scholarly journals An investigation on the link between driver demographic characteristics and distracted driving by using the SHRP 2 naturalistic driving data

2020 ◽  
Vol 3 (1) ◽  
pp. 1-16
Author(s):  
Haotian Cao ◽  
Zhenghao Zhang ◽  
Xiaolin Song ◽  
Hong Wang ◽  
Mingjun Li ◽  
...  
Keyword(s):  
Cell Phone ◽  
Large Scale ◽  
Population Attributable Risk ◽  
Demographic Characteristics ◽  
Driving Safety ◽  
Crash Risk ◽  
Lower Percentage ◽  
Content Type ◽  
Driving Experience ◽  
Naturalistic Driving

Purpose The purpose of this paper is to investigate the influence of driver demographic characteristics on the driving safety involving cell phone usages. Design/methodology/approach A total of 1,432 crashes and 19,714 baselines were collected for the Strategic Highway Research Program 2 naturalistic driving research. The authors used a case-control approach to estimate the prevalence and the population attributable risk percentage. The mixed logistic regression model is used to evaluate the correlation between different driver demographic characteristics (age, driving experience or their combination) and the crash risk regarding cell phone engagements, as well as the correlation among the likelihood of the cell phone engagement during the driving, multiple driver demographic characteristics (gender, age and driving experience) and environment conditions. Findings Senior drivers face an extremely high crash risk when distracted by cell phone during driving, but they are not involved in crashes at a large scale. On the contrary, cell phone usages account for a far larger percentage of total crashes for young drivers. Similarly, experienced drivers and experienced-middle-aged drivers seem less likely to be impacted by the cell phone while driving, and cell phone engagements are attributed to a lower percentage of total crashes for them. Furthermore, experienced, senior or male drivers are less likely to engage in cell phone-related secondary tasks while driving. Originality/value The results provide support to guide countermeasures and vehicle design.

scholarly journals Research on Collective Human Mobility in Shanghai Based on Cell Phone Data

2020 ◽  
Vol 9 (1) ◽  
pp. 44-62
Author(s):  
Xiyuan Ren ◽  
De Wang
Keyword(s):  
Temporal Variation ◽  
High Frequency ◽  
Cell Phone ◽  
Large Scale ◽  
Human Mobility ◽  
Indicator System ◽  
Data Sources ◽  
Census Tracts ◽  
Aggregate Distribution ◽  
Human Movements

The high-frequency mobility of a massive population has caused an enormous influence on the urban internal structure, which is unable to be described by traditional data sources. While recent advances in location-based technologies provides new opportunities for researchers to understand daily human movements and the structure as a whole. The article aims to explore human spatial movements and their aggregate distribution in Shanghai using large-scale cell phone data. The trajectory of each individual is extracted from cell phone data after data cleansing. Then, an indicator system which includes mobility intensity, mobility stability, influential range, and temporal variation is developed to describe collective human mobility features in census tracts scale. Finally, spatial elements are extracted using the indicator system and the structure of human mobility in Shanghai is discussed.

scholarly journals ICAT: Development of an Internet-Based Data Collection Method for Ecological Momentary Assessment Using Personal Cell Phones

2013 ◽  
Vol 29 (2) ◽  
pp. 140-148 ◽  
Author(s):  
Emmanuel Kuntsche ◽  
Florian Labhart
Keyword(s):  
Cell Phone ◽  
Large Scale ◽  
Cox Regression ◽  
Retention Rate ◽  
Cell Phones ◽  
Text Messages ◽  
Ease Of Use ◽  
Retention Rates ◽  
Baseline Assessment ◽  
Pilot Studies

Rapid advances in mobile data-transfer technologies offer new possibilities in the use of cell phones to conduct assessments of a person’s natural environment in real time. This paper describes features of a new Internet-based, cell phone-optimized assessment technique (ICAT), which consists of a retrospective baseline assessment combined with text messages sent to the participants’ personal cell phones providing a hyperlink to an Internet-stored cell phone-optimized questionnaire. Two participation conditions were used to test variations in response burden. Retention rates, completion rates, and response times in different subgroups were tested by means of χ² tests, Cox regression, and logistic regression. Among the 237 initial participants, we observed a retention rate of 90.3% from the baseline assessment to the cell-phone part, and 80.4% repeated participation in the 30 daily assessments. Each day, 40–70% of the questionnaires were returned, a fourth in less than 3 minutes. Qualitative interviews underscored the ease of use of ICAT. This technique appears to be an innovative, convenient, and cost-effective way of collecting data on situational characteristics while minimizing recall bias. Because of its flexibility, ICAT can be applied in various disciplines, whether as part of small pilot studies or large-scale, crosscultural, and multisite research projects.

scholarly journals Viability And Effectiveness of Mindfulness State Measurement Methodology Using Internet of Things: Proof of Concept

2021 ◽  
Author(s):  
André Godoi Chiovato ◽  
Marcelo Demarzo ◽  
Pollyana Notargiacomo
Keyword(s):  
Cell Phone ◽  
Large Scale ◽  
Statistical Data ◽  
Wearable Device ◽  
Proof Of Concept ◽  
External Temperature ◽  
State Measurement ◽  
Measurement Methodology ◽  
Mindful Attention Awareness Scale ◽  
Head Neck

Abstract Purpose: This proof-of-concept study aimed to develop and evaluate the feasibility and preliminary efficiency of a methodology to measure the mindfulness state using a wearable device (“Cap”) capable of monitoring students’ levels of full attention by means of real-time measured heart rate variability (HRV). Methods: The device was developed to export the data to the user’s cell phone via Bluetooth, which in turn stores the securely accessible data in the cloud. The autonomous wearable device consists of electronic boards of the Arduino platform that, in addition to the HRV, detect the heartbeat, the external temperature of the skull surface, and head/neck movements. Results: Preliminary statistical data using rMSSD (root mean squared successive differences), the Poincare map, the Toronto Mindfulness Scale, the Mindful Attention Awareness Scale (MAAS) and the Philadelphia Mindfulness Scale (PMS) show that increased HRV values converge to high values for the mindfulness state when the time difference between R and RN+1 sample is greater than 88 ms. Conclusion: The device proved to be viable and potentially effective for measuring the state of mindfulness. Thus, further studies should be conducted to test it on a large scale as well as in real classroom situations.

scholarly journals Wireless Sensor Network-Based Mathematical Modeling for Communication Intelligent Monitoring System

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ying Shen
Keyword(s):  
Cell Phone ◽  
Large Scale ◽  
Visual Analysis ◽  
User Behavior ◽  
Base Station ◽  
Time Base ◽  
Behavior Patterns ◽  
Behavioral Patterns ◽  
Commercial Development ◽  
Clustering Model

Knowing the behavioral patterns of city residents is of great value in formulating and adjusting urban planning strategies, such as urban road planning, urban commercial development, and urban pedestrian flow control. Based on the high penetration rate of cell phones, it is possible to indirectly understand the behavior of city residents based on the call records of users. However, the behavioral patterns of large-scale users over a long period of time can present characteristics such as large dispersion, difficult to discover patterns, and difficult to explain behavioral patterns. In this paper, we design and implement a human behavior pattern analysis system based on massive mobile communication data based on serial data modeling method and visual analysis technology. For the problem that it is difficult to capture the behavioral patterns of residents in cities in call records, this paper constructs base station trajectories based on users’ cell phone call records and uses users’ long-time base station trajectories to mine users’ potential behavioral patterns. Since users with similar activity characteristics will exhibit similar base station trajectories, this paper focuses on the similarity between text sequences and base station trajectory sequences and combines the word embedding method in natural language processing to build a Cell2vec model to identify the semantics of base stations in cities. In order to obtain the group behavior patterns of users from the base station trajectories of group users, a user clustering method based on users’ regional mobile preferences is proposed, and the results are projected using the Stochastic Neighbor Embedding (t-SNE) algorithm to expose the clustering features of large-scale cell phone users in the low-dimensional space. To address the problem that user behavior patterns are difficult to interpret, a visual analysis model with group as well as regional semantics is designed for the spatial and temporal characteristics of user behavior. Among them, the clustering model uses the distance between scatter points to map the similarity between users, which helps analysts to explore the behavioral characteristics of group users.

scholarly journals Close Relationships in Close Elections

Social Forces ◽  
2020 ◽  
Author(s):  
Byungkyu Lee
Keyword(s):  
Cell Phone ◽  
Close Relationships ◽  
Large Scale ◽  
Political Discussion ◽  
Political Dissent ◽  
Location Data ◽  
Political Views ◽  
2016 Election ◽  
Egocentric Network ◽  
Kinship Ties

Abstract Close elections are rare, but most Americans have experienced a close election at least once in their lifetime. How does intense politicization in close elections affect our close relationships? Using four national egocentric network surveys during the 1992, 2000, 2008, and 2016 election cycles, I find that close elections are associated with a modest decrease in network isolation in Americans’ political discussion networks. While Americans are more politically engaged in close elections, they also are less likely to be exposed to political dissent and more likely to deactivate their kinship ties to discuss politics. I further investigate a potential mechanism, the extent of political advertising, and show that cross-cutting exposure is more likely to disappear in states with more political ads air. To examine the behavioral consequence of close elections within American families, I revisit large-scale cell phone location data during the Thanksgiving holiday in 2016. I find that Americans are less likely to travel following close elections, and that families comprised of members with strong, opposing political views are more likely to shorten their Thanksgiving dinner. These results illuminate a process in which politicization may “close off” strong-tied relationships in the aftermath of close elections.

Large-Scale Observational Study of Drivers’ Cell Phone Use

2013 ◽  
Vol 2365 (1) ◽  
pp. 49-57 ◽  
Author(s):  
Keith E. Wenners ◽  
Michael A. Knodler ◽  
Jennifer R. Kennedy ◽  
Cole D. Fitzpatrick
Keyword(s):  
Observational Study ◽  
Cell Phone ◽  
Large Scale ◽  
Cell Phone Use

scholarly journals Predictors of short-term decay of cell phone contacts in a large scale communication network

2011 ◽  
Vol 33 (4) ◽  
pp. 245-257 ◽  
Author(s):  
Troy Raeder ◽  
Omar Lizardo ◽  
David Hachen ◽  
Nitesh V. Chawla
Keyword(s):  
Communication Network ◽  
Cell Phone ◽  
Large Scale ◽  
Short Term

scholarly journals Availability of Dutch General Practitioners for After-Hours Palliative Care

2018 ◽  
Vol 33 (3) ◽  
pp. 182-186 ◽  
Author(s):  
Fredrik M. Plat ◽  
Yvonne A.S. Peters ◽  
Paul Giesen ◽  
Marleen Smits
Keyword(s):  
Palliative Care ◽  
Cell Phone ◽  
Large Scale ◽  
Time Pressure ◽  
Home Visits ◽  
Online Questionnaire ◽  
Cross Sectional ◽  
Telephone Consultation ◽  
After Hours ◽  
Palliative Patients

Background: Continuity of care is important for palliative patients in their end of life. In the Netherlands, after-hours primary care for palliative patients is either provided by large-scale general practitioner (GP) cooperatives or GPs choose to give palliative care by themselves while they are not on duty. Aim: To examine the availability of, perceived problems by, and attitude of Dutch GPs regarding providing palliative care for their own patients outside office hours. Design and Setting: Cross-sectional observational study among 1772 GPs from 10 Dutch regions. Method: Online questionnaire among GPs affiliated with 10 GP cooperatives. Results: Five hundred twenty-four (29.6%) eligible questionnaires were returned. Of the GPs, 60.8% were personally available outside office hours for their own palliative patients on their own private cell phone and performed home visits if needed. In 33.0%, GPs were willing to make home visits in private time instigated by the GP cooperative and 26.8% were only accessible for telephone consultation by the GP cooperative. In 12.2%, the GP delegated after-hours palliative care completely to the GP cooperative. The GPs predominantly reported “time pressure” problems (17.3%) as a barrier and 61.7% stated that after-hours palliative care is the responsibility of the own GP. Conclusion: The large majority of Dutch GPs is personally available for telephone consultation and/or willing to provide palliative care for their own patients outside office hours. For the future, it is important to maintain the willingness of GPs to remain personally available for their palliative patients.

scholarly journals Cell-phone traces reveal infection-associated behavioral change

2021 ◽  
Vol 118 (6) ◽  
pp. e2005241118
Author(s):  
Ymir Vigfusson ◽  
Thorgeir A. Karlsson ◽  
Derek Onken ◽  
Congzheng Song ◽  
Atli F. Einarsson ◽  
...  
Keyword(s):  
Behavior Change ◽  
Mobile Phone ◽  
Cell Phone ◽  
Large Scale ◽  
Disease Modeling ◽  
Phone Call ◽  
Mobile Phone Use ◽  
Large Scale Data ◽  
Online Social Media ◽  
Infectious Disease Modeling

Epidemic preparedness depends on our ability to predict the trajectory of an epidemic and the human behavior that drives spread in the event of an outbreak. Changes to behavior during an outbreak limit the reliability of syndromic surveillance using large-scale data sources, such as online social media or search behavior, which could otherwise supplement healthcare-based outbreak-prediction methods. Here, we measure behavior change reflected in mobile-phone call-detail records (CDRs), a source of passively collected real-time behavioral information, using an anonymously linked dataset of cell-phone users and their date of influenza-like illness diagnosis during the 2009 H1N1v pandemic. We demonstrate that mobile-phone use during illness differs measurably from routine behavior: Diagnosed individuals exhibit less movement than normal (1.1 to 1.4 fewer unique tower locations; P<3.2×10−3), on average, in the 2 to 4 d around diagnosis and place fewer calls (2.3 to 3.3 fewer calls; P<5.6×10−4) while spending longer on the phone (41- to 66-s average increase; P<4.6×10−10) than usual on the day following diagnosis. The results suggest that anonymously linked CDRs and health data may be sufficiently granular to augment epidemic surveillance efforts and that infectious disease-modeling efforts lacking explicit behavior-change mechanisms need to be revisited.

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