Study of Cross-Correlations in Traffic Networks with Applications to Perimeter Control

2017 ◽  
Vol 2623 (1) ◽  
pp. 108-116
Author(s):  
Lele Zhang ◽  
Callum Stuart ◽  
Samithree Rajapaksha ◽  
Gentry White ◽  
Timothy Garoni
Keyword(s):  
Network Performance ◽  
Cross Correlation ◽  
Network Flows ◽  
Control Strategies ◽  
Single Point ◽  
Traffic Demand ◽  
Cross Correlations ◽  
The Mean ◽  
The Cross

A cross-correlation is proposed between network-aggregated density and flow as a natural indicator of traffic phases for two-dimensional road networks. An online estimator of the cross-correlation was studied with the use of empirical data. The result suggests that the measure can be used to identify traffic phases. To understand better the behavior of the true statistical cross-correlation, generic networks were simulated. With homogeneously distributed densities, the simulations suggested that the cross-correlation monotonically decreases with the growth of the mean density and vanishes when the network is at capacity. As a consequence, for such networks, the phase can be identified from a single point on the curve of the cross-correlation versus mean density. A case study of cross-correlation–based perimeter-control strategies was performed, with gate traffic flowing into the network when the cross-correlation was below a (negative) threshold to improve network flows. The simulation results suggest that even with anisotropic traffic demand, the cross-correlation–based control strategy can improve network performance, specifically traffic flow and density heterogeneity.

scholarly journals Investigations of Broad Line Region Structure and Kinematics Using Variability

1989 ◽  
Vol 134 ◽  
pp. 93-95
Author(s):  
C. Martin Gaskell ◽  
Anuradha P. Koratkar ◽  
Linda S. Sparke
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Sampling Interval ◽  
Broad Line ◽  
Line Flux ◽  
Line Region ◽  
Cross Correlations ◽  
The Mean ◽  
The Cross ◽  
The Continuum

Gaskell and Sparke (1986) showed that one can determine the sizes of BLRs more accurately that the mean sampling interval by cross-correlating the continuum flux time series with a line flux time series. The position of the peak in the cross-correlation function (CCF) and its shape give an indication of the BLR size. The technique is explained in detail in Gaskell and Peterson (1987). The widely propagated misunderstanding is that the method involves simply interpolating both time series and cross-correlating them (in which case the CCF is dominated by the cross-correlations of “made-up” data). Actually the method involves cross correlating the observed points in one time series (continuum, say) with the linear interpolations of the other series (line flux). The line flux time series must always be smoother than the continuum time series it is derived from. We have usually employed the method with the interpolation done both ways round and averaged them (to reduce errors due to the interpolation) and we can intercompare the two results (to investigate errors).

Generation of synthetic cross-correlated water demand time series

2013 ◽  
Vol 13 (4) ◽  
pp. 977-986 ◽  
Author(s):  
N. Ansaloni ◽  
S. Alvisi ◽  
M. Franchini
Keyword(s):  
Time Series ◽  
Water Demand ◽  
Distribution System ◽  
Water Distribution ◽  
Cross Correlation ◽  
Time Lags ◽  
Cross Correlations ◽  
The Cross ◽  
Water Districts

This paper presents a procedure for generating synthetic district-level series of hourly water demand coefficients cross-correlated in space (between districts) and time. The procedure consists of two steps: (1) generation of hourly water demand coefficients which respect, for each hour of the day, pre-assigned means and variances; and (2) introduction of the cross-correlation at different time lags through the application of a method which implies the reordering of the data generated at step 1. The procedure was applied to a case study of the Ferrara water distribution system with the aim of generating cross-correlated synthetic series of hourly water demand coefficients for the 19 water districts making it up. It was observed that the application of the method for introducing the cross-correlation (step 2) causes numerical problems when a large number of water districts are involved and the cross-correlations are considered at many time lags; this problem is solved by carrying out an appropriate regularization of the observed cross-correlation matrix. The results obtained show that overall the proposed procedure constitutes a valid tool for generating synthetic water demand time series with pre-assigned characteristics in terms of means, variances and cross-correlation at different time lags.

scholarly journals Modeling Complex System Correlation Using Detrended Cross-Correlation Coefficient

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Keqiang Dong ◽  
Hong Zhang ◽  
You Gao
Keyword(s):  
Complex System ◽  
Time Series ◽  
Correlation Coefficient ◽  
Cross Correlation ◽  
Heavy Tails ◽  
Correlation Property ◽  
Traffic Time Series ◽  
Cross Correlations ◽  
The Cross ◽  
Active Research

The understanding of complex systems has become an area of active research for physicists because such systems exhibit interesting dynamical properties such as scale invariance, volatility correlation, heavy tails, and fractality. We here focus on traffic dynamic as an example of a complex system. By applying the detrended cross-correlation coefficient method to traffic time series, we find that the traffic fluctuation time series may exhibit cross-correlation characteristic. Further, we show that two traffic speed time series derived from adjacent sections exhibit much stronger cross-correlations than the two speed series derived from adjacent lanes. Similarly, we also demonstrate that the cross-correlation property between the traffic volume variables from two adjacent sections is stronger than the cross-correlation property between the volume variables of adjacent lanes.

Analyzing the Impact of COVID-19 on the Cross-Correlations between Financial Search Engine Data and Movie Box Office

2020 ◽  
pp. 2150021
Author(s):  
Renyu Wang ◽  
Yujie Xie ◽  
Hong Chen ◽  
Guozhu Jia
Keyword(s):  
Financial Market ◽  
Cross Correlation ◽  
Multifractal Spectrum ◽  
Movie Industry ◽  
Box Office ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross ◽  
Google Search ◽  
The Impact

This paper explores the COVID-19 influences on the cross-correlation between the movie market and the financial market. The nonlinear cross-correlations between movie box office data and Google search volumes of financial terms such as Dow Jones Industrial Average (DJIA), NASDAQ and PMI are investigated based on multifractal detrended cross-correlation analysis (MF-DCCA). The empirical results show there are nonlinear cross-correlations between movie market and financial market. Metrics such as Hurst exponents, singular exponents and multifractal spectrum demonstrate that the cross-correlation between movie market and financial market is persistent, and the cross-correlation in long term is more stable than that in short term. In the COVID-19 period, the multifractal features of cross-correlation become stronger implying that COVID-19 enhanced the complexity between the movie industry and the financial market. Furthermore, through the rolling window analysis, the Hurst exponent dynamic trends indicate that COVID-19 has a clear influence on the cross-correlation between movie market and financial market.

scholarly journals Disentangling interstellar plasma screens with pulsar VLBI: combining auto- and cross-correlations

2019 ◽  
Vol 488 (4) ◽  
pp. 4963-4971 ◽  
Author(s):  
D Simard ◽  
U-L Pen ◽  
V R Marthi ◽  
W Brisken
Keyword(s):  
Angular Position ◽  
Angular Separation ◽  
Pulsar Emission ◽  
Scattering Geometry ◽  
Interstellar Plasma ◽  
Single Station ◽  
Cross Correlations ◽  
The Mean ◽  
The Cross ◽  
Scattering Systems

Abstract Current methods of measuring distances to pulsar scattering screens rely on a single screen dominating the scintillation pattern. We present a novel technique to reconstruct the scattered flux of a pulsar and solve for the scattering geometry in cases where the scattering environment along the line of sight to the pulsar is complex and may be composed of multiple scattering screens. This technique combines interferometric visibilities with cross-correlations of single-station intensities. It takes advantage of the fact that if one considers the interference of radiation from two points in the scattered image in delay–delay rate space, the visibilities are sensitive to the mean angular position of the points, while the cross-correlated intensities are sensitive to their angular separation. By combining the visibilities and the cross-correlated intensities, it is possible to measure the angular locations of both points in the pair. We show that this technique is able to reconstruct the published scattering geometry of PSR B0834+06. We then apply this technique to one-dimensional simulations of more complicated scattering systems, where we find that it can distinguish features from different scattering screens. This technique holds promise for studies of the interstellar medium and pulsars themselves: It will allow the application of scintillometry techniques, such as resolving pulsar emission regions using interstellar scattering, to sources for which a current lack of understanding of the scattering environment has precluded their use.

Time-Series and Dynamic Cross-Correlations Analysis on Unexpected Information: Evidence from Media News and Online Postings

2019 ◽  
Vol 19 (02) ◽  
pp. 2050011
Author(s):  
Yan Li ◽  
Xiangyu Kong ◽  
Xiao Li ◽  
Zuochao Zhang
Keyword(s):  
Mass Media ◽  
New Media ◽  
Trading Volume ◽  
Cross Correlation ◽  
Short Term ◽  
Cross Correlations ◽  
The Cross ◽  
The One ◽  
The Relationship

In this paper, we investigate the relationship between unexpected information from postings and news, and the unexpected information is measured by the residual of regressions of trading volume on numbers of news or postings. We mainly find that (i) There are significant positive contemporaneous correlations between the unexpected information coming from postings and different kinds of news; the correlation between the unexpected information coming from postings and new media news is stronger than that between the unexpected information coming from postings and mass media news; (ii) The unexpected information coming from postings could cause the unexpected information coming from news, but only the unexpected information coming from the mass media news could cause that coming from postings; (iii) There are persistent power-law cross-correlations between the unexpected information coming from postings and that coming from mass media news and new media news. The cross-correlation between the unexpected information coming from postings and new media news is more persistent than the one between the unexpected information coming from postings and mass media news. The cross-correlations are all more stable in long term than in short term. We attribute our findings above to the dissemination speed of the information on the Internet.

Multiscale Multifractal Detrended Cross-Correlation Analysis of High-Frequency Financial Time Series

2019 ◽  
Vol 18 (03) ◽  
pp. 1950014 ◽  
Author(s):  
Jingjing Huang ◽  
Danlei Gu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
High Frequency ◽  
Cross Correlation ◽  
Financial Time Series ◽  
Chinese Market ◽  
Change Of Scale ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

In order to obtain richer information on the cross-correlation properties between two time series, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). This method is based on the Hurst surface and can be used to study the non-linear relationship between two time series. By sweeping through all the scale ranges of the multifractal structure of the complex system, it can present more information than the multifractal detrended cross-correlation analysis (MF-DCCA). In this paper, we use the MM-DCCA method to study the cross-correlations between two sets of artificial data and two sets of 5[Formula: see text]min high-frequency stock data from home and abroad. They are SZSE and SSEC in the Chinese market, and DJI and NASDAQ in the US market. We use Hurst surface and Hurst exponential distribution histogram to analyze the research objects and find that SSEC, SZSE and DJI, NASDAQ all show multifractal properties and long-range cross-correlations. We find that the fluctuation of the Hurst surface is related to the positive and negative of [Formula: see text], the change of scale range, the difference of national system, and the length of time series. The results show that the MM-DCCA method can give more abundant information and more detailed dynamic processes.

ON THE APPLICATION OF THE CROSS-CORRELATIONS IN THE CHINESE FUND MARKET: DESCRIPTIVE PROPERTIES AND SCALING BEHAVIORS

2011 ◽  
Vol 14 (01) ◽  
pp. 97-109
Author(s):  
WEIBING DENG ◽  
WEI LI ◽  
XU CAI ◽  
QIUPING A. WANG
Keyword(s):  
Correlation Matrix ◽  
Cross Correlation ◽  
Matrix Theory ◽  
Factorial Moment ◽  
Correlation Coefficients ◽  
Mean Value ◽  
Distribution Of Eigenvalues ◽  
Cross Correlations ◽  
The Cross ◽  
Simple Definition

On the basis of the relative daily logarithmic returns of 88 different funds in the Chinese fund market (CFM) from June 2005 to October 2009, we construct the cross-correlation matrix of the CFM. It is shown that the logarithmic returns follow an exponential distribution, which is commonly shared by some emerging markets. We hereby analyze the statistical properties of the cross-correlation coefficients in different time periods, such as the distribution, the mean value, the standard deviation, the skewness and the kurtosis. By using the method of the scaled factorial moment, we observe the intermittence phenomenon in the distribution of the cross-correlation coefficients. Also by employing the random matrix theory (RMT), we find a few isolated large eigenvalues of the cross-correlation matrix, and the distribution of eigenvalues exhibits the power-law tails. Furthermore, we study the features of the correlation strength with a simple definition.

MULTISCALE DETRENDED CROSS-CORRELATION ANALYSIS OF STOCK MARKETS

Fractals ◽  
2014 ◽  
Vol 22 (04) ◽  
pp. 1450007 ◽  
Author(s):  
YI YIN ◽  
PENGJIAN SHANG
Keyword(s):  
Correlation Analysis ◽  
Stock Markets ◽  
Cross Correlation ◽  
Statistical Characteristics ◽  
Detailed Knowledge ◽  
Stock Indices ◽  
Regional Markets ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

In this paper, we employ the detrended cross-correlation analysis (DCCA) to investigate the cross-correlations between different stock markets. We report the results of cross-correlated behaviors in US, Chinese and European stock markets in period 1997–2012 by using DCCA method. The DCCA shows the cross-correlated behaviors of intra-regional and inter-regional stock markets in the short and long term which display the similarities and differences of cross-correlated behaviors simply and roughly and the persistence of cross-correlated behaviors of fluctuations. Then, because of the limitation and inapplicability of DCCA method, we propose multiscale detrended cross-correlation analysis (MSDCCA) method to avoid "a priori" selecting the ranges of scales over which two coefficients of the classical DCCA method are identified, and employ MSDCCA to reanalyze these cross-correlations to exhibit some important details such as the existence and position of minimum, maximum and bimodal distribution which are lost if the scale structure is described by two coefficients only and essential differences and similarities in the scale structures of cross-correlation of intra-regional and inter-regional markets. More statistical characteristics of cross-correlation obtained by MSDCCA method help us to understand how two different stock markets influence each other and to analyze the influence from thus two inter-regional markets on the cross-correlation in detail, thus we get a richer and more detailed knowledge of the complex evolutions of dynamics of the cross-correlations between stock markets. The application of MSDCCA is important to promote our understanding of the internal mechanisms and structures of financial markets and helps to forecast the stock indices based on our current results demonstrated the cross-correlations between stock indices. We also discuss the MSDCCA methods of secant rolling window with different sizes and, lastly, provide some relevant implications and issue.

Dynamic Cross-Correlations between Investors’ Attention and CSI300 Index Futures

2019 ◽  
Vol 18 (04) ◽  
pp. 1950022
Author(s):  
Xiong Xiong ◽  
Kewei Xu ◽  
Dehua Shen
Keyword(s):  
Cross Correlation ◽  
Futures Market ◽  
Relative Activity ◽  
Short Term ◽  
Index Futures ◽  
Search Volume ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

Using search volume on Baidu Index as the proxy for investors’ attention, we investigate the dynamic nonlinear relationship between investors’ attention and CSI300 index futures market. Multifractal detrend cross-correlation analysis (MF-DCCA) is employed to explore the multifractal features of the cross-correlations between investors’ attention and the return and relative activity of index futures market. We find that the power-law cross-correlations between investors’ attention and CSI300 index futures market are stronger in the short term than in the long term, and the cross-correlations are significantly multifractal. Precisely, the cross-correlation between abnormal search volume (ASV) and the relative activity is persistent, and the cross-correlation between ASV and return of IF is persistent in the short term but weakly anti-persistent in the long term. Besides, we also find that, with the restriction on index futures market, the cross-correlations between investors’ attention and CSI300 index futures market become less stable.

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