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However, the complexity of urban context and its indirect interaction with traffic flow deepen the difficulty of exploring their relationship. This study proposes a geo\u2010semantic framework first to generate semantic representations of multi\u2010hierarchical urban context and street\u2010level traffic flow, and then investigate their mutual correlation and predictability using a novel semantic matching method. The results demonstrate that each street is associated with its multi\u2010hierarchical spatial signatures of urban context and street\u2010level temporal signatures of traffic flow. The correlation between urban context and traffic flow displays higher values after semantic matching than those in multi\u2010hierarchies. Moreover, we found that utilizing traffic flow to predict urban context results in better accuracy than the reversed prediction. 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