The　development　of　intelligent　transportation　has　generated　many　ultra　reliable　low　latency　communication　(URLLC)　tasks,　which　require　sufficient　communication　and　computation　resources　for　task　offloading　and　processing.　Although　mobile　edge　computing　(MEC)　provides　a　promising　solution,　its　efficiency　is　subject　to　the　limited　knowledge　and　analysis　capability　on　the　physical　networks.　Therefore,　in　this　paper,　we　propose　a　digital　twin　(DT)　empowered　MEC　framework　to　strengthen　the　MEC　task　offloading　efficiency　in　cellular　vehicle-to-everything　(C-V2X)　networks.　Our　proposed　DT　is　constructed　through　a　hybrid　data-driven　and　model-driven　approach　to　capture　the　realistic　transportation　network　features.　Then,　DT　leverages　the　metric　of　time　to　collision　to　predict　vehicular　safety　levels　and　estimates　the　corresponding　URLLC　task　requirements　of　future　time　slots.　The　prediction　results　are　further　utilized　to　make　decisions　on　the　URLLC　resource　reservation.　Different　from　conventional　studies,　we　consider　the　influence　of　DT＇s　inaccurate　predictions　(i.e.,　the　prediction　with　error)　on　the　resource　allocations.　Specifically,　the　inaccurate　DT　prediction　results　are　considered　as　uncertain　constraints　of　the　resource　reservation　problem.　A　robust　parameter　from　the　robust　optimization　is　adopted　to　adjust　the　tradeoff　between　the　problem　uncertainty　and　solution　optimality　degree.　Further,　we　leverage　the　optimized　resource　reservation　results　to　construct　the　task　offloading　problem.　The　problem　is　decoupled　into　two　sub-problems　of　channel　resource　allocation　and　computation　resource　allocation,　respectively.　And　a　two-stage　matching　algorithm　is　developed　to　solve　each　sub-problem　based　on　the　resource　reservation　constraints.　Finally,　realistic　road　information　is　mapped　into　DT　for　simulations.　Simulation　results　validate　the　advantages　of　our　proposed　approach　by　comparing　with　existing　schemes.