Abstract
Freight mobility segregation, a phenomenon like residential social segregation, is a crucial aspect of city landscapes that influences the livability and sustainability of cities. However, there is a deficiency in understanding the intrinsic complexity of freight mobility segregation, particularly regarding the micro-level truck behaviors. In this study, we develop a new approach to assess the degree of freight mobility segregation within cities by leveraging large-scale truck GPS data in Chinese cities. The analysis indicates the existence of freight mobility segregation in cities, where certain groups of trucks serve high-demand areas, while another group of trucks serves low-demand areas. The activity spaces of distinct truck groups are largely non-overlapping or segregated. To uncover the correlations between mobility segregation and truck operational patterns, we introduce two metrics to characterize truck multi-tours behavior, focusing on tour pattern predictability and activity explorability. By employing freight point-of-interest (POI) data, we further reveal the influence of local economic structures and industrial compositions on mobility segregation in cities. These findings enrich our understanding of the dynamics of city freight systems, offering implications for improving logistics efficiency and fostering sustainable city development.
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