Accounting for intra-household joint travel in agent-based transport simulations

2026-08-19

AI Summary

本文提出一种三步方法，通过随机森林分类器、多项Logit模型和惩罚逻辑回归来解决交通模拟中家庭成员共同出行的问题。

Abstract

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.

Problem

• intra-household joint travel
• transport simulations
• bias in preference parameter estimates

Innovation

• Random Forest classifier
• Multinomial Logit model
• Penalized Logistic Regression