Crowd　counting　is　very　important　in　many　fields　such　as　public　safety,　urban　planning,　and　is　essential　for　the　intelligent　transportation　systems.　Due　to　the　complexity　and　diversity　of　traffic　scenes,　point-level　annotations　for　pedestrians　would　cost　much　human　labor.　Weakly　supervised　crowd　counting　methods　are　more　suitable　for　these　scenes,　considering　they　only　require　count-level　annotations.　However,　ignoring　the　uneven　distribution　of　cross-distance　crowd　region　density　and　multi-scale　pedestrian　head,　existing　weakly　supervised　methods　can　not　achieve　similar　counting　performance　as　fully　supervised　crowd　counting　methods.　To　solve　these　issues,　we　propose　a　novel　multi-level　dynamic　graph　convolutional　networks　for　weakly　supervised　crowd　counting.　Within　this　network,　a　multi-level　region　dynamic　graph　convolutional　module　is　designed　to　mine　the　cross-distance　intrinsic　relationship　between　crowd　regions.　A　feature　enhancement　module　is　used　to　enhance　crowd　semantic　information.　In　addition,　we　design　a　coarse　grained　multi-level　feature　fusion　module　to　aggregate　multi-scale　pedestrian　information.　Experiments　are　conducted　on　five　well-known　benchmark　crowd　counting　datasets,　achieving　state-of-the-art　results　compared　to　existing　weakly　supervised　methods　and　competitive　results　compared　to　fully　supervised　methods.