Vehicle　headway　distribution　is　fundamental　for　several　important　traffic　research　and　simulation　issues.　Many　headway　models　have　been　developed　over　the　past　decades.　Each　has　its　own　strength　and　weakness.　Selection　of　the　most　suitable　model　for　a　certain　traffic　condition　remains　an　open　issue.　A　comprehensive　study　of　the　performance　of　typical　headway　distribution　models　on　urban　freeways　is　presented.　With　the　advanced　loop　event　data　analyzer　system,　many　accurate　headway　observations　were　obtained　from　1-5　in　the　area　of　Seattle,　Washington.　These　headway　data　were　used　to　calibrate　and　examine　the　performance　of　various　headway　models.　The　goodness　of　fit　for　several　most　commonly　used　headway　distribution　models　was　investigated　by　using　headways　observed　on　regular　lanes　and　high-occupancy-vehicle　(HOV)　lanes　from　different　time　periods　of　day.　To　evaluate　the　performance　of　these　headway　models,　the　analytical　Kolmogorov-Smirnov　test　statistic　and　visualized　comparison　curves　were　used　to　measure　and　reflect　their　overall　goodness　of　fit　to　the　collected　headway　data.　Although　each　model　has　its　own　practicability　to　a　certain　extent,　the　test　results　showed　that　the　doubledisplaced　negative　exponential　distribution　model　provided　the　best　fit　to　these　urban　freeway　headway　data,　especially　for　HOV　lanes　at　wide-ranging　flow　levels.　The　shifted　lognormal　distribution　also　fits　the　general　purpose　lane　headways　very　well.　As　a　byproduct,　a　new　standard　parameter　estimation　method　was　developed　for　calibrating　complex　multi-parameter　headway　models.