Forecasting　the　forthcoming　intersection　movement-based　traffic　volume　enables　adaptive　traffic　control　systems　to　dynamically　respond　to　the　fluctuation　of　traffic　demands.　In　this　paper,　a　deep-learning　based　Signal-control　Refined　Dynamic　Traffic　Graph　(ScR-DTG)　Model　is　proposed　for　advancing　the　network-level　movement-based　traffic　volume　prediction　task.　The　proposed　model　attempts　to　further　improve　the-state-of-art　and　practice　algorithms　in　traffic　prediction　for　arterial　network　adaptive　signal　control　utilizing　tradition　traffic　flow　theory　boosted　deep-learning　methodology.　For　precisely　inferencing　the　movement-based　demand　at　cycle-to-cycle　level,　the　proposed　model　incorporates　spatial　graph　convolution　inferencing　layer　and　temporal　inferencing　layer　to　explore　both　the　intricate　spatial　temporal　dependencies,　respectively.　A　signal　control　refining　module　is　contrived　to　deduce　the　controlled　movement　saturation　flow　and　introduce　the　essential　control　inferences,　which　is　of　great　significance　but　frequently　neglected　in　the　previous　researches.　Additionally,　according　to　the　real-time　movement　specified　travel　time,　this　paper　creatively　constructs　an　adjacent　graph　with　dynamic　order　for　more　accurately　capturing　the　ever-changing　spatial　relevancies.　Field　experiments　with　multiple　signal　schemes　were　conducted　in　the　downtown　area　of　Zhangzhou　(China).　The　promising　results　demonstrated　the　state-of-the-art　accuracy　than　other　high-performance　volume　prediction　algorithms.　Implementing　the　proposed　model　enables　to　obtain　accurate　movement-based　volume　predictions,　which　would　assist　the　traffic　manage-ment　agencies　in　adjusting　signal　timing　adaptively　and　further　improve　the　efficiency　of　signal　intersection.