In　intelligent　transportation　systems,　there　are　many　tasks　that　rely　on　the　detection　of　road　congestion,　such　as　traffic　signal　scheduling　and　traffic　accident　detection.　As　traditional　methods　for　traffic　congestion　detection　are　difficult　to　use,　expensive,　and　may　cause　damage　to　the　road　surface,　this　paper　presents　a　method　for　road　congestion　detection　that　is　based　on　multidimensional　visual　features　and　a　convolutional　neural　network　(CNN).　This　method　first　detects　the　density　of　foreground　objects　by　using　a　gray-level　co-occurrence　matrix;　second,　the　speed　of　moving　objects　is　detected　by　using　the　Lucas-Kanade　optical　flow　with　pyramid　implementation.　Third,　a　Gaussian　mixture　model　is　used　to　model　the　background,　and　the　CNN　is　then　used　to　accurately　detect　the　final　foreground　from　the　candidate　foregrounds.　Finally,　the　proposed　method　performs　road　congestion　detection　in　terms　of　a　multidimensional　feature　space,　including　traffic　density,　traffic　velocity,　road　occupancy,　and　traffic　flow.　Furthermore,　we　propose　an　information　entropy　method　using　a　histogram　of　optical　flow　to　enhance　the　accuracy　and　reliability　of　road　congestion　detection.　Simulation　results　via　quantitative　and　qualitative　assessment　indicate　that　the　proposed　method　is　able　to　significantly　outperform　the　state-of-the-art　road-traffic　congestion　detection　methods　due　to　the　fusion　of　multidimensional　features　using　the　CNN.