Abstract
Passenger assignment of rail transit has recently attracted increasing research interest due to its potential applications in large-scale intelligent transportation systems. In the rail transit system, the foundation of passenger assignment is passengers’ origin and destination demand (OD matrix). However, due to the nature of stochastic of the short-term dynamic OD matrix, how to accurately predict the distribution of passenger travel spatio-temporally is still an open challenge. In this paper, combined multisource data with deep learning method is proposed to improve prediction of dynamic OD matrix accuracy. Firstly, multisource data such as smart card data, weather data and mobile phone data are introduced. And after quantitative analysis of the influencing factors, choosing 31 features as model inputs. Secondly, considering the superiority of Long Short-term Memory Network in time series, we improve the structure of LSTM by redesigning the hidden layer and neuron, in view of the spatio-temporal characteristics of spatio-temporal Long Short-term Memory Network (STLSTM) of rail transit passenger flow. Finally, using the Beijing subway network which had 54,056OD for verification. Extensive experiments and evaluations on a large-scale dataset well demonstrate the superiority of STLSTM over commonly used prediction models and standard LSTM model for short-term prediction of dynamic OD matrix. In addition, the application method of multisource data in OD prediction in this paper can deal with more data from other sources to further improve the information exploit effect on passenger flow law.
Highlights
Rail transit is an important means to alleviate the problems caused by urbanization such as traffic congestion and environmental pollution
MAIN CONTRIBUTIONS This paper studies the short-term OD passenger flow prediction in rail transit, and construct a short-term OD prediction model based on an spatio-temporal Long Short-term Memory Network (STLSTM) network under multisource conditions through analysis and selection of multisource data, and compare the assessments with other methods
NEURON STRUCTURE IMPROVEMENT To overcome the aforementioned disadvantages of traditional Recurrent Neural Network (RNN), and fully exploit the spatio-temporal attributes of multisource data, STLSTM is proposed in this paper to predict OD passenger flow prediction
Summary
Rail transit is an important means to alleviate the problems caused by urbanization such as traffic congestion and environmental pollution. Information and other features contained in the data, and explains the reasons in detail for choosing the feature and the quantitative processing methods, which are as follows: 1) INFLUENCE FACTOR OF OPERATION DAY The distribution of OD passenger flow on the rail transit network will show different characteristics on different operating days. The Pearson relationship value of each influence factor and OD volume is within the 95% confidence interval, indicating that there is a certain correlation between OD quantity and weather factors, but due to the low correlation coefficient, it is needed to divide OD and date to analyze on which characteristic days the weather factors mainly affect which types of OD conditions Weather factors such as weather condition, average temperature and air quality can have a certain impact on people’s travel. Considering that can be used as a supplement to smart card data, and avoiding neglecting the specific characteristics of the passenger, this paper adds the mobile phone data as a predictor into the forecast variable set
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