交通状态可预测性量化方法

Quantification for the predictability of traffic status

针对现有城市交通状态的可预测性缺乏有效量化分析方法这一问题,提出了基于熵的交通状态可预测性量化方法.首先,从静态可预测性出发,通过计算交通状态序列的熵得到对应的量化规律性,利用二元熵函数将该规律性转化为可预测性;然后,考虑到交通状态的可预测性会随着时间动态变化,通过瞬时熵实现了对特定时刻可预测性的量化计算;最后,分析了4类代表性交通状态预测模型的性能与交通状态可预测性之间的关联关系.实验表明,所提出的方法能够从静态和动态两个方面有效量化交通状态的可预测性,并揭示了不同类型的交通状态预测模型对可预测性依赖关系的差异,为交通状态预测模型的选择和设计提供了依据.

To address the issue of quantifying the predictability of traffic status,we proposed an entropy-based approach.First,the regularity of a traffic status sequence is evaluated in terms of entropy and converted to the corresponding predictability with a binary entropy function.Then,considering the dynamics of predictability,we introduced instantaneous entropy for quantifying the predictability of traffic status at specific time slot.In addition,we analyzed the correlation between the prediction performance of four representative traffic status prediction models and the quantified predictability of traffic status.As suggested by the experiments,the proposed approach can effectively quantify the predictability of traffic status from both static and dynamic views,and uncovered that different categories of prediction models behave differently against the predictability,hence providing meaningful supports for the selection and design of traffic status prediction models,