scholarly journals Analysis of multi-state traffic flow time series properties using visibility graph

2017 ◽  
Vol 66 (23) ◽  
pp. 230501
Author(s):  
Xing Xue ◽  
Yu De-Xin ◽  
Tian Xiu-Juan ◽  
Wang Shi-Guang
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Flow Time ◽  
Visibility Graph

The Forecasting of the Elevator Traffic Flow Time Series Based on ARIMA and GP

2012 ◽  
Vol 588-589 ◽  
pp. 1466-1471 ◽  
Author(s):  
Jun Fang Li ◽  
Qun Zong
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Linear Models ◽  
Moving Average ◽  
Arima Model ◽  
Flow Time ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Hybrid Forecasting ◽  
Artificial Neural Network Ann

As one of the conventional statistical methods, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but it is difficult to explain the meaning of the hidden layers of ANN and it does not produce a mathematical equation. In this study, by combining ARIMA with genetic programming (GP), a hybrid forecasting model will be used for elevator traffic flow time series which can improve the accuracy both the GP and the ARIMA forecasting models separately. At last, simulations are adopted to demonstrate the advantages of the proposed ARIMA-GP forecasting model.

scholarly journals Traffic Flow Characteristics mining of Time Series Data Based on Phase Space Reconstruction Using Complex Networks

CONVERTER ◽  
2021 ◽  
pp. 146-161
Author(s):  
Xue Xing, Yaqi Zhai, Zhongtai Jiang, Xiaoyu LI
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Network Analysis ◽  
Traffic Flow ◽  
Complex Network ◽  
Degree Distribution ◽  
Data Flow ◽  
Flow Time ◽  
Phase Space Reconstruction ◽  
Traffic Data

Traffic flow time series is vital for mining the traditional statistical characteristics by using the theory of statistics and machine learning when its identity is a special time series. The network analysis of the traffic flow time series, who uses the complex network of time series analysis method, is designed to inquire into the special law of traffic flow time series which uses its visualization characteristics. Through the network analysis of traffic data flow, the connotation of traffic data flow can be revealed, and the relationship between all data and some data can be further studied. Therefore, it is constructed by combination with the phase space reconstruction theory. The phase space trajectory may be squeezed and the structure of attractor may change. We need to use C-C method to estimate the time delay according to the characteristics of integral parameters, and use G-P algorithm to estimate the embedding dimension to avoid it. This study can effectively reveal the motion law of the system. After constructing the complex network of traffic flow time series with various traffic parameters, the degree distribution, clustering coefficient and modularization of the representative critical threshold corresponding network are statistically analysed. The analysis results show that the new networked structure of traffic flow time series proposed in this study has strong advantages, and its core is phase space reconstruction, which can well reflect the information space of traffic dynamic fluctuation. The time series networking method based on phase space reconstruction has become a new approach to inquire into the characteristics of traffic flow time series. The degree distribution of the actual multi-traffic parameter time series construction network satisfies the characteristics of a Gaussian distribution. Their average clustering coefficients have attenuation characteristics, and their modularization degree is obvious.

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