The　road　traffic　system　is　a　time-varying,　complex　nonlinear　system.　Real-time　and　accurate　road　short-term　traffic　flow　prediction　is　the　key　to　realizing　the　traffic　flow　guidance　system.　In　order　to　improve　the　prediction　accuracy　of　short-term　traffic　flow,　this　paper　proposes　an　algorithm　based　on　the　fusion　model　of　differential　evolution　algorithm　(DE)　and　radial　basis　function　(RBF).　This　method　takes　the　fitness　function　as　the　measurement　standard,　and　uses　the　DE　algorithm　to　optimize　the　RBF　parameters　to　obtain　the　optimal　short-term　traffic　flow　prediction　value.　Through　MATLAB　simulation　experiments,　a　relatively　accurate　prediction　of　the　short-term　traffic　flow　of　the　DE-RBF　fusion　model　is　realized.　The　mean　square　error　(MSE)　and　the　average　absolute　error　percentage　of　actual　and　predicted　values　(MAPE)　analysis　index　are　introduced　as　the　evaluation　index　of　the　prediction　model.　After　comparing　with　the　two　prediction　network　models　of　radial　basis　function　(RBF)　and　wavelet　function　(WNN),　the　results　show　that　the　DE-RBF　fusion　model　proposed　in　this　paper　is　effective　and　feasible　for　short-term　traffic　flow　prediction.　©　Published　under　licence　by　IOP　Publishing　Ltd.