Real time traffic navigation is an important capability in smart transportation technologies, which has been extensively studied these years. Due to the vast development of edge devices, collecting real time traffic data is no longer a problem. However, real traffic navigation is still considered to be a particularly challenging problem because of the time-varying patterns of the traffic flow and unpredictable accidents/congestion. To give accurate and reliable navigation results, predicting the future traffic flow(speed,congestion,volume,etc) in a fast and accurate way is of great importance. In this paper, we adopt the ideas of ensemble learning and develop a two-stage machine learning model to give accurate navigation results. We model the traffic flow as a time series and apply XGBoost algorithm to get accurate predictions on future traffic conditions(1st stage). We then apply the Top K Dijkstra algorithm to find a set of shortest paths from the give start point to the destination as the candidates of the output optimal path. With the prediction results in the 1st stage, we find one optimal path from the candidates as the output of the navigation algorithm. We show that our navigation algorithm can be greatly improved via EOPF(Enhanced Optimal Path Finding), which is based on neural network(2nd stage). We show that our method can be over 7% better than the method without EOPF in many situations, which indicates the effectiveness of our model.
The current conventional train hook crack repair technology is mainly used to remanufacture and repair worn hooks by laser cladding repair technology, which leads to poor crack identification due to the lack of simulation and analysis of crack data. In this regard, a multimodal information fusion-based crack repair method for train hooks is proposed. The attention mechanism based on the attributes of multimodal information fusion is used to fuse the multi-scale image alignment method and calculate the crack image region features to realize the recognition of hook cracks. Based on this, numerical simulations of train hook crack repair are performed, and the repair process is optimized. In the experiments, the proposed method is verified for the crack recognition effect. The experimental results show that the proposed method has a high recognition accuracy and ideal crack recognition effect when the proposed method is used to recognize train hook images.
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