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		<title>Mamba-based model tames noisy urban traffic prediction with frequency-domain filtering</title>
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		<pubDate>Sun, 25 Oct 2026 16:05:21 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[adaptive graph learning]]></category>
		<category><![CDATA[frequency-domain enhancement]]></category>
		<category><![CDATA[intelligent transportation systems]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Mamba]]></category>
		<category><![CDATA[prediction]]></category>
		<category><![CDATA[RoST-Mamba]]></category>
		<category><![CDATA[Spatiotemporal Modeling]]></category>
		<category><![CDATA[state space models]]></category>
		<category><![CDATA[traffic]]></category>
		<category><![CDATA[traffic prediction]]></category>
		<category><![CDATA[urban road networks]]></category>
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					<description><![CDATA[A new spatiotemporal model called RoST-Mamba combines adaptive graph learning, frequency-domain filtering and graph-constrained state-space modeling to keep traffic forecasts accurate on noisy urban road networks.]]></description>
		
		
		
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