Inferring freeway traffic volume with spatial interaction enhanced betweenness centrality

Freeway traffic volume is strongly correlated with the intensity of regional socioeconomic spatial interactions and the road network structure. Although existing studies have proposed indicators of betweenness centrality (BC) integrated into regional spatial interactions, the socio-economic drivers of freeway traffic volume formation have been neglected. More importantly, existing studies have not established a non-linear response relationship among BC, city socio-economic spatial interactions, and road traffic volume, which severely limits the comprehensive quantification of the role of freeway traffic flow drivers. Therefore, this study proposes a freeway traffic volume inference method that integrates spatial interaction to enhance BC. First, the socioeconomic factors of the origin and destination cities are incorporated into the BC indicator to create an enhanced betweenness centrality indicator (ODBC), which quantifies the strength of spatial interactions between cities. Second, a machine learning approach is used to develop the non-linear response relationship between ODBC and freeway traffic flow to accurately infer traffic volume. Finally, utilizing the SHapley additive explanation approach, the role vectors of intercity freeway traffic volume drivers are quantified. Experiments conducted on data from freeway toll stations demonstrate that the proposed method surpasses the baseline method based on BC and weighted by BC considering only the potential destination or origin city attractiveness, with an improvement in R2 of 14%, 4.2%, and 4%, and a maximum reduction in RMSE of 40%, 24.5%, and 26%. The proposed method yields higher accuracy for unknown road segments and is easily interpretable.