Abstract
Prediction of short-term bus passenger flow can help bus managers timely and accurately get the changes of the passenger flow and make scientific and reasonable vehicle scheduling to meet passengers' needs. In this paper, a SLMBP model is constructed to predict the bus passenger flow. The SRCC(Spearman rank correlation coefficient) method is used to determine the factors that have significant influence on passenger flow changes. The Levenberg-Marquardt algorithm is used to optimize the BP neural network to avoid getting stuck in local optimal solutions and prompt the convergence speed. A SLMBP neural network parallel algorithm is constructed to perform multiple stations prediction. The experimental results show that the SLMBP neural network parallel algorithm can not only guarantee the accuracy of short-term passenger flow prediction, but reduce the time spent on model learning and prompt the prediction speed.
Highlights
Practical experiences have shown that encouraging people to take public transportation can solve the problem of congestion faced by cities effectively
Most short-term passenger flow prediction model algorithms run in stand-alone mode and appear to be inefficient in multiple stations prediction with large-scale data
This paper designs a SLMBP neural network parallel algorithm based on Hadoop, which can greatly prompt the prediction speed
Summary
Practical experiences have shown that encouraging people to take public transportation can solve the problem of congestion faced by cities effectively. Through the analysis of the previous bus passenger flow and the prediction of future passenger number, it is possible for bus companies to take effective measures in time and ensure the allocation of bus resources to meet passengers' demand. It is of great significance to optimize urban bus system and assist decision-making. It is important to use proper model algorithms to accurately predict short-term bus passenger flow. Most short-term passenger flow prediction model algorithms run in stand-alone mode and appear to be inefficient in multiple stations prediction with large-scale data. This paper designs a SLMBP neural network parallel algorithm based on Hadoop, which can greatly prompt the prediction speed
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