Stagnation point judging method based on home-work correspondence and time-space kernel clustering

Abstract

The invention provides a stagnation point judging method based on home-work correspondence and time-space kernel clustering. User signaling data and user internet surfing data with high frequency signal triggering and time-space association features in a specified time period are acquired according to sampling requirements; through two turns of home-work correspondence and space-time kernel clustering analysis, a set of coarse-grained time-space stagnation points with the time-space activity characteristics is formed; the obtained time-space stagnation set is subjected to verification and algorithm optimization, and the activity characteristics of special population is analyzed; finally, an OD matrix travel schedule based on the traffic community is formed, thereby providing big data support, which satisfies the characteristics of the time-space travel activity chain, for the traffic planning and traffic demand management. The stagnation point judging method based on home-work correspondence and time-space kernel clustering in the invention combines the advantages of the home-work correspondence and the time-space kernel clustering analysis to finally form a stagnation point sequence with the time-space activity characteristics, further improves the rationality and accuracy of the traffic travel OD matrix and effectively reduces the cost.

Description

technical field [0001] The present invention relates to the technical field of traffic planning and traffic demand management, specifically a stagnation point judgment method based on job-housing correspondence and time-space kernel clustering, which can provide traffic planning and traffic demand management that satisfy the characteristics of time-space travel activity chains Big data support. Background technique [0002] The traffic OD trip matrix is ​​the characteristic matrix (Origin-Destination Matrix) based on the origin-destination trip of the traffic district in the traffic network, which is used to describe the distribution of traffic trips between urban traffic areas, and is an important factor in the construction of traffic planning models. component. The traditional traffic OD matrix is ​​a processing process of statistical analysis based on individual traffic travel survey data. It needs to go through three steps: sampling survey of origin and destination poin...

AI Technical Summary

Problems solved by technology

The disadvantage of this stagnation point judgment technology is that it ignores the characteristics of user travel activities and travel purposes. For example, general user travel activities: commuting, life, entertainment, travel and other activities. For life and entertainment travel, such as shopping malls, parks, large entertainment areas, etc., there are multiple base stations covered, and there are long-term residences in multiple base stations at the same time, it may be judged as multiple residence points, while for traffic travel In terms of OD matrix, a trip of a travel purpose activity should be judged as a trip. Therefore, the judgment of displacement state based on time series will ignore the characteristics of travel purpose activities, resulting in misjudgment of stagnation points that do not meet the characteristics of traffic travel OD activities.

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