The　widespread　deployment　of　the　Internet　of　Things　(IoT)　allows　for　the　utilization　of　mobile　signaling　data　(MSD)　in　cellular　networks　to　perceive　the　underlying　traffic　states　on　road　networks.　Nevertheless,　the　application　of　MSD　for　traffic　flow　prediction　can　be　impeded　by　the　limited　positioning　accuracy　of　MSD　and　stringent　privacy　policies.　To　surmount　these　obstacles,　we　propose　a　traffic　flow　prediction　method　that　integrates　an　estimation　process　using　a　macroscopic　fundamental　function　(M)　with　a　positional　factor　(P),　combined　with　the　sequence-to-sequence　(S2S)　neural　networks　model　(MPS2S).　First,　we　estimate　traffic　flow　using　a　macroscopic　fundamental　function　(M).　In　this　approach,　we　introduce　a　positional　factor　(P)　that　fuses　road　inflows　and　outflows　to　capture　the　relationship　between　cellular　network　and　road　network.　Next,　we　employ　an　S2S　neural　network　model,　which　considers　the　spatial　and　temporal　aggregation　information　to　infer　future　traffic　flows.　Specifically,　an　encoder　estimates　traffic　parameters　from　MSD,　a　decoder　infers　multistep　traffic　flows,　and　a　coefficients　estimation　method　introduces　the　macroscopic　fundamental　function　into　the　neural　network.　To　validate　the　prediction　performance　of　our　proposed　model,　we　collect　video　data　from　roadside　cameras　to　measure　ground-truth　values.　In　addition,　we　analyze　the　impact　of　the　positional　factor　on　our　estimation　method　and　the　impact　of　spatial　and　temporal　aggregation　on　our　prediction　model　in　real-world　freeway　scenarios.　The　results　indicate　that　MSD　can　link　the　potential　traffic　information,　and　our　method　contributes　to　better　traffic　flow　prediction.