scholarly journals Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors

Safety ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 20
Author(s):  
Giulia Pulvirenti ◽  
Natalia Distefano ◽  
Salvatore Leonardi ◽  
Tomaz Tollazzi
Keyword(s):  
Driving Behavior ◽  
Chi Square ◽  
Interaction Detection ◽  
Driving Behaviors ◽  
Chaid Analysis ◽  
Unsafe Behaviors ◽  
Road Study

This study investigated the nature and causes of unsafe driving behavior at roundabouts through an on-road study. Four urban double-lane roundabouts with different layouts were selected for an on-road study. Sixty-six drivers (41 males and 25 females) aged 18–65 years took part in the study. Unsafe behaviors observed during the in situ survey were divided into three different categories: entry unsafe behaviors, circulation unsafe behaviors, and exit unsafe behaviors. Three chi-square automatic interaction detection (CHAID) analyses were developed in order to analyze the influence of roundabout characteristics and maneuvers on unsafe behaviors at double-lane roundabouts. The results confirmed the awareness that double-lane roundabouts are unsafe and inadvisable. More than half of unsafe driving behaviors were found to be entry unsafe behaviors. Moreover, the entry radius was found to be the geometric variable most influencing unsafe driving behaviors.

scholarly journals Data mining and mall users profile

2012 ◽  
Vol 12 (1) ◽  
pp. 195-207 ◽  
Author(s):  
Marina Romeo Delgado ◽  
Núria Codina Mata ◽  
Montserrat Yepes Baldó ◽  
José Vicente Pestana Montesinos ◽  
Joan Guardia Olmos
Keyword(s):  
Data Mining ◽  
Empirical Research ◽  
Consumer Response ◽  
Demographic Variables ◽  
Online Questionnaire ◽  
Chi Square ◽  
Effective Strategies ◽  
Interaction Detection ◽  
Chaid Analysis ◽  
Behavioral Variables

Marketing scholars have suggested a need for more empirical research on consumer response to malls, in order to have a better understanding of the variables that explain the behavior of the consumers. The segmentation methodology CHAID (Chi-square automatic interaction detection) was used in order to identify the profiles of consumers with regard to their activities at malls, on the basis of socio-demographic variables and behavioral variables (how and with whom they go to the malls). A sample of 790 subjects answered an online questionnaire. The CHAID analysis of the results was used to identify the profiles of consumers with regard to their activities at malls. In the set of variables analyzed the transport used in order to go shopping and the frequency of visits to centers are the main predictors of behavior in malls. The results provide guidelines for the development of effective strategies to attract consumers to malls and retain them there.

scholarly journals Klasifikasi Jenis Pencabutan Layanan oleh Pelanggan Indihome Menggunakan Metode Chi-Square Automatic Interaction Detection

2019 ◽  
Vol 2 (2) ◽  
pp. 80
Author(s):  
Siti Khodijatunnuriyah ◽  
Hasih Pratiwi
Keyword(s):  
Customer Satisfaction ◽  
Market Segmentation ◽  
Chi Square ◽  
Statistical Tool ◽  
Independent Variables ◽  
Interaction Detection ◽  
Chaid Analysis ◽  
Chi Squared ◽  
Degree Of Association ◽  
A Company

<p>Market segmentation is a classic topic in marketing which is never loss its attractiveness. In addition to market segmentation, customer satisfaction is important in the field of marketing. Customer satisfaction is a person's feelings after using goods or services produced by a company. High customer satisfaction shows a company's success in producing goods or services. Statistics provides many tools for segmentation research. One of statistical tool for segmentation research which takes the dependency method as an approach is Chi-Squared Automatic Interaction Detection (CHAID) analysis. CHAID analysis would provide decision tree like diagram which provide information about degree of association from dependent variable to the independent variables and the information about segments characteristic. In this case, the CHAID analysis is used to determine the type of service revocation segmentation by Indihome customers. Based on CHAID analysis, 25 segmentations were obtained, which consisted of revocation of the downgrade category of 45314 customers and the number of revocation of the Churn Out category by 11137 customers.</p><strong>Keywords : </strong>market segmentation, customer satisfaction<strong>, </strong>CHAID, Indihome

Visitors' Spending on Accommodation: A Segmentation Model using Two-Step CHAID Analysis

2021 ◽  
Vol 12 (3) ◽  
pp. 602-612
Author(s):  
Rosmini Ismail Et.al
Keyword(s):  
Classification Tree ◽  
Regression Tree ◽  
Local Economy ◽  
Chi Square ◽  
Interaction Detection ◽  
Chaid Analysis ◽  
Fourth Segment ◽  
Level 3 ◽  
Level 1 ◽  
Level 2

Tourism receipt through visitors' spending is one of the contributors that stimulate the local economy. Therefore, it is crucial to determine the factors influencing these spending preferences. This study determines factors and average spending on accommodations using segmentation techniques for Perhentian Island's visitors. Determinant factors include demographic, trip-related, and psychographic characteristics. Data were collected through a survey and run for 929 visitors using two-step Chi-Square Automatic Interaction Detection (CHAID) analysis. The analysis produces a three-level regression tree and later a classification tree. The findings documented that, level 1 consisted of four segments and were segmented according to the country of origin (COO). Overall, the Italian is a segment that has the highest average spending. The fourth segment of level 1, namely Malaysia, branched out further to level 2 and level 3. These levels were segmented based on the number of dependents during the trip and length of stay, respectively. For domestic visitors, Malaysian with dependents on the trip spend the highest. Based on the results, recommendations for the Perhentian Islands accommodation operator were to provide infrastructure to accommodate families for domestic market and marketing strategy that target Italians for the international market. The results could also assist local authorities outlining tourism planning.

PENERAPAN CHAID DENGAN PENDEKATAN SMOTE PADA KEMATIAN BALITA DI KAWASAN TIMUR INDONESIA TAHUN 2017

2020 ◽  
Vol 2019 (1) ◽  
pp. 357-367
Author(s):  
Isti Samrotul Hidayati ◽  
I Made Arcana
Keyword(s):  
Sampling Technique ◽  
Chi Square ◽  
Interaction Detection ◽  
Chi Squared

Metode Chi-squared Automatic Interaction Detection (CHAID) merupakan metode segmentasi berdasarkan hubungan variabel respon dan penjelas menggunakan uji chi-square, yang dalam penerapannya perlu memperhatikan keseimbangan data untuk meminimalkan kesalahan dalam klasifikasi. Salah satu pendekatan yang dapat digunakan pada data yang tidak seimbang adalah metode Synthetic Minority Over-sampling Technique (SMOTE). Dalam penelitian ini, metode CHAID dengan pendekatan SMOTE diterapkan pada Angka Kematian Balita (AKBa) di Kawasan Timur Indonesia (KTI). Tujuannya adalah untuk mengetahui variabel-variabel yang mencirikan kematian balita berdasarkan metode analisis CHAID yang diterapkan dan membandingkannya dengan pendekatan SMOTE. Hasil perbandingan menunjukkan bahwa pendekatan SMOTE lebih baik digunakan dengan nilai sensitivitas sebesar 48,3% dan nilai presisi sebesar 75,9%. Variabel yang signifikan mencirikan kematian balita di KTI adalah berat badan saat lahir, jenis kelahiran, status bekerja ibu dan kekayaan rumah tangga, dengan karakteristik utama adalah balita yang memiliki berat badan lahir rendah dan terlahir kembar.

Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model

2021 ◽  
pp. 036119812199485
Author(s):  
Xiao Qi ◽  
Ying Ni ◽  
Yiming Xu ◽  
Ye Tian ◽  
Junhua Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Reference Model ◽  
Numerical Test ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Car Following ◽  
Driving Behaviors ◽  
Human Driver ◽  
Normal Human ◽  
Naturalistic Driving

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

scholarly journals Exploring the Influence of E-Hailing Applications on the Taxi Industry—From the Perspective of the Drivers

2021 ◽  
Vol 10 (2) ◽  
pp. 77
Author(s):  
Yitong Gan ◽  
Hongchao Fan ◽  
Wei Jiao ◽  
Mengqi Sun
Keyword(s):  
Driving Behavior ◽  
Urban Traffic ◽  
Behavior Changes ◽  
Transportation Industry ◽  
Trajectory Data ◽  
Long Distance ◽  
Taxi Drivers ◽  
Driving Behaviors ◽  
Taxi Industry ◽  
The Times

In China, the traditional taxi industry is conforming to the trend of the times, with taxi drivers working with e-hailing applications. This reform is of great significance, not only for the taxi industry, but also for the transportation industry, cities, and society as a whole. Our goal was to analyze the changes in driving behavior since taxi drivers joined e-hailing platforms. Therefore, this paper mined taxi trajectory data from Shanghai and compared the data of May 2015 with those of May 2017 to represent the before-app stage and the full-use stage, respectively. By extracting two-trip events (i.e., vacant trip and occupied trip) and two-spot events (i.e., pick-up spot and drop-off spot), taxi driving behavior changes were analyzed temporally, spatially, and efficiently. The results reveal that e-hailing applications mine more long-distance rides and new pick-up locations for drivers. Moreover, driver initiative have increased at night since using e-hailing applications. Furthermore, mobile payment facilities save time that would otherwise be taken sorting out change. Although e-hailing apps can help citizens get taxis faster, from the driver’s perspective, the apps do not reduce their cruising time. In general, e-hailing software reduces the unoccupied ratio of taxis and improves the operating ratio. Ultimately, new driving behaviors can increase the driver’s revenue. This work is meaningful for the formulation of reasonable traffic laws and for urban traffic decision-making.

scholarly journals 709 Healthy Sleep as an important resource and buffer for intersectional discrimination: Results from Covid 19 study

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A276-A277
Author(s):  
Abhishek Pandey ◽  
Kerry Littlewood ◽  
Christine Spadola ◽  
Michelle Rosenthal ◽  
Larry Cooper ◽  
...  
Keyword(s):  
Race And Ethnicity ◽  
Online Survey ◽  
Economic Status ◽  
Grandparents Raising Grandchildren ◽  
Chi Square ◽  
Resource Potential ◽  
Important Resource ◽  
Chaid Analysis ◽  
Resource Needs ◽  
Automatic Interaction Detector

Abstract Introduction Our previous studies have highlighted sleep disparities for this underserved population, including how Grandparents Raising Grandchildren (GRG) experience troubled and disruptive sleep. Intersectional types of discrimination facing these families during COVID 19, include: race/ethnicity of self and children, income, age, essential workforce status, and impairments (mobility, vision, and hearing). This current study intends to explore how healthy sleep is an important resource (potential buffer) for GRG experiencing intersectional discrimination during COVID 19. Methods We used community partnerships to recruit 600 GRG from all fifty states in USA and several tribes to complete an online survey on their experiences with caregiving and intersectional discrimination during COVID 19. We developed an index on intersectional discrimination based on GRG lived experiences to inform the survey and used descriptive and bivariate statistics to profile this group. Chi-square Automatic Interaction Detector (CHAID) analysis was used to build a predictive model to help determine how variables in our study best merge to explain intersectional discrimination. Results Of the GRGs’, 37% were between 54–65 years and 33% cared for children 6 to 10 years for at least 5 years. The types of discrimination that were more likely to be included in intersectional discrimination include: Black or African American [83.8% (31)], my child’s race [59.5% (22)], my lower economic status [56.8% (21)], and my status as a caregiver [56.8% (21)]. The resource needs that showed the most disparity (higher rate showed higher priority/extreme concern) between those with ID and those without included: Information on how COVID impacts race and ethnicity differently (6.0 vs. 3.61), ability to pay utilities (3.7 vs. 1.99), and information on how to achieve healthy sleep (4.19 vs. 2.64). Conclusion This study suggests that GRG facing intersectional discrimination identify the importance of attaining information on how to achieve healthy sleep as an important resource to them during COVID 19. These results can be used to help mobilize resources and disseminate information for this underserved group to improve healthy sleep and also model for their extended families and communities. Support (if any) This study was conducted by the Grandfamilies Outcome Workgroup, (GrOW), with support from Generations United and Collaborative Solutions.

scholarly journals Examining Risky Driving Behavior Among Older Drivers Using Self-Reported and Objective Data

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 465-465
Author(s):  
Jennifer Zakrajsek ◽  
Lisa Molnar ◽  
David Eby ◽  
David LeBlanc ◽  
Lidia Kostyniuk ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Seat Belt ◽  
Driving Behavior ◽  
Video Data ◽  
Older Drivers ◽  
Driving Frequency ◽  
Middle Aged ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Study Results

Abstract Motor vehicle crashes represent a significant public health problem. Efforts to improve driving safety are multifaceted, focusing on vehicles, roadways, and drivers with risky driving behaviors playing integral roles in each area. As part of a study to create guidelines for developing risky driving countermeasures, 480 drivers (118 young/18-25, 183 middle-aged/35-55, 179 older/65 and older) completed online surveys measuring driving history, risky driving (frequency of engaging in distracted [using cell phone, texting, eating/drinking, grooming, reaching/interacting] and reckless/aggressive [speeding, tailgating, failing to yield right-of-way, maneuvering unsafely, rolling stops] driving behaviors), and psychosocial characteristics. A cluster analysis using frequency of the risky behaviors and seat belt use identified five risky behavior-clusters: 1) rarely/never distracted-rarely/never reckless/aggressive (n=392); 2) sometimes distracted-rarely/never reckless/aggressive (n=33); 3) sometimes distracted-sometimes reckless/aggressive (n=40); 4) often/always distracted-often/always reckless/aggressive (n=11); 5) no pattern (n=4). Older drivers were more likely in the first/lowest cluster (93.8% of older versus 84.2% of middle-aged and 59.3% of young drivers; p&lt;.0001). Fifteen older drivers participated in a follow-up study in which their vehicles were equipped with a data acquisition system that collected objective driving and video data of all trips for three weeks. Analysis of video data from 145 older driver trips indicated that older drivers engaged in at least one distracted behavior in 115 (79.3%) trips. While preliminary, this suggests considerably more frequent engagement in distracted driving than self-reported and that older drivers should not be excluded from consideration when developing risky driving behavior countermeasures. Full study results and implications will be presented.

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