Bus arrival prediction has important implications for public travel, urban dispatch, and mitigation of traffic congestion. The factors affecting urban traffic conditions are complex and changeable. As the predicted distance increases, the difficulty of traffic prediction becomes more difficult. Forecast based on historical data responds quite slowly for changes under the short-term conditions, and vehicle prediction based on real-time speed is not sufficient to predict under long-term conditions. Therefore, an arrival prediction method based on temporal vector and another arrival prediction method based on spatial vector is proposed to solve the problems of remote dependence of bus arrival and road incidents, respectively. In this paper, combining the advantages of the two prediction models, this paper proposes a long short-term memory (LSTM) and Artificial neural networks (ANN) comprehensive prediction model based on spatialtemporal features vectors. The long-distance arrival-to-station prediction is realized from the dimension of time feature, and the short-distance arrival-to-station prediction is realized from the dimension of spatial feature, thereby realizing the bus-to-station prediction. Besides, experiments were conducted and tested based on the entity dataset, and the result shows that the proposed method has high accuracy among bus arrival prediction problems. INDEX TERMS Artificial neural networks, bus arrival prediction, LSTM, spatial-temporal feature vector.

No abstract

No abstract

Invariant features are vital to domain of pattern recognition. This research develops a vision-based invariant recognizer for 2D object. We perform a recognition method which adopted KRA invariant feature extractor and used grey relational analysis. The feature extraction is to derive translation, rotation, and scaling-free features through the sequential boundary and is described with its K-curvature. Our work represents the object profile with the K-curvature to obtain the position invariant property; and then the transformation of autocorrelation is to ensure orientation-invariant property. Experimental also reveals that proposed method with either GRA or MD methods offers distinctiveness and effectiveness for part recognition.