Traffic trip mode splitting method based on mobile phone data

Abstract

The invention discloses a method for splitting traffic trip modes on the basis of mobile phone data.The method comprises the steps that according to mobile phone behavior characteristics, on the basis of the mobile phone data, a home address community and a work community of a mobile phone user are predicted, a commuting O-D matrix between the home address community and the work community of the mobile phone user is constructed by means of communication records, produced in the home address community and the work community, of the mobile phone user on working days; the commuting trip time, trip distance and trip speed are calculated by means of time information and position information which are contained in the mobile phone data, a speed and distance rank algorithm is built through trip characteristics of different trip modes, urban traffic network information data is introduced, a Logit model based on the trip time is built to achieve large scale urban traffic trip mode splitting, and a novel economical and feasible method is provided for traffic demand prediction among the traffic modes.

Description

technical field [0001] The invention belongs to the field of public transportation data processing, and relates to a method for dividing traffic travel modes based on mobile phone data. Background technique [0002] The division of transportation mode is the proportion of travelers choosing transportation for travel. It is based on the data of residents’ travel survey, studies people’s transportation mode selection behavior when traveling, and establishes a model to predict the traffic between transportation modes when conditions such as infrastructure or services change. changes in demand. [0003] The forecast of traffic mode division is an important part of traffic demand forecast in urban traffic planning, and the forecast of traffic mode division can provide important decision support for the optimization of future urban transportation structure. The traditional traffic mode division is mainly based on the questionnaire survey and analysis method, which consumes a lot ...

AI Technical Summary

Problems solved by technology

[0004] 1) It requires high cost and the implementation process is relatively complicated;

[0005] 2) Mobile devices need to be carried with you and have high requirements for signal strength. Information omissions are prone to occur during the information collection process, which affects the classification results of traffic modes;

[0006] 3) Only small samples can be sampled, the sample size is limited, and large-scale traffic mode division cannot be realized

[0007] To sum up, the existing research methods for traffic mode division are limited to laboratory research. Due to the limitation of data volume, large-scale traffic mode division cannot be realized, and it is difficult to apply to the actual urban traffic mode division prediction.

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