The efficiency of urban road networks is a critical determinant of traffic congestion levels. This efficiency depends not only on high road density but also on strong connectivity between regions. Conventional approaches to measuring this efficiency often adopt a density-based perspective, typically using planar fractal dimension to assess the extent to which a road network covers urban space—a metric referred to as the space-filling degree. Nevertheless, such methods tend to overlook connectivity, which may lead to an overestimation of space-filling degree in networks with high detour ratios or limited accessibility, thereby failing to reflect actual efficiency of road system. To address this limitation, we propose a flow-based box-counting fractal dimension from an origin-destination (OD) perspective, which evaluates how well a road network spatially supports real travel flows between OD pairs. We apply this method to assess the space-filling degree of major road networks in 135 cities worldwide and use multiple linear regression models to compare the explanatory power of the proposed flow-based fractal dimension against traditional planar fractal dimensions in relation to urban traffic congestion. The results demonstrate that our approach can capture the spatial structure of urban road networks. Moreover, the flow-based fractal dimension outperforms traditional metrics in explaining traffic congestion, showing a stronger negative correlation with congestion levels and greater model explanatory power than both planar fractal dimension and road density.
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