Modeling　how　network-level　traffic　flow　changes　in　the　urban　environment　is　useful　for　decision-making　in　transportation,　public　safety　and　urban　planning.　The　traffic　flow　system　can　be　viewed　as　a　dynamic　process　that　transits　between　states　(e.g.,　traffic　volumes　on　each　road　segment)　over　time.　In　the　real-world　traffic　system　with　traffic　operation　actions　like　traffic　signal　control　or　reversible　lane　changing,　the　system＇s　state　is　influenced　by　both　the　historical　states　and　the　actions　of　traffic　operations.　In　this　paper,　we　consider　the　problem　of　modeling　network-level　traffic　flow　under　a　real-world　setting,　where　the　available　data　is　sparse　(i.e.,　only　part　of　the　traffic　system　is　observed).　We　present　DTIGNN,　an　approach　that　can　predict　network-level　traffic　flows　from　sparse　data.　DTIGNN　models　the　traffic　system　as　a　dynamic　graph　influenced　by　traffic　signals,　learns　the　transition　models　grounded　by　fundamental　transition　equations　from　transportation,　and　predicts　future　traffic　states　with　imputation　in　the　process.　Through　comprehensive　experiments,　we　demonstrate　that　our　method　outperforms　state-of-the-art　methods　and　can　better　support　decision-making　in　transportation.　©　2022　ACM.