Composition of high frequency ambient noise from cross-correlation: A case study using a small aperture array

Carsharing as a service has been growing rapidly worldwide. Its expansion has drawn wide attention in the research community with regard to the underlying driving factors and user characteristics. Despite these extensive investigations, there are still limited studies focusing on the examination of users using carsharing as a commuting mode. The answers to questions such as what kind of people would like to use carsharing for commuting and why they frequently use carsharing to commute are not clear. To enrich our understanding of these problems, this paper aims to investigate carsharing commuters in a mega city. Specifically, it intends to integrate the actual user order data with survey data from 1,920 participants to uncover the characteristics of carsharing commuters. Data from the Evcard carsharing systems in Shanghai were explicitly analyzed. Through descriptive analysis and logistic regression models, the characteristics and critical factors that affect the choice of carsharing as a commuting mode were captured. The results show that: 1. carsharing commuters mostly live or work in suburban areas in which public transport accessibility is limited; 2. carsharing commuters are more likely to be highly educated, in a higher income bracket, and older than other carsharing members; 3. high-frequency carsharing commuters own a reduced number of private cars; and 4. those high-frequency carsharing commuters with higher income are less sensitive to the carsharing costs caused by congestion. The findings in the study offer some insights into carsharing commuters and provide some supportive information for considering policies in developing carsharing systems in urban areas.

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Detection of ultraweak signals on the Mikhnevo small-aperture seismic array by using cross-correlation of waveforms

Doklady Earth Sciences ◽

10.1134/s1028334x15020142 ◽

2015 ◽

Vol 460 (2) ◽

pp. 189-191 ◽

Cited By ~ 5

Author(s):

V. V. Adushkin ◽

I. O. Kitov ◽

N. L. Konstantinovskaya ◽

K. S. Nepeina ◽

M. A. Nesterkina ◽

...

Keyword(s):

Cross Correlation ◽

Seismic Array ◽

Small Aperture

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Emergence rate of the time-domain Green’s function from the ambient noise cross-correlation function

The Journal of the Acoustical Society of America ◽

10.1121/1.2109059 ◽

2005 ◽

Vol 118 (6) ◽

pp. 3524-3531 ◽

Cited By ~ 122

Author(s):

Karim G. Sabra ◽

Philippe Roux ◽

W. A. Kuperman

Keyword(s):

Correlation Function ◽

Green’S Function ◽

Green's Function ◽

Time Domain ◽

Ambient Noise ◽

Cross Correlation ◽

Cross Correlation Function ◽

Emergence Rate ◽

The Time Domain ◽

Noise Cross Correlation

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HFTViz: Visualization for the exploration of high frequency trading data

Information Visualization ◽

10.1177/14738716211064921 ◽

2021 ◽

pp. 147387162110649

Author(s):

Javad Yaali ◽

Vincent Grégoire ◽

Thomas Hurtut

Keyword(s):

Portfolio Management ◽

High Frequency ◽

High Speed ◽

High Frequency Trading ◽

Domain Experts ◽

Risk Return ◽

Visualization Tools ◽

Liquidity Measures ◽

Trading Volumes

High Frequency Trading (HFT), mainly based on high speed infrastructure, is a significant element of the trading industry. However, trading machines generate enormous quantities of trading messages that are difficult to explore for financial researchers and traders. Visualization tools of financial data usually focus on portfolio management and the analysis of the relationships between risk and return. Beside risk-return relationship, there are other aspects that attract financial researchers like liquidity and moments of flash crashes in the market. HFT researchers can extract these aspects from HFT data since it shows every detail of the market movement. In this paper, we present HFTViz, a visualization tool designed to help financial researchers explore the HFT dataset provided by NASDAQ exchange. HFTViz provides a comprehensive dashboard aimed at facilitate HFT data exploration. HFTViz contains two sections. It first proposes an overview of the market on a specific date. After selecting desired stocks from overview visualization to investigate in detail, HFTViz also provides a detailed view of the trading messages, the trading volumes and the liquidity measures. In a case study gathering five domain experts, we illustrate the usefulness of HFTViz.

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