多项式logistic回归
计算机科学
模式选择
决策树
模式(计算机接口)
旅游行为
离散选择
旅行时间
逻辑回归
钥匙(锁)
关联规则学习
目的地
预测建模
订单(交换)
Apriori算法
计量经济学
先验与后验
线性模型
树(集合论)
随机森林
回归分析
机器学习
多项式分布
亲和力分析
决策树学习
交通规划
弹道
数据挖掘
公共交通
罗伊特
嵌套逻辑
联想(心理学)
期限(时间)
运输工程
作者
Hui Song,Xinxin Wang,Wen Tian,Lina Shi,Shiqi Li
标识
DOI:10.1038/s41598-026-37216-4
摘要
This study was conducted to investigate the travel behavior of residents in a medium-sized Chinese city, with the goal of exploring travel characteristics and identifying the key factors influencing urban travel mode choices. While traditional discrete choice models are known for their strong interpretability, their predictive accuracy remains limited. In contrast, machine learning models are recognized for offering higher predictive accuracy but are frequently criticized for their lack of interpretability. To address this issue, a CART-Apriori predictive model was constructed through the integration of the Classification and Regression Tree (CART) model and the Apriori algorithm. Accuracy, the Kappa coefficient, and the Macro-F1 score were utilized as performance metrics for the quantitative comparison of the CART-Apriori model with various alternative models. Additionally, the RuleFit model was employed to extract nonlinear relationships generated by the CART-Apriori model. These relationships were subsequently converted into rule-based features and incorporated into a multinomial Logit linear model to identify the most influential travel rules for each travel mode. The results demonstrated that an average overall prediction accuracy of 82.77% was attained by the CART-Apriori model. Using the Apriori association rule algorithm, the most critical factors influencing urban residents' travel mode choices were ranked in descending order of importance as travel distance, travel purpose, car ownership, and the number of transfers. When walking was chosen as the travel mode, travel distance played a dominant role. When shared electric vehicles or private cars were chosen, travelers were primarily motivated by the intention to reach their destinations directly. Shared bicycles were predominantly chosen by commuters traveling 1-3 km. For bus users, travel distance and the number of transfers were the most influential factors. Ride-hailing users were primarily commuters traveling 1-3 km who required multiple transfers on public transportation.
科研通智能强力驱动
Strongly Powered by AbleSci AI