The　rapid　development　of　Internet　of　Vehicles　(IoV)　data　powers　various　online　intelligent　transportation　applications,　such　as　network　travel　time　reporting.　However,　the　accuracy　might　be　severely　compromised　due　to　limited　probe　vehicle　sampling　frequency.　On　that　account,　this　article　proposes　a　dynamic　multigraph　model-enabled　framework　to　estimate　reliable　network　travel　time,　even　in　low-IoV-frequency　arterial　corridors.　The　proposed　framework　first　develops　an　improved　sparse　IoV　travel　time　decomposition　method.　The　segment　travel　time　is　further　divided　into　the　free-flow　running　time　and　static　and　dynamic　delays.　Second,　a　dynamic　multigraph　traffic　network　model　(DMGTN)　is　developed　to　aid　the　proposed　decomposition　method.　The　model　analyzes　complicated　spatiotemporal　relevance　between　segments　from　multiple　perspectives:　the　real-time　travel　time,　congestion　level,　signal　control　(which　is　frequently　neglected　in　previous　research),　and　segment　properties.　Additionally,　two　distinct　enhanced　modules　are　designed　for　handing　dense　and　sparse　network　graphs,　respectively.　This　allows　for　a　more　efficient　inspection　over　large-scale　intricate　arterial　networks　while　maintaining　precision.　Field　implementation　is　conducted　in　the　downtown　area　of　Zhangzhou,　China.　Compared　to　other　high-performance　baseline　models,　the　designed　DMGTN　model　as　well　as　the　proposed　decomposition　method　demonstrate　state-of-the-art　accuracy　and　successfully　capture　travel　time　variability.　The　proposed　framework　better　utilizes　available　IoV　data　to　provide　valuable　traffic　information　for　commuters　and　traffic　management　agencies.　©　2009-2012　IEEE.