Transportation mode determination method based on triangular positioning data of mobile phone

Abstract

The invention discloses a transportation mode determination method based on triangulation location data of a mobile phone. A mobile phone user transportation chain is constructed based on mobile phonetriangulation location data; on the basis of a penalty factor, the obtained mobile phone user transportation chain data are cleaned to remove noise data, and an all-day mobile phone user transportation chain is reconstructed; according to the all-day mobile phone user transportation chain, time-dimension fine granularity division is carried out to form a plurality of sub transportation periods, total transportation distances and straight transportation distances of all sub transportation periods are calculated respectively, and non-linear coefficients of all sub transportation periods are obtained; multi-mode transportation modes of the sub transportation period of the mobile phone user are identified; and on the basis of the identified multi-mode transportation mode proportion of the subtransportation period of the mobile phone user, a main transportation mode of the all-day transportation period is identified. According to the invention, the transportation mode of the individual user can be obtained based on the on-aggregation level, so that the complexity of the model is reduced and the prediction accuracy is improved.

Description

technical field [0001] The invention relates to the field of traffic big data, in particular to a traffic travel mode discrimination method based on mobile phone triangulation data. Background technique [0002] With the rapid advancement of urban construction, the temporal and spatial distribution and mode structure of urban traffic have undergone profound changes. Under the multi-mode combined travel environment, changes in urban traffic demand directly determine the planning and construction of urban traffic infrastructure. Objectively grasping the traffic demand of different travel modes is the key to scientifically evaluating the construction level and operation effect of the urban traffic system. The structure of urban travel patterns determines the investment focus of relevant departments in facility construction and maintenance, and provides an important data basis and evaluation reference for urban traffic management and traffic planning. [0003] The survey of tr...

AI Technical Summary

Problems solved by technology

In terms of travel mode recognition, most studies use the aggregated mode to obtain the proportion of various travel modes in the user population, the recognition accuracy is not high, and the model migration ability is poor; a small number of studies focus on the non-aggregated level, through a large number of parameter calibration To improve the recognition accuracy of the user's individual traffic mode, the model is complex and difficult to promote

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