The　aviation　hub　curbside　is　the　interface　between　airside　and　landside　transportation　systems.　Microscopic　traffic　simulation　technology　is　the　most　common　method　to　analyze　the　characteristics　of　curbside　operation.　For　scientific　use　of　microscopic　traffic　simulation　tools,　the　operation　characteristics　of　simulation　objects　should　be　mastered　in　detail.　In　this　paper,　airport　curbside　operation　characteristic　parameter　detection　method　based　on　radar　equipment　is　proposed.　The　method　is　divided　into　four　steps,　background　point　segmentation,　trajectory　point　extraction,　trajectory　tracking,　and　traffic　information　extraction.　Firstly,　a　coordinate　system　correction　is　performed　by　Random　Sample　Consensus　(RANSAC)　algorithm.　Secondly,　the　data　are　clustered　and　the　trajectory　points　of　the　vehicle　are　obtained　by　Density-Based　Spatial　Clustering　of　Application　with　Noise-Oriented　bounding　box　(DBSCAN-OBB)　algorithm,　and　the　trajectory　points　generated　by　the　same　vehicle　are　tracked　to　obtain　the　original　vehicle　trajectory　chain.　And　remove　the　outliers　in　the　data　sequence　and　optimize　the　original　trajectory　chain　by　combining　median　filtering　and　Kalman　filtering.　Finally,　the　vehicle　running　speed　and　acceleration　parameters　are　extracted　from　the　trajectory　data.　In　this　study,　the　proposed　method　was　used　to　obtain　a　set　of　curbside　parameters　for　researchers　to　use,　which　include　the　layout　of　the　facilities　along　the　curbside　and　the　operation　characteristics　data　of　vehicles　and　pedestrians.