Infrastructure and Operational Influences on Collisions Between Trams and Left-Turning Cars

2017 ◽  
Vol 2648 (1) ◽  
pp. 10-22
Author(s):  
Christian M. Marti ◽  
Ambra Toletti ◽  
Seraina Tresch ◽  
Ulrich Weidmann
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Research Approach ◽  
Data Set ◽  
Left Turn ◽  
Binomial Regression ◽  
Traffic Volumes ◽  
The Right ◽  
Predictor Factors

This research identified infrastructural and operational factors that influenced the most common type of car–tram collision: cars making opposing turns in front of trams. Few studies have analyzed influences on car–tram collisions quantitatively, but none have explored predictor factors for opposing-turn crashes—a research gap addressed with this paper. The two largest Swiss tram networks, Basel and Zurich, were used for the analysis. A point-based research approach was chosen: all locations within a tram network at which a car could turn left (an opposing turn where traffic drives on the right) in front of a tram were identified. For each of these points, data on dependent and predictor variables were collected. This data set was analyzed with Poisson, negative binomial, and zero-inflated negative binomial regression models. The number of left-turning car–tram collisions was used as the dependent variable, while predictors were derived from a literature review; models were fitted by using all predictors and with forward variable selection by means of Akaike’s information criterion. Traffic volumes (cars and trams), tram speed, and dedicated left-turn lanes were found to be significantly associated with a higher frequency of car–tram collisions, whereas turning left to access a service rather than a road, left-turn restrictions, proximity to a tram stop, and perpendicular turning angles were significantly associated with a lower frequency of left-turning car–tram collisions. On the basis of these results, left turns across tramways should be restricted for cars. Remaining conflict points should be located close to tram stops, have limited tram speed, and feature perpendicular turning angles.

scholarly journals Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA

2019 ◽  
Vol 11 (17) ◽  
pp. 1958 ◽  
Author(s):  
Hanlin Zhou ◽  
Lin Liu ◽  
Minxuan Lan ◽  
Bo Yang ◽  
Zengli Wang
Keyword(s):  
Negative Binomial ◽  
Infrared Imaging ◽  
Negative Impact ◽  
Positive Impact ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Binomial Regression ◽  
Street Robbery ◽  
The Impact

Previous research has recognized the importance of edges to crime. Various scholars have explored how one specific type of edges such as physical edges or social edges affect crime, but rarely investigated the importance of the composite edge effect. To address this gap, this study introduces nightlight data from the Visible Infrared Imaging Radiometer Suite sensor on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS) to measure composite edges. This study defines edges as nightlight gradients—the maximum change of nightlight from a pixel to its neighbors. Using nightlight gradients and other control variables at the tract level, this study applies negative binomial regression models to investigate the effects of edges on the street robbery rate and the burglary rate in Cincinnati. The Akaike Information Criterion (AIC) of models show that nightlight gradients improve the fitness of models of street robbery and burglary. Also, nightlight gradients make a positive impact on the street robbery rate whilst a negative impact on the burglary rate, both of which are statistically significant under the alpha level of 0.05. The different impacts on these two types of crimes may be explained by the nature of crimes and the in-situ characteristics, including nightlight.

What Do We Know About Changing Economic Activity of Firms?

2019 ◽  
pp. 232102221886979
Author(s):  
Radhika Pandey ◽  
Amey Sapre ◽  
Pramod Sinha
Keyword(s):  
Economic Activity ◽  
Regression Models ◽  
Statistical Approach ◽  
Negative Binomial ◽  
Large Data ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Binomial Regression ◽  
History Of

Identification of primary economic activity of firms is a prerequisite for compiling several macro aggregates. In this paper, we take a statistical approach to understand the extent of changes in primary economic activity of firms over time and across different industries. We use the history of economic activity of over 46,000 firms spread over 25 years from CMIE Prowess to identify the number of times firms change the nature of their business. Using the count of changes, we estimate Poisson and Negative Binomial regression models to gain predictability over changing economic activity across industry groups. We show that a Poisson model accurately characterizes the distribution of count of changes across industries and that firms with a long history are more likely to have changed their primary economic activity over the years. Findings show that classification can be a crucial problem in a large data set like the MCA21 and can even lead to distortions in value addition estimates at the industry level. JEL Classifications: D22, E00, E01

scholarly journals Customer reactions to a webshop’s service quality

Empirica ◽  
2019 ◽  
Vol 47 (4) ◽  
pp. 699-731
Author(s):  
Franz Hackl ◽  
Rudolf Winter-Ebmer
Keyword(s):  
Service Quality ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Product Categories ◽  
Service Characteristics ◽  
Binomial Regression ◽  
Quality Aspects ◽  
Quality Dimensions ◽  
The Impact

Abstract E-commerce has become an integral part of the world’s economy. In this study we investigate the impact of service quality in e-tailing on site visits and consumer demand. Such an analysis is important given the almost Bertrand-like competitive structure. Our analysis is based on a large representative data set obtained from a price comparison site covering essentially the complete Austrian e-tailing market. Customer evaluations for a broad range of 15 different service characteristics are condensed using factor analysis. Negative binomial regression analysis is used to measure the impact of service quality dimensions on referral requests to online shops for different product categories. Our results show that the most important service quality aspects are those related to the ordering process and the firm’s website performance.

Estimate of the Safety Effect of All-Way Stop Control Conversion in Washington, DC

2020 ◽  
Vol 2674 (7) ◽  
pp. 77-86
Author(s):  
Zuxuan Deng ◽  
Sergiy Kyrychenko ◽  
Taylor Lee ◽  
Richard Retting
Keyword(s):  
Negative Binomial ◽  
Statistical Significance ◽  
Negative Binomial Regression ◽  
Washington Dc ◽  
Left Turn ◽  
Binomial Regression ◽  
Pedestrian Crashes ◽  
Safety Effect ◽  
Vision Zero

This study evaluated safety effects associated with converting traditional stop control (TSC) to all-way stop control (AWSC) at 53 intersections in Washington, DC. The study utilized an observational treatment group and a randomly selected comparison group. Negative binomial regression modeling was used to estimate the effect of AWSC conversion on crash outcomes, control for confounding factors, and check its statistical significance. The study also examined potential covariates that could influence AWSC crash outcomes, such as the number of legs of the intersection and the functional classification of the intersecting roads. This study found an overall 36% reduction in all crashes and a 42% reduction in injury crashes associated with converting intersections from TSC to AWSC. In addition, the study revealed a statistically significant reduction in right-angle crashes along with a statistically significant increase in straight hit pedestrian crashes. For all the other collision types, including right turn, left turn, rear-end, sideswipes, and bicycle crashes, no statistically significant coefficients were found. With many “Vision Zero” cities considering increased use of AWSC to help achieve their safety goals, it is important to understand and communicate the safety effects of AWSC.

scholarly journals Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 829
Author(s):  
Shuai Sun ◽  
Jun Bi ◽  
Montserrat Guillen ◽  
Ana M. Pérez-Marín
Keyword(s):  
Risk Score ◽  
Poisson Regression ◽  
High Speed ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Near Miss ◽  
Risk Scores ◽  
Data Set ◽  
Binomial Regression ◽  
Driving Risk

This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance. Telematics data gathered by the Internet of Vehicles (IoV) contain a large number of near-miss events which can be regarded as an alternative for modeling claims or accidents for estimating a driving risk score for a particular vehicle and its driver. Poisson regression and negative binomial regression are applied to a summary data set of 182 vehicles with one record per vehicle and to a panel data set of daily vehicle data containing four near-miss events, i.e., counts of excess speed, high speed brake, harsh acceleration or deceleration and additional driving behavior parameters that do not result in accidents. Negative binomial regression (AICoverspeed = 997.0, BICoverspeed = 1022.7) is seen to perform better than Poisson regression (AICoverspeed = 7051.8, BICoverspeed = 7074.3). Vehicles are separately classified to five driving risk levels with a driving risk score computed from individual effects of the corresponding panel model. This study provides a research basis for actuarial insurance premium calculations, even if no accident information is available, and enables a precise supervision of dangerous driving behaviors based on driving risk scores.

scholarly journals A Study of Count Regression Models for Mortality Rate

CAUCHY ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 142-151
Author(s):  
Anwar Fitrianto
Keyword(s):  
Mortality Rate ◽  
Regression Model ◽  
Count Data ◽  
Bayesian Information Criterion ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Poisson Regression Model ◽  
Binomial Regression

This paper discusses how overdispersed count data to be fit. Poisson regression model, Negative Binomial 1 regression model (NEGBIN 1) and Negative Binomial regression 2 (NEGBIN 2) model were proposed to fit mortality rate data. The method used is comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to find out which method suits the data the most. The results show that the data indeed display higher variability. Among the three models, the model preferred is NEGBIN 1 model.

scholarly journals Comparison of Zero Inflated Poisson (ZIP) Regression, Zero Inflated Negative Binomial Regression (ZINB) and Binomial Negative Hurdle Regression (HNB) to Model Daily Cigarette Consumption Data for Adult Population in Indonesia

2021 ◽  
Vol 17 (3) ◽  
pp. 357-369
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Tobacco Control ◽  
Negative Binomial ◽  
Adult Population ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Cigarette Consumption ◽  
Consumption Data ◽  
Comparison Results ◽  
Binomial Regression ◽  
Hurdle Regression

Smoking is a habit that is not good for health. Smoking habits are generally practiced by adults but it is possible for teenagers to do so.The Report of Southeast Asia Tobacco Control Alliance (SEATCA) entitled The Tobacco Control Atlas, ASEAN Region shows that Indonesia is the country with the highest number of smokers in ASEAN, namely 65.19 million people. This figure is equivalent to 34 percent of the total population of Indonesia in 2016. Based on these data, the authors are interested in modeling the daily cigarette consumption data for adults in Indonesia obtained from the 2015 Indonesia Family Life Survey. The variables used include the variable amount of cigarette consumption, education, level of welfare and income per month. The author wants to compare the best model that can be used to model the daily cigarette consumption of adults in Indonesia. The models being compared are Zero Inflated Poisson Regression (ZIP), Zero Inflated Negative Binomial Regression (ZINB) and Binomial Negative Hurdle Regression (HNB). The comparison results of the three models obtained that the best model is the Zero Inflated Negative Binomial (ZINB) Regression model because it has the smallest Akaike's Information Criterion (AIC) value.

scholarly journals Determinants of Transmission Risk During the Late Stage of the West African Ebola Epidemic

2019 ◽  
Vol 188 (7) ◽  
pp. 1319-1327
Author(s):  
Alexis Robert ◽  
W John Edmunds ◽  
Conall H Watson ◽  
Ana Maria Henao-Restrepo ◽  
Pierre-Stéphane Gsell ◽  
...  
Keyword(s):  
First Generation ◽  
Negative Binomial ◽  
Infected Individual ◽  
Negative Binomial Regression ◽  
West African ◽  
Transmission Risk ◽  
Treatment Unit ◽  
Data Set ◽  
Binomial Regression ◽  
Insight Into

Abstract Understanding risk factors for Ebola transmission is key for effective prediction and design of interventions. We used data on 860 cases in 129 chains of transmission from the latter half of the 2013–2016 Ebola epidemic in Guinea. Using negative binomial regression, we determined characteristics associated with the number of secondary cases resulting from each infected individual. We found that attending an Ebola treatment unit was associated with a 38% decrease in secondary cases (incidence rate ratio (IRR) = 0.62, 95% confidence interval (CI): 0.38, 0.99) among individuals that did not survive. Unsafe burial was associated with a higher number of secondary cases (IRR = 1.82, 95% CI: 1.10, 3.02). The average number of secondary cases was higher for the first generation of a transmission chain (mean = 1.77) compared with subsequent generations (mean = 0.70). Children were least likely to transmit (IRR = 0.35, 95% CI: 0.21, 0.57) compared with adults, whereas older adults were associated with higher numbers of secondary cases. Men were less likely to transmit than women (IRR = 0.71, 95% CI: 0.55, 0.93). This detailed surveillance data set provided an invaluable insight into transmission routes and risks. Our analysis highlights the key role that age, receiving treatment, and safe burial played in the spread of EVD.

scholarly journals Comparison among Akaike Information Criterion, Bayesian Information Criterion and Vuong's test in Model Selection: A Case Study of Violated Speed Regulation in Taiwan

2019 ◽  
Vol 3 (1) ◽  
pp. 293 ◽  
Author(s):  
Kim-Hung Pho ◽  
Sel Ly ◽  
Sal Ly ◽  
T. Martin Lukusa
Keyword(s):  
Model Selection ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Future Research ◽  
Data Set ◽  
Research Issues ◽  
Creative Commons ◽  
Advantages And Disadvantages

When doing research on scientific issues, it is very significant if our research issues are closely connected to real applications. In reality, when analyzing data in practice, there are frequently several models that can appropriate to the survey data. Hence, it is necessary to have a standard criterion to choose the most ecient model. In this article, our primary interest is to compare and discuss about the criteria for selecting a model and its applications. The authors provide approaches and procedures of these methods and apply to the traffic violation data where we look for the most appropriate model among Poisson regression, Zero-inflated Poisson regression and Negative binomial regression to capture between number of violated speed regulations and some factors including distance covered, motorcycle engine and age of respondents by using AIC, BIC and Vuong's test. Based on results on the training, validation and test data set, we find that the criteria AIC and BIC are more consistent and robust performance in model selection than the Vuong's test. In the present paper, the authors also discuss about advantages and disadvantages of these methods and provide some of the suggestions with potential directions in future research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

scholarly journals Failing to Complain: Do Nursing Homes with more Residents with Dementia have Fewer Complaints?

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 847-848
Author(s):  
Kallol Kumar Bhattacharyya ◽  
Lindsay Peterson ◽  
John Bowblis ◽  
Kathryn Hyer
Keyword(s):  
Negative Binomial ◽  
Tracking System ◽  
Negative Binomial Regression ◽  
Policy Implications ◽  
Data Set ◽  
For Profit ◽  
Binomial Regression ◽  
Resource File ◽  
Area Health

Abstract The majority of nursing home (NH) residents have Alzheimer’s Disease or Related Dementias (ADRD). However, the association of ADRD prevalence and NH quality is unclear. The objective of the current study is to understand the association of NH characteristics, including the proportion of ADRD residents, with the prevalence of NH complaints as an indicator of quality of care and quality of life. We merged data from the ASPEN Complaints/Incident Tracking System with national NH data from the Certification and Survey Provider Enhanced Reports, the Minimum Data Set, the Area Health Resource File, and zip-code level rural-urban codes in 2017. Three groups of NHs were created, including those whose proportion of residents with ADRD was in the top decile (i.e., high-dementia NHs (N=1,473)) and those whose proportion of ADRD residents was in the lowest decile (i.e., low-dementia NHs (N=1,524)). Bivariate results revealed high-ADRD NHs had higher percentages of Medicaid-paying residents, were less likely to be for-profit and chain-affiliated, had lower staffing hours and lower percentages of Black, Hispanic, and Asian residents. Using NHs in the middle deciles as reference, negative binomial regression models showed that having a low proportion of ADRD residents was significantly associated with higher numbers of total complaints (p<.001) and substantiated complaints (p<.001), whereas having a high proportion of ADRD residents was significantly associated with lower numbers of substantiated complaints (p=.001). The findings suggest the proportion of residents with ADRD in NHs is associated with quality, as measured by complaints. Policy implications of these findings will be discussed.

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