Abstract
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 spatial-temporal 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.
Highlights
As it is difficult for road supply capacity to meet the rapid growth of traffic demand, traffic congestion has already become a serious problem in many regions
With the development of the Global Positioning System (GPS), by installing positioning sensors on buses, managers can obtain their spatial location in real-time, and calculate vehicle arrival time based on information such as the speed of the buses and distance between stations
In order to effectively cope with the influences of the incidents in the bus arrival prediction problem, this paper proposes a forecasting analysis method based on space feature vector
Summary
As it is difficult for road supply capacity to meet the rapid growth of traffic demand, traffic congestion has already become a serious problem in many regions. In order to effectively cope with the influences of the incidents in the bus arrival prediction problem, this paper proposes a forecasting analysis method based on space feature vector. In this method, the bus driving path is divided into several segments according to the intersection of the road and calculates the current speed of all buses on each road [2]. In order to solve the above problems, this paper proposes a station arrival prediction method based on spatial feature vector, which can solve the problem of remote dependence of bus by analyzing and calculating bus historical data. Time-based feature vector analysis can effectively solve the problem of long-range dependencies, but it cannot deal with road incidents. After a lot of training and learning, the model has achieved great results on the test set
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