多源数据融合驱动的城市快速路交通状态划分

To enhance the effectiveness of traffic state division, this paper proposes an improved fuzzy C-means clustering model based on Negative Incentive Terms (BNIT-FCM). Building upon the original FCM model, the BNIT-FCM considers the impact of the weight of traffic flow sample points and traffic flow parameters on the clustering. It introduces negative membership incentives, traffic flow weight amplification incentives, and traffic flow sample point weight amplification incentives to foster high intra-class coherence and low inter-class coherence in clustering results. Furthermore, the model introduces weighted sample points and employs weighted Euclidean distance to depict sample point relationships. Iterative formulas are derived via the Lagrange multiplier method and solved iteratively. To address the issue of low dimensionality in most traffic state division methods, this paper constructs high-dimensional feature inputs using parameters such as speed, speed standard deviation, flow, density, and road capacity obtained through multi-source data fusion. The classification accuracy of the BNIT-FCM model is evaluated through numerical simulation experiments. Results demonstrate that compared to the FCM model and Improved Fuzzy Membership FCM model (IFMD-FCM), the ARI of the BNIT-FCM model improves by 4.17% and 3.56% respectively. Using traffic flow data from both bayonet and floating cars on the North Ring Road in Shenzhen, experimental findings reveal that the silhouette coefficients of the BNIT-FCM model improve by 4.12% and 4.07% respectively compared to the FCM model and IFMD-FCM model. Additionally, utilizing multi-source fusion data, the speed and standard deviation of the BNIT-FCM model exhibit increases of 29.67% and 54.13% respectively compared to using bayonet data and floating car data alone.