Transportation mode recognition method based on mobile phone grid data

Abstract

The invention discloses a transportation mode recognition method based on mobile phone grid data. A mobile phone user transportation sequence is constructed based on mobile phone grid data and time characteristics, distance characteristics and speed characteristics of the sequence are obtained; on the basis of a penalty factor, the obtained mobile phone user transportation sequence data are cleaned to remove noise data; according to the obtained cleaned mobile phone user transportation sequence, sub mobile phone user transportation sequences are divided based on a speed clustering method; according to the obtained sub transportation sequences, a mobile phone user transportation chain is generated and time characteristics, distance characteristics and speed characteristics of the transportation chain are obtained; multi-mode transportation modes of the mobile phone user transportation chain are identified; and on the basis of the identified multi-mode transportation mode proportion of the mobile phone user transportation chain, a main transportation mode of the all-day transportation period of the user 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 pattern recognition method based on mobile phone grid data. Background technique [0002] The main task of urban comprehensive transportation planning is to coordinate the multi-modal transportation subsystem with the layout of urban land and the development of transportation travel demand, so as to obtain the best benefits of urban construction and operation. The demand and supply of multi-mode combined travel has become the main trend in the development of comprehensive urban transportation in China. Under the multi-mode combined travel environment, objectively grasping the traffic structure and demand of different travel modes is the key to scientifically evaluating the construction level and operation effect of the urban traffic system, and providing a data basis for the coordinated resource allocation of urban multi-modal traffic networks. [0003] For a lon...

AI Technical Summary

Problems solved by technology

[0004] At present, the main data source for research on urban traffic travel patterns using mobile phone data at home and abroad is mobile phone triangulation, and there is no related research on urban traffic travel pattern recognition based on mobile phone grid data.

At the same time, limited by the influence of the base station itself and the user environment, there are a lot of "noise" data in the mobile phone grid data, which increases the difficulty of research

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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