在基于智能体的交通模拟中考虑家庭内部联合出行
Accounting for intra-household joint travel in agent-based transport simulations
AI总结：
该研究针对基于智能体的交通模拟忽略家庭联合出行的问题，提出三步整合方法，经巴黎数据验证可复现相关出行特征，助力更可靠评估差异化交通政策。
AI中文摘要：
家庭内部基于家的联合出行——即家庭成员一同出发、参与共享活动并一同返回的出行——占日常出行的相当大比例，但在交通模拟中被系统性忽略。将联合出行与单独出行合并到单一的方式选择框架中，会在偏好参数估计中引入偏差。本文提出一种三步法，将联合出行整合到基于智能体的交通模型中：一是用于识别联合出行的随机森林分类器，二是专门针对联合出行估计方式选择的多项Logit模型，三是用于驾驶员/乘客分配的惩罚Logistic回归。将该方法应用于巴黎地区的家庭出行调查数据，结果显示其成功复现了合成人口中观测到的联合出行比例与方式分布。所提出的框架可实现对那些对联合出行与单独出行影响不同的政策（如高承载车辆HOV车道或家庭公交票价折扣）的更可靠评估。
英文摘要：
Intra-household joint home-based tours - trips in which household members depart together, engage in shared activities, and return together - represent a significant share of daily travel, yet are systematically ignored in transport simulations. Conflating joint and solo tours within a single mode choice framework introduces bias in preference parameter estimates. This paper proposes a three-step methodology to integrate joint tours in agent-based transport models: a Random Forest classifier to identify joint tours, a Multinomial Logit model estimating mode choice specific to joint tours, and a Penalized Logistic Regression for driver/passenger assignment. Applied to the Paris region using household travel survey data, the methodology successfully replicates observed joint tour shares and mode distributions in a synthetic population. The proposed framework enables more reliable evaluation of policies whose impacts differ between joint and solo travel, such as HOV lanes or family transit fare discounts.
