To　date　the　resilience　of　transport　networks　has　not　been　effectively　modelled　by　taking　into　account　the　traffic　dynamics　along　with　individual　drivers＇　learning　process　and　irrational　behaviours.　This　study　proposes　an　agent-based　day-to-day　dynamic　model　with　bounded　rationality　to　capture　traffic　evolution　and　drivers＇　inertial　behaviours　when　transport　networks　suffer　from　local　capacity　degradation,　and　variable　message　signs　are　incorporated　into　the　proposed　model　to　improve　the　resilience,　which　is　indicated　by　the　rapidity　of　recovering　to　a　new　approximation　equilibrium　after　disruptions.　We　employ　a　small　network　as　a　numerical　study　to　conduct　resilience　analysis,　and　variable　message　signs　with　different　compliance　rates　are　utilized　to　induce　traffic　flows　for　alternative　routes　when　a　given　link　of　the　network　is　subject　to　mild　(25%),　moderate　(50%),　severe　(75%)　capacity　reduction.　The　results　show　that　variable　message　signs　can　apparently　improve　the　resilience　of　the　network　in　most　of　cases,　and　a　larger　compliance　rate　of　variable　message　signs　does　not　necessarily　lead　to　better　rapidity　of　recovery　for　approximation　equilibrium.　This　study　may　provide　an　insight　into　the　resilience　analysis　and　improvement　of　transport　networks　under　different　levels　of　disruptions,　which　fully　takes　into　account　the　individual　drivers＇　day-to-day　learning　process,　behavioural　inertial　and　the　control　mechanism　of　variable　message　signs　with　different　compliance　rates.