Existing　real-world　travel-time　estimation　applications　face　the　crucial　challenge　of　inferencing　spatiotemporal　traffic　status　propagation　over　complex　and　irregular　networks.　To　meet　that　challenge,　this　article　presents　a　novel　GPS-data-based　travel-time　estimation　approach　utilizing　an　adaptive　spatiotemporal　graph　(ASTG).　The　proposed　ASTG　approach　improves　the　original　attention　mechanism　(with　an　enhanced　self-attention　mechanism)　while　adaptively　analyzing　the　dynamic　relevancy　between　segments　over　the　vast　spatial　and　temporal　dimensions.　Moreover　various　traffic　metadata,　such　as　additionally　available　traffic　state　variables　and　network/road　characteristic　information,　were　better　utilized.　Leveraging　a　gate　fusion　function,　the　spatial　and　temporal　dependencies　extracted　from　traffic　metadata　were　fused　for　inferencing　more　precise　traffic　states.　A　field　implementation　of　the　proposed　approach　was　conducted　in　Zhangzhou,　China,　with　sparse　GPS　probe　data.　and　evaluated　against　the　automatic　vehicle　identification　reported　segment　travel　time.　Compared　to　other　high-performance　baseline　algorithms,　the　proposed　ASTG　model　demonstrated　state-of-the-art　accuracy　while　intuitively　capturing　the　sophisticated　dynamic　spatiotemporal　relevancy　with　the　proposed　enhanced　attention　mechanism.　Implementing　the　proposed　system　would　provide　valuable　travel　information　for　road　users　and,　meanwhile,　assist　traffic　management　agencies　in　congestion　alleviation.