Commuting behavior identification method based on shared electric bicycle borrowing and returning point data

Abstract

The invention discloses a commuting behavior identification method based on shared electric bicycle borrowing and returning point data. The commuting behavior identification method comprises the following steps: 1) preprocessing original data of a user order; 2) sorting'borrowing or returning time points' by the same vehicle number data, and matching travel data based on'vehicle use state '; 3) calculating the travel distance and time of the travel data matching result and cleaning; 4) clustering the longitude and latitude data of the vehicle borrowing and returning points in the peak hours of the workdays by using a DBSCAN algorithm, obtaining and calculating a hot spot area and a centroid coordinate thereof, and automatically generating a Thiessen polygon to define a new travel cell; 5) generating a travel OD matrix in combination with a matching result of the vehicle borrowing and returning points and the travel cells; 6) calculating a travel OD matrix traffic flow coefficient of each workday, and identifying an outgoing behavior commuting travel of which the travel OD matrix traffic flow coefficient is smaller than an agreed threshold; and 7) identifying the occupational and residential places. According to the invention, the commuting behavior of the shared electric bicycle is effectively identified from the user order data, and the occupational and residential place distribution is analyzed to improve the commuting use; a theoretical basis is provided for rate and scheduling.

Description

technical field [0001] The invention belongs to the field of data mining of shared motorcycle borrowing and returning points, and relates to a commuting behavior recognition method based on the data of shared motorcycle borrowing and returning points. Background technique [0002] The acceleration of domestic urbanization has led to an increase in population and an increase in the number of motor vehicles, which has brought about many problems such as urban pollution, road congestion, and air pollution, seriously affecting the daily work and life of urban residents, making it a huge bottleneck restricting urban development. "Transit-oriented" travel has become the common goal of more and more urban planners, residents, and governments, but at the same time it has also made the "last mile" problem increasingly prominent. Based on the concept of sharing economy, shared motorcycles are in line with the future personality as a terminal travel mode Its high-efficiency, flexible, ...

AI Technical Summary

Problems solved by technology

[0003] However, due to the backward technical means, low efficiency, and insufficient accuracy of the current manual static dispatching method, it is difficult to meet the demand for shared motorcycles, which leads to the inability to meet the commuting needs of residents during morning and evening peak hours, and problems such as "no car to borrow, and vehicle concentration" appear. , which seriously affects the quality of operation and service of shared motorcycles

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