We　are　witnessing　increasing　interests　in　developing　＂smart　cities＂　which　helps　improve　the　efficiency,　reliability,　and　security　of　a　traditional　city.　An　important　aspect　of　developing　smart　cities　is　to　enable　＂smart　transportation,＂　which　improves　the　efficiency,　safety,　and　environmental　sustainability　of　city　transportation　means.　Meanwhile,　the　increasing　use　of　GPS　devices　has　led　to　the　emergence　of　big　trajectory　data　that　consists　of　large　amounts　of　historical　trajectories　and　real-time　GPS　data　streams　that　reflect　how　the　transportation　networks　are　used　or　being　used　by　moving　objects,　e.g.,　vehicles,　cyclists,　and　pedestrians.　Such　big　trajectory　data　provides　a　solid　data　foundation　for　developing　various　smart　transportation　applications,　such　as　congestion　avoidance,　reducing　greenhouse　gas　emissions,　and　effective　traffic　accident　response,　etc.　Instead　of　proposing　yet　another　specific　smart　transportation　application,　we　propose　the　parallel-distributed　network-constrained　moving　objects　database　(PD-NMOD),　a　general　framework　that　manages　big　trajectory　data　in　a　scalable　manner,　which　provides　an　infrastructure　that　is　able　to　support　a　wide　variety　of　smart　transportation　applications　and　thus　benefiting　the　smart　city　vision　as　a　whole.　The　PD-NMOD　manages　both　transportation　networks　and　trajectories　in　a　distributed　manner.　In　addition,　the　PD-NMOD　is　designed　to　support　general　SQL　queries　over　moving　objects　and　to　efficiently　process　the　SQL　queries　on　big　trajectory　data　in　parallel.　Such　design　facilitates　smart　transportation　applications　to　retrieve　relevant　trajectory　data　and　to　conduct　statistical　analyses.　Empirical　studies　on　a　large　trajectory　data　set　collected　from　3,500　taxis　in　Beijing　offer　insight　into　the　design　properties　of　the　PD-NMOD　and　offer　evidence　that　the　PD-NMOD　is　efficient　and　scalable.