technology

How to use low-cost communication data to efficiently identify residents' travel purposes is a very challenging problem

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