Intersections　are　the　bottlenecks　of　the　road　network.　The　capacity　of　signalized　intersections　restricts　the　operation　of　the　road　network.　Dynamic　estimation　of　capacity　is　necessary　for　signalized　intersections　refined　management.　With　the　development　of　technology,　more　and　more　detectors　were　installed　near　the　intersection.　It　had　been　the　information-rich　environment,　which　provided　support　for　dynamic　estimation　of　capacity.　A　dynamic　estimation　method　for　a　saturation　flow　rate　based　on　a　neural　network　was　developed.　It　would　grasp　the　dynamic　change　of　saturation　flow　rates　and　influencing　factors.　The　measure　data　at　three　scenarios　(through　lanes,　shared　right-turn　and　through　lanes,　shared　left-turn　and　through　lanes)　of　signalized　intersections　in　Beijing　were　taken　as　examples　to　validate　the　proposed　method.　Firstly,　the　traffic　flow　characteristics　of　the　three　scenarios　and　factors　affecting　the　saturation　flow　rate　were　analyzed.　Secondly,　neural　network　models　of　the　three　scenarios　were　established.　Then　the　hyperparameters　of　neural　network　models　were　determined.　After　training,　the　neural　network　structure　and　parameters　were　saved.　Lastly,　the　test　set　data　was　validated　by　the　training　model.　At　the　same　time,　the　proposed　method　was　compared　with　the　Highway　Capacity　Manual　(HCM)　method　and　the　statistical　regression　method.　The　results　show　that　both　regression　models　and　neural　network　models　have　better　accuracy　than　HCM　models.　In　a　simple　scenario,　the　neural　network　models　are　not　much　different　from　the　regression　models.　With　the　increase　of　complexity　of　scenarios,　the　advantages　of　neural　network　models　are　highlighted.　In　through-left　lane　and　through-right　lane　scenarios,　the　estimated　saturation　flow　rates　used　by　the　proposed　method　were　7.02%,　4.70%,　respectively.　In　the　complexity　of　traffic　scenarios,　the　proposed　method　can　estimate　the　saturation　flow　rate　accurately　and　timely.　The　results　could　be　used　for　signal　control　schemes　optimizing　and　operation　managing　at　signalized　intersections　subtly.　©　2020　by　the　authors.