As　an　important　transportation　infrastructure,　the　timely　updating　of　road　network　data　is　of　great　significance　in　the　fields　of　traffic　management,　emergency　response,　and　urban　planning.　Road　network　matching　that　determines　the　correspondence　between　the　features　of　road　network　data　from　different　sources　serves　this　purpose.　It　also　provides　technical　support　for　tasks　such　as　the　quality　assessment　of　crowdsourced　road　network　data,　which　has　attracted　a　lot　of　attention　in　the　field　of　geographic　information.　However,　traditional　road　network　matching　methods　mainly　measure　the　similarity　of　road　network　structure　through　the　geometric　and　topological　attributes　of　road　network　data　to　determine　the　matching　relationship　of　road　network　elements.　Such　methods　with　manually　designed　features　and　thresholds　are　easily　limited　by　experts＇　experience,　which　degrades　their　performance　under　complex　road　network　structures.　In　recent　years,　road　network　data　modeling　based　on　graph　neural　networks　has　become　a　research　hotspot　and　has　achieved　excellent　performance　in　several　road　network　modeling　tasks.　However,　most　of　the　existing　methods　use　direct　neighborhood　aggregation　on　the　graph　topology　to　learn　the　embedded　representation　of　the　road　network　structure,　without　considering　the　spatial　relationship　of　road　network　features　in　this　key　step,　and　failing　to　make　full　use　of　the　representation　learning　capability　of　graph　neural　networks.　For　this　reason,　this　study　proposes　an　improved　neighborhood　aggregation　that　performs　a　spatially　explicit　graph-based　embedding　learning　method　for　road　network　matching.　First,　a　road　graph　model　of　the　road　network　data　is　constructed,　and　geometric,　semantic,　and　location　features　are　extracted.　Then,　based　on　the　GraphSAGE　framework,　three　kinds　of　neighborhood　aggregation　operators　(i.　e.,　spatial,　classified,　and　hybrid)　are　proposed,　and　the　computation　of　spatial　relationships　and　attribute　types　of　road　network　features　is　introduced　in　the　neighborhood　aggregation　operations.　Finally,　the　similarity　of　graph　node　embedding　is　utilized　to　determine　the　matching　relationship　of　road　network　features.　To　verify　the　effectiveness　of　the　proposed　method,　extensive　experiments　are　carried　out　using　real-world　road　network　data.　The　proposed　method　achieves　the　optimal　performance　in　all　metrics　on　the　test　data　of　the　study　region,　which　improves　the　matching　correctness　rate　by　more　than　11%　and　the　recall　rate　by　more　than　6.8%　compared　to　the　baseline　graph　neural　network　method.　Furthermore,　the　road　network　graph　embedding　features　are　analyzed　from　the　aspects　of　graph　embedding　structure　and　embedded　road　network　structure,　which　helps　explore　the　role　of　improved　neighborhood　aggregation　on　the　graph　embedding　representation　capability　and　provides　a　new　perspective　for　further　improving　the　graph　neural　network　road　network　modeling.　©　2024　Science　Press.　All　rights　reserved.