Advancing and lagging effects of weather conditions on intercity traffic volume: A geographically weighted regression analysis in the Guangdong-Hong Kong-Macao Greater Bay Area

With the rapid expansion of urban areas, intercity highways have become crucial for daily transportation. Traffic administrators and planners increasingly rely on evaluating highway traffic volume. This paper aims to investigate the relationship between various factors and intercity traffic volume, with a specific focus on exploring the advancing and lagging effects of weather conditions on traffic volume in the districts of urban agglomerations. Using multiple data sources in the Guangdong-Hong Kong-Macao Greater Bay Area, including weather factors (i.e., rain, temperature, wind, and visibility), traffic factors (i.e., total traffic volume and travel time), and other factors (i.e., node degree, hub cities, and time of day), a mixed geographically weighted regression (MGWR) model is applied to examine the spatial heterogeneity of these factors. The results show that intercity traffic volume is influenced by weather, traffic, and other factors. Additionally, the advancing and lagging effects of different weather factors exhibit spatial heterogeneity across districts. Moreover, the weather lagging effect has a more significant impact than the advancing effect on intercity traffic volume. These findings provide valuable insights into the impact of weather on intercity travel volume and offer precise traffic guidance for intercity travelers.