Differences in spatial pattern of expressway passenger and freight flows based on OTC data: A case study of Guangdong, China

Intercity passenger and freight transport on expressways reflect distinct socio-economic patterns. Understanding these differences is vital for urban network theory and regional development, but data limitations hinder vehicle type differentiation in past studies. Using Guangdong Province as a case study, we utilized expressway online toll collection data to create networks of passenger and freight flows, examining differences through network analysis and the Random Forest algorithm.The findings are summarized below. Passenger and freight flows largely adhere to a Pareto distribution. Passenger flow networks exhibit higher density and cohesion than freight networks. The lognormal function is the most suitable model for depicting the distance decay pattern of passenger flows, whereas the power function offers the best fit for freight flows. The range of passenger flows is concentrated within 0∼40 km, whereas the range for freight flows is 0∼100 km. From a spatial perspective, a notable spatial agglomeration effect is observed in passenger flows, which primarily concentrate along both sides of the Pearl River Estuary. Additionally, population factors exert a more substantial influence on passenger flows, whereas labor force and enterprise factors significantly impact freight flows. This study deepens our understanding of expressway transport and supports vehicle classification control and collaborative traffic governance.