Expressways　in　urban　agglomerations　are　important　in　connecting　cities,　thus　attracting　great　attention　from　researchers　in　the　expressways　risk　assessment.　However,　there　is　a　lack　of　safety　assessment　models　suitable　for　the　characteristics　of　expressways　in　Chinese　urban　agglomerations,　and　the　nature　and　mode　of　dynamic　risks　on　Chinese　highways　are　still　unclear.　Therefore,　this　study　adopts　the　Adaptive　Neural　Fuzzy　Inference　System　(ANFIS)　and　the　method　of　decision　tree,　combined　with　data　from　the　Beijing　section　of　the　Beijing　Harbin　Expressway,　to　model　the　risk　of　accident-prone　highways　in　urban　agglomerations.　To　determine　the　optimal　model,　we　evaluated　the　model＇s　bias　at　different　time　intervals.　In　addition,　key　factors　affecting　highway　safety　were　analyzed,　providing　scientific　support　for　the　risk　prevention　of　highways　in　urban　agglomerations　in　China.