Information　and　communication　technologies　have　improved　the　quality　of　intelligent　transportation　systems　(ITS).　By　estimating　from　cellular　floating　vehicle　data　(CFVD)　is　more　cost-effective,　and　easier　to　acquire　than　traditional　ways.　This　study　proposes　a　cell　probe　(CP)-based　method　to　analyse　the　cellular　network　signals　(e.g.,　call　arrival,　handoff,　and　location　update),　and　regression　models　are　trained　for　vehicle　speed　estimation.　In　experiments,　this　study　compares　the　practical　traffic　information　of　vehicle　detector　(VD)　with　the　estimated　traffic　information　by　the　proposed　methods.　The　experiment　results　show　that　the　accuracy　of　vehicle　speed　estimation　by　CP-based　method　is　97.63%.　Therefore,　the　CP-based　method　can　be　used　to　estimate　vehicle　speed　from　CFVD　for　ITS.