In making daily commuting trips, a part of travelers, which are called captive travelers, rely on one transport mode due to a lack of access or affordability to other transport modes. To account for the effect of such captive travelers on network equilibrium performances, this paper proposes a multi-modal network equilibrium (MMNE) model that accounts for the captive travelers and the correlations between modes and between routes. First, a hybrid mode choice model is developed by integrating the dogit and nested logit (NL) models. The hybrid dogit–NL (DNL) model has smaller direct and cross elasticity than the NL model, it alleviates the property of irrelevant from independent alternatives and takes the dogit and NL modal splits as bounds. Second, the path-size logit (PSL) model is adopted for predicting travelers’ route choices with overlapping routes. The DNL–PSL MMNE model is formulated as a mathematical programming problem that admits an equivalent and unique solution. Then, a partial linearization algorithm with the Barzilai–Borwein (BB) step sizes is developed. The numerical results reveal that captive travelers lead to lower sensitivity toward transport policies and may cause higher network total travel time; while the perception of mode similarity may impair the overall attractiveness of modes with a high degree of similarity. The observations indicate that to promote green transportation, policy efforts should be made to make use of or adjust the captivity structure and produce diversified perceptions of and preferences for different green transport modes. The BB step sizes are suggested for low travel demand cases when solving the combined travel choice problems. Further, extensions of the DNL model with bundle captivities are discussed. The results of the paper help improve the network equilibrium prediction and support transport policymaking.

在日常通勤旅行中，一部分旅行者（被称为“专属旅行者”）由于无法使用或负担不起其他交通方式而依赖一种交通方式。为了解释此类专属旅行者对网络均衡性能的影响，本文提出了一种多模式网络均衡（MMNE）模型，该模型考虑了专属旅行者以及模式之间和路线之间的相关性。首先，通过集成 dogit 和嵌套 logit (NL) 模型开发了混合模式选择模型。混合dogit-NL（DNL）模型比NL模型具有更小的直接弹性和交叉弹性，它减轻了独立替代方案的不相关性，并以dogit和NL模态分裂为界。其次，采用路径大小逻辑（PSL）模型来预测具有重叠路线的旅行者的路线选择。 DNL-PSL MMNE 模型被表述为一个数学规划问题，它有一个等价且唯一的解决方案。然后，开发了一种具有 Barzilai–Borwein (BB) 步长的部分线性化算法。数值结果表明，圈养旅客导致对交通政策的敏感度降低，并可能导致网络总出行时间增加；而对模式相似度的感知可能会损害高度相似的模式的整体吸引力。观察表明，推动绿色交通，应在政策上利用或调整圈养结构，形成对不同绿色交通方式的多元化认知和偏好。在解决组合行程选择问题时，建议针对低行程需求情况使用 BB 步长。此外，还讨论了 DNL 模型与束约束的扩展。论文结果有助于改进网络均衡预测并支持交通政策制定。