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Travelers often change their behavior in reaction to adverse weather conditions. This paper seeks to conduct an empirical evaluation of both ordered and unordered discrete outcome frameworks for examining the adaptive behavior decisions of intercity travelers and investigate the varying effects of explanatory factors on different alternatives of adaptive behavior during adverse weather. The data from 754 respondents in the Beijing-Tianjin-Hebei urban agglomeration, China are collected by a two-phase survey instrument. To capture the unobserved heterogeneity more effectively, this paper develops three advanced models to investigate the variations in intercity travel behavior during adverse weather. Four alternative adaptive behaviors are determined as outcome variables: maintaining original travel plans, changing only the intercity mode, changing the departure date, and canceling the trip, while potential influencing factors including adverse weather conditions, trip-related characteristics, individual attributes and urban agglomeration attributes are statistically assessed. The results indicate that the unordered models consistently outperform their ordered counterparts, and the incorporation of multilayer heterogeneity enhances the model fit. Furthermore, significant factors and their coefficient values vary across the different adaptive behavior alternatives. Intercity travelers demonstrate a higher probability of changing departure dates or canceling trips during snowy and windy days compared to rainy and foggy days. Trains exhibit higher flexibility and reliability during adverse weather, and access attributes significantly affect intercity travel adaptive behavior. Additionally, the analysis of individual and urban agglomeration attributes uncovers variations in adaptive behavior among individuals and cities. These findings provide profound insights into the complexities and variations of intercity travel behavior during adverse weather, and propose practical strategies to mitigate the detrimental impacts of adverse weather on intercity travelers. • Adaptive behavior of intercity travelers during adverse weather is investigated. • Unordered framework outperforms ordered counterparts in adaptive behavior study. • Multilayer unobserved heterogeneity in the adaptive behavior is accounted for. • The varying effects of explanatory factors on different adaptive alternatives are examined. • Practical strategies are proposed to mitigate negative impacts of adverse weather. [ABSTRACT FROM AUTHOR]

The causes analysis of road traffic accidents is often modelled based on high-dimensional small-sample data; however, such models often have low predictive accuracy and poor generalization performance. An analytical framework that considers both data augmentation and model optimization can enhance variable interpretation and predictive model performance, thereby improving the shortcomings of existing accident analysis methods. Our approach is as follows: 1) Starting with an analysis of the nature of road accidents, a symbolic operation is used to design a feature crosses algorithm. A random variable is added to construct a quantifiable feature selection algorithm, which can form a data augmentation method that conforms to the accident rules. 2) A highly reliable framework for analysing accident causes is constructed by using forward selection to optimize an ensemble learning model subset combined with feature crosses, feature selection and multiple-classification algorithms. A case study with accident data from a city in China shows that ensemble learning has the advantages of high predictive accuracy and strong generalization performance. It can accurately identify the key causes of accidents based on highly dimensional small-sample data. Driving behaviours such as lane changes and turns are the key causes of accidents. Giving drivers effective traffic environment information in a timely manner can significantly improve driving performance and reduce accident risk. This research provides a reference for the analysis of road accidents. [ABSTRACT FROM AUTHOR]