Travel and activity mode identification method based on DBSCAN clustering algorithm

Abstract

The present invention discloses a travel and activity mode identification method based on a BDSCAN clustering algorithm. The method comprises the following steps: cleaning traveler spatio-temporal track data sets that are continuously acquired; calculating an average speed of coordinate points of cleaned data sets, and classifying a position coordinate point whose average speed is higher than a set threshold into a travel mode; based on a DBSCAN clustering algorithm, performing clustering analysis on the cleaned data sets, and determining an activity starting point and an activity ending point according to a clustering result; and according to coordinates and time of data points of an identified travel mode and activity mode, generating a travel time table. According to the method disclosed by the present invention, based on the acquired traveler spatio-temporal track sequence sets, the behavior modes of the travelers are divided into the travel mode and the activity mode by using the density-based clustering algorithm (DBSCAN). The method disclosed by the present invention is convenient for calculation and actual operation and has strong practicality, and by the method, the behavior mode of the travelers can be determined more accurately, so as to facilitate subsequent researches, so that the method has important realistic significance.

Description

technical field [0001] The invention relates to the technical field of traffic travel information, in particular to a method for collecting travel and activity pattern recognition information based on a DBSCAN clustering algorithm. Background technique [0002] Resident travel data is the basis of transportation planning and management. The development of transportation demand modeling theory can be roughly classified into two types of theoretical systems: travel-based and activity-based demand modeling. Travel-based demand modeling is widely used in the practice of traditional "four-stage approach" transportation planning. Based on the travel demand modeling method, from a macro perspective, the independent travel unit is used as the object to analyze the travel demand of each traffic district as a whole. However, this method does not consider the connection between these individual trips, mainly in two aspects, one is the lack of consideration of individual travel behavi...

AI Technical Summary

Problems solved by technology

[0003] At present, my country mainly adopts the manual survey method to obtain residents' travel information, which is cumbersome and consumes manpower and financial resources.

Moreover, the results of manual surveys are affected by various factors such as the level of investigators, the enthusiasm of residents to participate, the recovery rate of forms, and accidents. did not get good results

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