Due　to　environmental　concerns,　the　energy-saving　train　regulation　is　necessary　for　urban　metro　transportation,　which　can　improve　the　service　quality　and　energy　efficiency　of　metro　lines.　In　contrast　to　most　of　the　existing　research　of　train　regulation　based　on　centralized　control,　this　paper　studies　the　energy-saving　train　regulation　problem　by　utilizing　distributed　model　predictive　control　(DMPC),　which　is　motivated　by　the　breakthrough　of　vehicle-based　train　control　(VBTC)　technology　and　the　pressing　real-time　control　demand.　Firstly,　we　establish　a　distributed　control　framework　for　train　regulation　process　assuming　each　train　is　self-organized　and　capable　to　communicate　with　its　preceding　train.　Then　we　propose　a　DMPC　algorithm　for　solving　the　energy-saving　train　regulation　problem,　where　each　train　determines　its　control　input　by　minimizing　a　constrained　local　cost　function　mainly　composed　of　schedule　deviation,　headway　deviation,　and　energy　consumption.　Finally,　simulations　on　train　regulation　for　the　Beijing　Yizhuang　metro　line　are　carried　out　to　demonstrate　the　effectiveness　of　the　proposed　DMPC　algorithm,　and　the　results　reveal　that　the　proposed　algorithm　exhibits　significantly　improved　real-time　performance　without　deteriorating　the　service　quality　or　energy　efficiency　compared　with　the　centralized　MPC　method.