This paper aims to discover the relationships among the weather, holidays, and the traffic volume using multisource data from the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and to reveal the corresponding regional spatial–temporal traffic and migration patterns. Using accurate hourly weather and traffic volume data, this study examines the traffic volume from the origin to the destination county by considering traffic factors, weather factors, and temporal factors. A Random-effect regression model and a random forest model are established to analyze the above factors and identify the factors that contribute to the annual variation in traffic patterns. An RER + RF fusion prediction model based on ridge regression is proposed to predict the hourly traffic volume from origin to destination county, and is adopted in the spatial–temporal submodels. The results show that the impact of rainfall on traffic volume varies as the rainfall varies, and a rain-induced traffic pattern shift towards highway travel is found, which interacts with the negative effect of rainfall on highway traffic volumes. The Spring Festival holiday witnesses a V-shaped traffic volume curve during the study period. Some traffic pattern differences are also found in different spatial–temporal submodels. The RER + RF fusion model performs better in predicting in parent model and most of the spatial–temporal submodels, which validates the proposed model in predicting the traffic volume. The findings can provide transport agencies, urban planning agencies, and urban agglomeration travelers with valuable information for highway transport activity analysis considering the effects of weather and festival events.

本文旨在利用粤港澳大湾区（GBA）的多源数据发现天气、节假日和交通量之间的关系，并揭示相应的区域时空交通和迁移模式。本研究使用准确的每小时天气和交通量数据，通过考虑交通因素、天气因素和时间因素来检查从起点到目的地县的交通量。建立随机效应回归模型和随机森林模型，对上述因素进行分析，找出影响交通模式年度变化的因素。提出了一种基于岭回归的RER+RF融合预测模型来预测始发地到目的地县的每小时交通量，并在时空子模型中采用。结果表明，降雨对交通量的影响随着降雨量的变化而变化，并且发现了降雨引起的交通模式向高速公路出行的转变，这与降雨对高速公路交通量的负面影响相互作用。研究期间，春节假期的车流量呈V型曲线。在不同的时空子模型中也发现了一些交通模式差异。RER + RF融合模型在父模型和大部分时空子模型中的预测性能更好，这验证了所提出的模型在预测交通量方面的效果。研究结果可以为交通机构、城市规划机构、