scholarly journals Analyzing Drivers’ Distractions due to Smartphone Usage: Evidence from AutoLog Dataset

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Inayat Khan ◽  
Sanam Shahla Rizvi ◽  
Shah Khusro ◽  
Shaukat Ali ◽  
Tae-Sun Chung
Keyword(s):  
Logistic Regression ◽  
Empirical Study ◽  
User Interfaces ◽  
Empirical Evaluation ◽  
Comprehensive Analysis ◽  
Steering Wheel ◽  
Context Aware ◽  
The Road ◽  
Adaptive User Interfaces ◽  
Driver Distractions

The usage of a smartphone while driving has been declared a global portent and has been admitted as a leading cause of crashes and accidents. Numerous solutions, such as Android Auto and CarPlay, are used to facilitate for the drivers by minimizing driver distractions. However, these solutions restrict smartphone usage, which is impractical in real driving scenarios. This research paper presents a comprehensive analysis of the available solutions to identify issues in smartphone activities. We have used empirical evaluation and dataset-based evaluation to investigate the issues in the existing smartphone user interfaces. The results show that using smartphones while driving can disrupt normal driving and may lead to change the steering wheel abruptly, focus off the road, and increases cognitive load, which could collectively result in a devastating situation. To justify the arguments, we have conducted an empirical study by collecting data using maxed mode survey, i.e., questionnaires and interviews from 98 drivers. The results show that existing smartphone-based solutions are least suitable due to numerous issues (e.g., complex and rich interfaces, redundant and time-consuming activities, requiring much visual and mental attention, and contextual constraints), making their effectiveness less viable for the drivers. Based on findings obtained from Ordinal Logistic Regression (OLR) models, it is recommended that the interactions between the drivers and smartphone could be minimized by developing context-aware adaptive user interfaces to overcome the chances of accidents.

scholarly journals Towards the Design of Context-Aware Adaptive User Interfaces to Minimize Drivers’ Distractions

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Inayat Khan ◽  
Shah Khusro
Keyword(s):  
Empirical Study ◽  
Real Time ◽  
User Interfaces ◽  
User Satisfaction ◽  
Steering Wheel ◽  
Context Aware ◽  
Road Accidents ◽  
Abrupt Changes ◽  
Driving Scenario ◽  
Driver Distractions

The usage of a smartphone while driving is a pervasive problem and has been acknowledged as a significant source of road accidents and crashes. Several solutions have been developed to control and minimize risky driving behavior. However, these solutions were mainly designed from the perspective of normal users to be used in a nondriving scenario. In a driving scenario, any deviation from these assumptions (e.g., touching or taping interfaces and looking to visual items) could impact driving performance. In this research paper, we aimed to design and develop a context-aware adaptive user interface framework to minimize driver distraction. The proposed framework is implemented in Android platform, namely, “DriverSense,” which is capable of adapting smartphone user interfaces based on contextual factors including driver preferences, environmental factors, and device usage in real time using adaptation rules. The proposed solution is evaluated both in real time using AutoLog application and through an empirical study by collecting data from 93 drivers through a mixed-mode survey using a questionnaire. Results obtained from AutoLog dataset show that performing activities on smartphone native interfaces while driving leads to abrupt changes in speed and steering wheel angle. However, minimal variations have been observed while performing activities on DriverSense interfaces. The results obtained from the empirical study show that the data are found to be internally consistent with 0.7 Cronbach’s alpha value. Furthermore, an Iterated Principal Factor Analysis (IPFA) retained 60 of a total of 61 measurement items with lower uniqueness values. The findings show that the proposed solution has significantly minimized the driver distractions and has positive perceptions in terms of usefulness, attitude, learnability and understandability, and user satisfaction.

Pervasive Computing in the Supermarket

2012 ◽  
pp. 172-185 ◽  
Author(s):  
Darren Black ◽  
Nils Jakob Clemmensen ◽  
Mikael B. Skov
Keyword(s):  
Empirical Study ◽  
Real World ◽  
Context Awareness ◽  
Empirical Evaluation ◽  
Context Aware ◽  
Uniform Pattern ◽  
User Attention ◽  
Context Aware System ◽  
Mobile Context ◽  
Interactive Experience

Shopping in the real world is becoming an increasingly interactive experience as stores integrate various technologies to support shoppers. Based on an empirical study of supermarket shoppers, the authors designed a mobile context-aware system called the Context-Aware Shopping Trolley (CAST). The purpose of CAST is to support shopping in supermarkets through context-awareness and acquiring user attention, thus, the authors’ interactive trolley guides and directs shoppers in the handling and finding of groceries. An empirical evaluation showed that shoppers using CAST behaved differently than shoppers using a traditional trolley. Specifically, shoppers using CAST exhibited a more uniform pattern of product collection and found products more easily while travelling a shorter distance. As such, the study finds that CAST supported the supermarket shopping activity.

Pervasive Computing in the Supermarket

2010 ◽  
Vol 2 (3) ◽  
pp. 31-43 ◽  
Author(s):  
Darren Black ◽  
Nils Jakob Clemmensen ◽  
Mikael B. Skov
Keyword(s):  
Empirical Study ◽  
Pervasive Computing ◽  
Real World ◽  
Context Awareness ◽  
Empirical Evaluation ◽  
Context Aware ◽  
User Attention ◽  
Context Aware System ◽  
Mobile Context ◽  
Interactive Experience

Shopping in the real world is becoming an increasingly interactive experience as stores integrate various technologies to support shoppers. Based on an empirical study of supermarket shoppers, the authors designed a mobile context-aware system called the Context-Aware Shopping Trolley (CAST). The purpose of CAST is to support shopping in supermarkets through context-awareness and acquiring user attention, thus, the authors’ interactive trolley guides and directs shoppers in the handling and finding of groceries. An empirical evaluation showed that shoppers using CAST behaved differently than shoppers using a traditional trolley. Specifically, shoppers using CAST exhibited a more uniform pattern of product collection and found products more easily while travelling a shorter distance. As such, the study finds that CAST supported the supermarket shopping activity.

Road Adhesion Coefficient Estimation Based on the Steering Wheel Rebound After Steering

2018 ◽  
Author(s):  
Liangyao Yu ◽  
Sheng Zheng ◽  
Xiaohui Liu ◽  
Jinghu Chang ◽  
Fei Li
Keyword(s):  
Stability Control ◽  
Front Wheel ◽  
Intelligent Vehicles ◽  
Steering Wheel ◽  
Adhesion Coefficient ◽  
The Road ◽  
Whole Process ◽  
Friction Moment ◽  
Stability Control System ◽  
The Coefficient Of Friction

Accurately estimating road adhesion coefficient is very important for vehicle stability control system. In this paper, an innovation method to estimate the road adhesion coefficient is proposed. This method can be used in vehicles without additional sensors. And this method is especially suitable to be used in the intelligent vehicle equipped with steer-by-wire (SBW) system. When vehicle steers, releasing the steering wheel suddenly will result in rebound to a certain angle. When the steer wheel turns the same angle on different road whose adhesion coefficients are different, the front wheel rebound angles are different. The friction moment between the road and tire is the main factor to prevent the tire from turning back, and the coefficient of friction is equal to road adhesion coefficient when the vehicle is stationary. In this paper, the detailed dynamical models describing the whole process of the front wheel and tire rebound are established. Furthermore, the Luenberger reduced-order disturbance observer is established to estimate the friction moment, and then the adhesion coefficient is estimated. The SBW system which is usually equipped in intelligent vehicles can control the steer moment and steer angle accurately. When the steer wheel turns to certain angle, the SBW system is able to stop outputting torque quickly and timely, which is important for improving the experiment accuracy. In this paper, the SBW system is used to conduct an experiment on different roads. The experiment results demonstrate the validity of this method.

scholarly journals PELUANG ALUMNI PENDIDIKAN MATEMATIKA FKIP UMB DALAM MENDAPATKAN PEKERJAAN DENGAN MENGGUNAKAN ANALISIS REGRESI LOGISTIK

2017 ◽  
Vol 10 (2) ◽  
pp. 85
Author(s):  
Mahyudi Mahyudi
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Mathematics Education ◽  
Survey Data ◽  
Logistic Regression Analysis ◽  
Work Experience ◽  
National Origin ◽  
College Graduation ◽  
The Road ◽  
Ordinal Logistic Regression Analysis

Graduation or college graduation become the most exciting moment for a student. In addition to successfully get a degree, they are also eager to enter the workforce. But sometimes the spirit was lost in the middle of the road. Many fresh graduates complain of difficult to get a job at this time. Every year the number of graduates to grow while jobs are not directly proportional to the increase in the number of graduates. The study analyzed what are the chances of graduates Mathematics Education FKIP Muhammadiyah University of Bengkulu in getting a job. Samples taken as many as 78 graduates between September 2015 to April 2016. The factors considered were gender, age, GPA, national origin, jobs for college and the work areas as desired. Analysis of survey data using ordinal logistic regression analysis. The results showed that the dominant factors that affect the length of the graduates in getting a job is GPA, work experience in college and the desired field of work.

scholarly journals Determining factors in MSMEs success: An empirical study in Mexico

2022 ◽  
Vol 6 (1) ◽  
pp. e384
Author(s):  
Rubén Molina-Sánchez ◽  
Domingo García-Pérez-de-Lema ◽  
Alejandra López-Salazar ◽  
Roberto Godínez-López
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Empirical Study ◽  
Small And Medium Enterprises ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Technology And Innovation ◽  
Determining Factors ◽  
Quality Technology ◽  
Medium Enterprises

This work empirically analyzes the competitive factors that help make micro, small, and medium enterprises (MSMEs) successful. To do this, an empirical study with a sample of 614 companies in Guanajuato, Mexico, has been carried out. The results of the binary logistic regression analysis show that quality, technology, and innovation are the main variables that determine a company’s success. These findings could provide guidelines to help MSMEs improve their competitiveness, and they could help public administrations better support MSME growth.

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