为倡导绿色出行理念，解决以往研究在处理重复观测数据时容易忽视的潜在相关性和个体异质性问题，针对如何利用智能手机APP提供的多模式出行信息引导小汽车出行者转向停车换乘（Park-and-Ride, P+R）模式进行了探究，同时引入广义线性混合模型（Generalized Linear Mixed Model，GLMM）分析了多模式出行信息对小汽车出行者转向P+R意向的影响。首先，基于上海市路网设计意向调查问卷，整合了自驾和P+R两种出行方式的道路拥堵程度、出行时间、停车费用及地铁车厢座位情况等信息，并运用全因子设计法构建了24种不同信息水平组合的假设情景。然后，通过智能手机APP界面示意图向小汽车出行者展示这些多模式出行信息，并收集其转向P+R的意向数据。最后，运用GLMM方法处理同一个体重复决策数据中潜在的相关性和捕捉个体间的异质性。结果显示，GLMM的应用不仅解决了同一个体重复决策间的相关性，还揭示了不同个体对道路拥堵程度和地铁车厢座位情况的差异化关注；智能手机APP整合的多模式出行信息显著提升了小汽车出行者转向P+R的意愿，且这一转变占比达29.2%；高收入、长驾龄以及对P+R政策不了解的出行者转向P+R的意愿较低。研究表明，通过智能手机APP整合自驾和P+R的多模式出行信息能显著增强P+R方式的吸引力，可为提升P+R的普及率提供新思路，有效促进小汽车出行者向绿色出行方式的转变。

To advocate the concept of green travel and address the likely neglected issues of potential correlation among repeated observations and individual heterogeneity in previous studies, this study explored how to leverage multimodal travel information provided by smartphone APPs to steer private car travelers towards park-and-ride (P+R) mode, and constructed a GLMM (Generalized Linear Mixed Model) to analyze the influence of multimodal travel information on private car travelers' intention to shift towards P+R. Firstly, based on the road network of Shanghai, a stated preference survey was designed, and the information on road congestion levels, travel time, parking fees, and subway seat availability for both self-driving and P+R options was integrated. A full factorial design approach was employed to construct 24 hypothetical scenarios with varying combinations information levels. Then, the multimodal travel information was presented to private car travelers through smartphone APP interface illustrations, and their intentions to shift towards P+R were collected. Finally, the GLMM method was used to address the correlation in repeated decision-making data from the same individual and capture inter-individual heterogeneity. The results showed that the application of GLMM not only resolved the correlation within repeated decisions made by the same individual but also revealed differentiated concerns among individuals regarding information on road congestion and subway seat availability; the integration of multimodal travel information via smartphone APPs significantly increased private car travelers' willingness to shift towards P+R, with a notable shift ratio of 29.2%; however, travelers with higher income, longer driving experience, and limited knowledge of P+R policies exhibited lower intentions to adopt P+R. The study concludes that the integration of multimodal travel information for self-driving and P+R options through smartphone APPs significantly enhances the attractiveness of P+R, offering novel insights for boosting P+R adoption rates and effectively promoting the transition of private car travelers towards green travel modes.