Research on the Dynamic Adjustment Mechanism of Online Ride Hailing Capacity Scale

Abstract

This study aims to address the lack of methods for adjusting the scale of online ride hailing capacity. Taking G city as an example, a dynamic monitoring index system for the scale of online ride hailing capacity is constructed by comprehensively considering factors such as order volume, driver labor hours and income levels, and the operation status of the taxi market. Pearson correlation analysis and stepwise linear regression methods are applied to screen the indicators, and key indicators and threshold standards for the dynamic adjustment mechanism of online ride hailing capacity are proposed, providing theoretical basis and decision support for the adjustment of urban online ride hailing capacity scale