Today, road safety is a great concern of traffic engineers because accidents have imposed extensive impacts on the quality of people’s lives. In order to reduce accident black spots, researchers have discovered feasible solutions to identify and prioritize high risk locations. However, their findings were shown that they cannot able to find accurate intensity of accident-prone segments along road length. According to their studies, the novelty of this study is to present a new combination method and modify previous studies based on segmentation and prioritization methods. Thus, the aim of this study is to use wavelet dynamic segmentation and the cause-oriented model by means of multi- criterion decision making method. Therefore, the results obtained on Khoy- Ivoghli road in West Azarbaijan province indicated that accident-prone segments regarding their intensity are classified as main and local segments including S4> S3> S5> S1> S7> S2> S6> S9> S8>S10. The results indicate that the S4 section with the length of 4 km and 56 accidents is ranked in the higher priority. Respectively, S10 section with the length of 4 km and 13 accidents is ranked in the lower priority of the improvement of the road safety. Moreover, by comparison the cause-oriented model with the wavelet dynamic segmentation, it was shown that the cause-oriented model has more capability and accuracy for prioritizing accident prone segments. In the future, this study may help researchers to identify accurate spots and accident prone segments with low budget.