In recent years, a multiplicative hybrid (MH) route choice model was proposed to overcome the drawbacks of the multinomial logit (MNL) model and the multinomial weibit (MNW) model. This paper compares the conditions for the stochastic traffic assignment paradox of the three models. We analyze the condition when improving a link in an uncongested network counterintuitively increases total travel costs. Using three typical flow-independent networks (two links, $n$ independent links, and $n$ routes with $m$ overlapping links), we reveal the strong relationships in the paradox conditions of the three models. We further study the paradoxical features of the three models in the Sioux-Falls network, where the model parameters are estimated from simulated route sets. The case study shows that (1) the MH model fits the data the best, (2) using the MNL or the MNW model to identify paradox links exhibits intrinsic tendencies that are consistent with the theoretical analysis, and (3) the paradox links identified by the MH model is a compromise of the other two models. This paper delves into the relationships of the three models in the stochastic assignment paradox and provides suggestions and caveats to the application of the three models.

近年来，为了克服多项式logit（MNL）模型和多项式weibit（MNW）模型的缺点，提出了乘法混合（MH）路径选择模型。本文比较了三种模型的随机交通分配悖论的条件。我们分析了在不拥塞的网络中改善链接会违反直觉地增加总旅行成本的情况。使用三个典型的流无关网络（两个链路，$n$独立链接，以及$n$路线与$米$重叠链接），我们揭示了三个模型的悖论条件中的强关系。我们进一步研究了苏福尔斯网络中三个模型的矛盾特征，其中模型参数是根据模拟的路线集估计的。案例研究表明：(1) MH 模型与数据的拟合效果最好，(2) 使用 MNL 或 MNW 模型识别悖论链接表现出与理论分析一致的内在趋势，以及 (3) 确定的悖论链接MH 模型是其他两种模型的折衷方案。本文深入研究了随机分配悖论中三种模型的关系，并对三种模型的应用提出了建议和注意事项。