Abstract
The subway station passenger flow prediction model can forecast passenger volume in the future. This model helps to carry out safety warnings and evacuation of passenger flow in advance. Based on the data of the Shanghai traffic card, the passenger volume in all the time intervals is clustered into three different models for prediction. Taking the Nanjing East Road Station in Shanghai as an example, the time series of passenger volumes was combined with weather data to create several supervised sequences and was converted to supervised sequences according to different values of timestep. To accelerate convergence, two artificial features were added as input. The gated recurrent unit (GRU) network model achieves accurate rolling prediction from 15 minutes to 6 hours. Finally, comparing it with the long short-term memory (LSTM) network and the back-forward propagation network (BPN), it was confirmed that the GRU network with a timestep of 1.5 hours is the best model for the long-term (more than 3 hours) traffic flow rolling prediction, while GRU with a timestep of 45 minutes has the best result for short-term rolling prediction.
Highlights
In this paper, the long short-term memory (LSTM), gated recurrent unit (GRU) and back propagation (BP) network models are constructed using the data of Shanghai subway stations and weather data as time series data, and the rolling prediction of passenger flow in the 15 minutes to 6 hours is made for short or long time periods
It is confirmed that the GRU network with a timestep of 1.5 hours has the best prediction effect for long-term prediction, while GRU with a timestep of 45 minutes has the best result for short-term prediction
This result indicates that the recursive neural network can be applied to natural language processing and to passenger flow prediction
Summary
A. PROBLEM Recently, with the development of the business circle in big cities, traffic problems have been gradually increasing. One of the most serious problems is overcrowding, which has created the hidden danger of public security and waste of time. Developing public transport is an effective way to solve congestion [1]. Intelligent operation can increase the capacity of public transport, which requires accurate prediction of passenger flow to better guide the use of passenger capacity. Many studies have analyzed the big data generated by traffic control systems to solve practical problems, such as vehicle routing problem [2]–[8]
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