This　study　proposes　a　novel　traffic　information　estimation　method　based　on　deep　learning　and　cellular　floating　vehicle　data　(CFVD).　In　this　paper,　a　probabilistic　analysis　model　based　on　deep　learning　is　proposed　to　consider　the　relationship　between　vehicle　speed　and　communication　behaviors.　Then,　a　vehicle　speed　estimation　method　based　on　the　proposed　probabilistic　analytical　model　is　proposed　to　estimate　vehicle　speed.　For　traffic　flow　estimation,　normal　location　update　is　adopted　to　estimate　traffic　flow.　The　estimated　vehicle　speed　and　the　estimated　traffic　flow　can　be　gathered　to　estimate　traffic　density.　The　proposed　method　is　verified　by　the　simulation　tool　and　the　experiment　results　revealed　that　the　accuracies　of　estimated　vehicle　speed,　estimated　traffic　flow,　and　estimated　traffic　density　are　96.36%,　99.80%,　and　96.45%,　respectively.　Thus,　this　method　estimates　traffic　information　accurately　and　helps　improve　the　performance　of　the　intelligent　transportation　system　(ITS).　©　2022,　Taiwan　Ubiquitous　Information　CO　LTD.　All　rights　reserved.