出租车超速概率建模因素对应回归系数的稳定性分析

[ Objective ] This study investigates the stability of regression coefficients corresponding to modeling factors for taxi speeding likelihood across different time scales, identifying stable factors that can support taxi safety management. [Method] GPS data were collected from 5 719 taxis in Chengdu City from 1st to 28th of November, 2016. Then, this study identified the speeding behaviors and measured the percentage of speeding distance with three time scales as the speeding likelihood, i. e., weeks, weekday & non-weekend, and workday. The operational factors were simultaneously extracted from three time scales. Bayesian random coefficient model was applied to model speeding likelihood in each time unit. The stability of regression coefficients was subsequently examined through the likelihood ratio test. [ Result] The coefficients of various operational factors vary significantly across three time scales. The coefficient of driving distance ratio during peak hours is the most unstable, as it yields significant variation across time scales. The regression coefficient of daily driving distance is unstable neither. In contrast, regression coefficients of factors are relatively stable across all time scales, e. g., ratio of driving at night, ratio of driving on low-speed-limit roads, average hourly passenger capacity, average one-way passenger distance, and average no-load distance. The elasticity analysis further indicates that the pursuit of higher revenue is the primary reason for increasing taxi speeding likelihood. [Conclusion] Among contributing factors of taxi speeding likelihood, the partial operational factors’ regression coefficients are stable across time scales, which require common countermeasures to tackle the speeding behaviors. However, for the factors with unstable regression coefficients, they require more tailored and flexible countermeasures to improve the effectiveness of safety management strategies.