Computer recognition method of GPS (Global Positioning System) positioning-based transportation mode combined travelling

Abstract

The invention discloses a computer recognition method of GPS (Global Positioning System) positioning-based transportation mode combined travelling. The method comprises the following steps of: acquiring data information such as travelling point and space tracks, instant speed and longitude and latitude of combination of multiple travelling modes of a traveler by using a GPS mobile positioning technology; carrying out mode recognition on the combination of multiple travelling modes by adopting data mining methods such as a BP neutral network through combination of a GPS technology and a GIS (Geographic Information System) technology, extracting information including geographic locations of a travelling time point, a travelling mode change time point and a transformation point of the traveler, and finally researching and developing one set of complete recognition and acquisition method which is suitable for the travelling of the combination of the multiple transportation modes in large cities in China.

Description

technical field [0001] The invention relates to computer identification technology, in particular to a computer identification method for combined travel of traffic modes based on GPS positioning. Background technique [0002] At present, the data collection of residents’ traffic travel behavior mainly uses traditional questionnaire surveys, telephone interviews, email interviews and other methods, but there are many problems in obtaining personal travel behavior characteristic data in traditional travel survey methods of residents: 1) subjective recall is usually used in manual questionnaire surveys The problems of deviation and error are relatively common, and the randomness is too strong, so it is difficult to fully guarantee the accuracy of the data. In traditional questionnaire surveys, respondents need to recall detailed travel data such as travel time, travel routes, travel methods, and travel destinations. The workload of respondents is heavy, and the burden of recal...

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Problems solved by technology

[0002] At present, the data collection of residents’ traffic travel behavior mainly uses traditional questionnaire surveys, telephone interviews, mail interviews and other methods, but there are many problems in obtaining personal travel behavior characteristic data in traditional travel survey methods of residents: 1) subjective recall is usually used in manual questionnaire surveys The problem of deviation and error is relatively common, and the randomness is too strong, so it is difficult to fully guarantee the data accuracy

In traditional questionnaire surveys, respondents need to recall detailed travel data such as travel time, travel routes, travel methods, and travel destinations. The workload of respondents is heavy, and the burden of recall is heavy. Random or wrong filling of questionnaires often occurs. At the same time, some short-term or short-distance trips are often missed, which seriously affects the quality of the survey; 2) The survey costs are high and the organization and implementation are difficult

The city-wide large-scale travel survey of residents requires the leadership of high-level government administrative departments and the implementation of multiple urban management and traffic management departments. It involves a wide range of areas and requires the assistance of various sub-district offices for household surveys. It is difficult to organize and implement. , the survey cost is relatively high. In recent years, the cost of traffic survey in the compilation of comprehensive traffic planning in some large cities is as high as several million; 3) The real-time dynamics of survey data update is poor

Because traditional travel surveys require a lot of financial and material resources and are difficult to implement, cities at all levels often need to wait several years to conduct resident travel surveys. In this way, the data cannot be updated with the rapid expansion of traffic, and the data becomes outdated. , cannot reflect the true and accurate traffic demand, and traffic planning has become a castle in the air

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