In　recent　years,　Light　Detection　and　Ranging　(LIDAR)　has　been　widely　used　in　the　Intelligent　Traffic　System　due　to　its　high-precision　data　acquisition　capabilities.　It　has　great　potential　for　accurately　detecting　and　tracking　vehicle　trajectories　on　the　roadside.　This　study　aims　to　develop　a　novel　methodological　framework　for　accurate　vehicle　trajectory　extraction　from　roadside　LIDAR.　The　proposed　method　starts　by　designing　a　detection　area,　followed　by　the　application　of　statistical　filtering,　density　clustering,　and　a　random　sample　consensus　(RANSAC)　segmentation　algorithm　to　eliminate　the　background　point　cloud.　Subsequently,　a　modified　density-based　spatial　clustering　of　applications　with　a　noise　algorithm　(BSO-DBSCAN)　is　employed　to　detect　the　vehicle　based　on　a　beetle　swarm　optimization　algorithm.　An　oriented　bounding　box　(OBB)　is　used　to　obtain　vehicle　position,　length,　and　width.　A　modified　trajectory　association　method　based　on　the　Kalman　filtering　algorithm　is　proposed　to　address　vehicle　occlusion.　LIDAR　is　deployed　to　obtain　vehicle　operation　data　in　the　expressway　work　zone,　and　the　performance　of　the　proposed　method　is　tested　using　an　unmanned　aerial　vehicle　(UAV).　The　experimental　results　demonstrate　that　the　proposed　method　successfully　extracts　high-precision　vehicle　trajectories.　Specif-ically,　the　vehicle　recognition　accuracy　increased　by　12.7%　in　MAPE　and　14.9%　in　RMSE　compared　to　DBSCAN.　The　trajectory　tracking　accuracy　increased　by　13.5%,　and　the　number　of　ID　switching　(ID-SW)　was　reduced　by　271　times　compared　to　SORT.　The　vehicle　trajectory　data　extracted　in　this　study　provides　a　foundation　for　traffic　characteristic　analysis　and　traffic　modeling.　The　extracted　data　can　be　downloaded　from　the　following　GitHub:　https://github.com/gao0628/Dataset.