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The design of pedestrian-friendly spaces is crucial for successful transit-oriented development (TOD). Macroscale environmental factors have been studied extensively, and microscale factors are gaining attention for their direct effect on walking behaviour and lower implementation costs. However, previous studies have predominantly employed symmetric analytical methods (such as classical statistical approaches), which tend to overlook the complexity inherent in urban design problems, limiting the exploration of configurational effects and causal asymmetry in environmental factors. In this study, rough set theory was applied to create flow graphs and analyse decision-making patterns related to sidewalk characteristics and pedestrian satisfaction in TOD-focused districts. Data were collected from 100 sidewalks in 18 designated TOD districts in Guangzhou, China. Eight causal rules were extracted that revealed potential configuration and asymmetric effects in walkability research. For example, sanitary facilities and adequate cleanliness measures appeared in rules associated with both high and low pedestrian satisfaction, suggesting that the effect of these factors depends on their interaction with other conditions. The causal-based data mining approach used in this study provides a systematic understanding of microscale pedestrian environments in relation to TOD. The findings offer strong interpretative value and practical insights for TOD planning, promoting the development of effective and evidence-based urban design strategies.
Zhu et al. (Mon,) studied this question.