scholarly journals Investigating Pedestrian Injury Crashes on Modern Roundabouts in Addis Ababa, Ethiopia

2015 ◽  
Vol 2512 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Getu Segni Tulu ◽  
M. Mazharul Haque ◽  
Simon Washington ◽  
Mark J. King
Keyword(s):  
Low Income ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Addis Ababa ◽  
Random Effect ◽  
Pedestrian Safety ◽  
Fit Statistics ◽  
Traffic Characteristics ◽  
Multiple Observations ◽  
Pedestrian Crashes

Pedestrian crashes represent about 40% of total fatal crashes in low-income developing countries. Although many pedestrian crashes in these countries occur at unsignalized intersections such as roundabouts, studies focusing on this issue are limited. The objective of this study was to develop safety performance functions for pedestrian crashes at modern roundabouts to identify significant roadway geometric, traffic, and land use characteristics related to pedestrian safety. Detailed data, including various forms of exposure, geometric and traffic characteristics, and spatial factors such as proximity to schools and to drinking establishments were collected from a sample of 22 modern roundabouts in Addis Ababa, Ethiopia, representing about 56% of such roundabouts in Addis Ababa. To account for spatial correlation resulting from multiple observations at a roundabout, both the random effect Poisson (REP) and random effect negative binomial (RENB) regression models were estimated. Model goodness-of-fit statistics revealed a marginally superior fit of the REP model to the data compared with the RENB model. Pedestrian crossing volume and the product of traffic volumes along major and minor roads had significant and positive associations with pedestrian crashes at roundabouts. The presence of a public transport (bus or taxi) terminal beside a roundabout was associated with increased pedestrian crashes. Although the maximum gradient of an approach road was negatively associated with pedestrian safety, the provision of a raised median along an approach appeared to increase pedestrian safety at roundabouts. Remedial measures were identified for combating pedestrian safety problems at roundabouts in the context of a developing country.

Insights from Integrated Geo-Location Data for Pedestrian Crashes, Demographics, and Land Uses

2020 ◽  
Vol 2674 (8) ◽  
pp. 720-731
Author(s):  
Rui Guo ◽  
Zhiqiang Wu ◽  
Yu Zhang ◽  
Pei-Sung Lin ◽  
Zhenyu Wang
Keyword(s):  
Low Income ◽  
Fast Food ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Pedestrian Safety ◽  
Marginal Effect ◽  
Land Uses ◽  
Crash Frequency ◽  
Location Data ◽  
Pedestrian Crashes

This study investigates the effects of demographics and land uses on pedestrian crash frequency by integrating the contextual geo-location data. To address the issue of heterogeneity, three negative binomial models (with fixed parameters, with observed heterogeneity, and with both observed and unobserved heterogeneities) were examined. The best fit with the data was obtained by explicitly incorporating the observed and unobserved heterogeneity into the model. This highlights the need to accommodate both observed heterogeneity across neighborhood characteristics and unobserved heterogeneity in pedestrian crash frequency modeling. The marginal effect results imply that some land-use types (e.g., discount department stores and fast-food restaurants) could be candidate locations for the education campaigns to improve pedestrian safety. The observed heterogeneity of the area indicator suggests that priority should be given to more populated low-income areas for pedestrian safety, but attention is also needed for the higher-income areas with larger densities of bus stops and hotels. Moreover, three normally distributed random parameters (proportion of older adults, proportion of lower-speed roads, and density of convenience stores in the area) were identified as having random effects on the probability of pedestrian crash occurrences. Finally, the identification of pedestrian crash hot zone provides practitioners with prioritized neighborhoods (e.g., a list of areas) for developing effective pedestrian safety countermeasures.

scholarly journals Modeling Traffic Accident Occurrence at Hazardous Road Locations: A Case Study in Tunisia

2018 ◽  
Author(s):  
Fedy Ouni ◽  
Mounir Belloumi
Keyword(s):  
Goodness Of Fit ◽  
Negative Binomial ◽  
Prediction Models ◽  
Pearson Correlation ◽  
Surface Condition ◽  
Random Effect ◽  
Negative Binomial Model ◽  
Empirical Modeling ◽  
Binomial Model ◽  
Model Average

The purpose of the present study is to explore the linkage between Hazardous Road Locations-based crash counts and a variety of geometric characteristics, roadway characteristics, traffic flow characteristics and spatial features in the region of Sousse, Tunisia. For this purpose, collision data was collected from at 52 hazardous road sections including 1397 crash records for a 11-year monitoring period from January 1, 2004 to December 31, 2014 obtained from National Observatory for Information, Training, Documentation and Studies on Road Safety in Tunisia (NOITDRS). The matrix of Pearson correlation was used in order to avoid inclusion of both variables, which were highly correlated. Both the Random Effects Negative Binomial model and the Negative Binomial model were estimated. To evaluate the models, the Random Effect Negative Binomial model improves the goodness-of-fit compared to the Negative Binomial model. Average Daily Traffic volume, Curved alignment, Presence of public lighting, Visibility, Number of lane, Presence of vertical/horizontal sign, Presence of rural segment, Presence of drainage system, Roadway surface condition, Presence of paved shoulder and presence of major road were found as significant variables influencing accident occurrences. Overall, the current research contributes to the literature from empirical, modeling methodological standpoints since it was the first study conducted in Tunisia to use crash prediction models for hazardous road locations, and that portrays Tunisian reality. The research findings present advantageous insights on hazardous road locations in the region of Sousse, Tunisia and present useful planning tools for public authorities in Tunisia.

scholarly journals Pedestrian Safety in Compact and Mixed-Use Urban Environments: Evaluation of 5D Measures on Pedestrian Crashes

2022 ◽  
Vol 14 (2) ◽  
pp. 646
Author(s):  
Hyungun Sung ◽  
Sugie Lee ◽  
SangHyun Cheon ◽  
Junho Yoon
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Pedestrian Safety ◽  
Urban Environments ◽  
Vehicle Crashes ◽  
Traditional Markets ◽  
Major Injury ◽  
Mixed Use ◽  
Pedestrian Crashes ◽  
The Impact

This study examined the impact of density, diversity, design, distance to transit, and destination accessibility, five measures, known as the 5Ds, that characterize the built environment, on pedestrian–vehicle crashes in Seoul, Korea. Using spatial analysis based on 500-m grid cells, this study employed negative binomial regression models on the frequencies of three specific types of pedestrian–vehicle crashes: crashes causing death, major injury, and minor injury to pedestrians. Analysis shows that compact and mixed-use urban environments represented by 5D measures have mixed effects on pedestrian safety. Trade-off effects are found between a higher risk for all types of pedestrian crashes, and a lower risk for fatal pedestrian crashes in 5D urban environments. As a design variable, a higher number of intersections is more likely to increase some types of pedestrian crashes, including fatal crashes, a finding which warrants policy attention to promote pedestrian safety near intersection areas. This study also confirms an urgent need to secure the travel safety of pedestrians near public transit stations due to the higher risk of pedestrian crashes near such facilities. Various destinations, such as retail stores, traditional markets, and hospitals, are associated with pedestrian crashes. Pedestrian safety measures should be implemented to reduce the likelihood of pedestrian crashes near major destination facilities.

scholarly journals Factors affecting under-five mortality in Ethiopia: a multi-level negative binomial model

2019 ◽  
Author(s):  
Bisrat Misganew Geremew ◽  
Kassahun Alemu Gelaye ◽  
Alemakef Wagnew Melesse ◽  
Temesgen Yihunie Akalu ◽  
Adhanom Gebreegziabher Baraki
Keyword(s):  
Birth Interval ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Addis Ababa ◽  
Household Head ◽  
Multiple Birth ◽  
Negative Binomial Regression Model ◽  
Factors Affecting ◽  
Under Five ◽  
Significant Factors

Abstract Introduction: Under-five mortality is a key indicator of countries’ developmental status. Even though remarkable declines in under-five mortality rates, nearly 5.6 million children still die annually worldwide before their fifth birthday. The 2016 Ethiopian Demographic and Health Survey (EDHS) report revealed that 67 children per 1,000 live births died before the fifth birthday. This study was aimed at determining factors affecting under-five mortality in Ethiopia using EDHS, 2016.Methods: The data was retrieved from the EDHS 2016. A total weighted number of 11,023 under-five children were included in this study. Descriptive statistics were done and reported using tables, graphs, and texts. The multilevel negative binomial regression model was fitted to identify significant factors of under-five mortality. Adjusted Incidence rate (AIRR) with a 95% confidence interval (CI) and p-value <0.05 in the multivariable model were reported. The goodness of fit was checked using the deviance test.Results: Mother attained higher education (AIRR=0.25, 95% CI: 0.10-0.66); female-headed household (AIRR=1.32, 95%CI:1.05-1.66); age of household head (AIRR=1.07, 95%CI: 1.03,1.11); preceding birth interval ≥48 months (AIRR=0.51, 95%CI: 0.42-0.61); child’s had history of diarrhea (AIRR=1.23, 95% CI:1.08-1.41); multiple birth type (AIRR=1.80, 95% CI:1.34-2.42); mothers delivered in health facility (IRR=0.86, 95% CI:0.73,0.94), residents of Addis Ababa (AIRR=0.52, 95%CI: 0.28-0.98), and Amhara region (AIRR=1.43, 95%CI: 1.09, 1.88) were statistically significant factors to under-five mortality in Ethiopia.Conclusion: In this study, under-five mortality remains a public health problem in Ethiopia. Mothers education level, women delivered at health institution, preceding birth interval 24-35 and ≥48, and residents of Addis Ababa have reduced the incidence of under-five mortality. On the other hand, being a female household head, age of mother at first giving birth, being employment, having multiple births and having childhood diarrhea was associated with a higher incidence of under-five mortality. This finding suggests that enhancing opportunities to female education, addressing regional disparities, and encouraging mothers to deliver at health institutions will help to combat the burden of under-five mortality. Keywords: Under-five mortality, negative binomial, multilevel analysis, Ethiopia

scholarly journals United States fatal pedestrian crash hot spot locations and characteristics

2021 ◽  
Vol 14 (1) ◽  
pp. 1-23
Author(s):  
Robert James Schneider ◽  
Rebecca Sanders ◽  
Frank Proulx ◽  
Hamideh Moayyed
Keyword(s):  
New York ◽  
New York City ◽  
Low Income ◽  
Hot Spots ◽  
Hot Spot ◽  
Pedestrian Safety ◽  
The Us ◽  
Safety Strategies ◽  
Traffic Volumes ◽  
Pedestrian Crashes

US pedestrian fatalities are at their highest level in nearly three decades and account for an increasing share of total traffic fatalities (16%). To achieve the vision of a future transportation system that produces zero deaths, pedestrian safety must be improved. In this study, we screened the entire US roadway network to identify fatal pedestrian crash “hot spot” corridors: 1,000-meter-long sections of roadway where six or more fatal pedestrian crashes occurred during an eightyear period. We identified 34 hot spot corridors during 2001-2008 and 31 during 2009-2016. While only five corridors were hot spots during both analysis periods, the 60 unique hot spots had remarkably consistent characteristics. Nearly all (97%) were multilane roadways, with 70% requiring pedestrians to cross five or more lanes. More than three-quarters had speed limits of 30 mph or higher, and 62% had traffic volumes exceeding 25,000 vehicles per day. All had adjacent commercial retail and service land uses, 72% had billboards, and three-quarters were bordered by low-income neighborhoods. Corridors with these characteristics clearly have the potential to produce high numbers of pedestrian fatalities. We also used hierarchical clustering to classify the hot spots based on their roadway and surrounding landuse characteristics into three types: regional highways, urban primary arterial roadways, and New York City thoroughfares. Each context may require different safety strategies. Our results support a systemic approach to improve pedestrian safety: Agencies should identify other roadway corridors with similar characteristics throughout the US and take actions to reduce the risk of future pedestrian fatalities.

Assessment of Crash Occurrence Using Historical Crash Data and a Random Effect Negative Binomial Model: A Case Study for a Rural State

2021 ◽  
pp. 036119812110275
Author(s):  
Karla J Diaz-Corro ◽  
Leyla Coronel Moreno ◽  
Suman Mitra ◽  
Sarah Hernandez
Keyword(s):  
Serial Correlation ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Random Effect ◽  
Functional Class ◽  
Average Wind Speed ◽  
Causal Factors ◽  
Crash Data ◽  
Average Precipitation ◽  
Unit Increase

This work identifies factors that influence crash occurrence within a traffic analysis zone (TAZ) by accounting for location-specific effects and serial correlation in longitudinal crash data. This is accomplished by applying a random effect negative binomial (RENB) model. Unlike commonly used count models such as Poisson and negative binomial (NB), RENB accounts for heterogeneity and serial correlation in crash occurrence. An RENB was applied to 15 years of crash data in Arkansas with 1,817 TAZs. Four models were developed for total crashes and by severity (property damage only (PDO), injury, and fatal). RENB-estimated impacts were measured using the incidence rate ratio (IRR). The significant causal factors found to increase in observed crashes include: (i) average precipitation (a one-unit increase in average precipitation results in a 134% increase in total monthly crashes for a TAZ); (ii) average wind speed (16%); (iii) urban designation (7%); (iv) traffic volume (2%); and (v) total roadway mileage (1% for each functional class). Snow depth and days of sunshine were found to decrease the number of accidents by 15% and 2%, respectively. Employment and total population had no impact on crash occurrence. Goodness-of-fit comparisons show that RENB provides the best fit among Poisson and NB formulations. All four model diagnostics confirm the presence of over-dispersion and serial correlation indicating the necessity of RENB model estimation. The main contribution of this work is the identification of crash causal factors at the TAZ level for longitudinal data, which supports data-driven performance measurement requirements of recent federal legislation.

scholarly journals Application of Two Level Count Regression Modeling on the Determinants of Fertility among Married Women in Ethiopia

2021 ◽  
Author(s):  
Nuru Mohammed Hussen
Keyword(s):  
Sampling Design ◽  
Negative Binomial ◽  
Addis Ababa ◽  
Random Effect ◽  
Married Women ◽  
Skewed Distribution ◽  
Sub Saharan Africa ◽  
Negative Binomial Regression Model ◽  
Age At First Birth ◽  
Fertility Variation

Abstract Background: Fertility is the element of population dynamics that has a vital contribution towards changing population size and structure over time. The global population showed a major increment from time to time because of these dynamics, particularly in south Asia and sub-Saharan Africa including Ethiopia. So this study targeted on the factors affecting fertility among married women in Ethiopia through the framework of multilevel count regression analysis using EDHS 2016 data.Methods: The sampling design for EDHS 2016 was a two-stage stratified cluster sampling design, where stratification was achieved by separating every region into urban and rural areas except the Addis Ababa region. Results: Among the random sample of 6141 women in the country, 27150 births were recorded based on EDHS 2016 report. The histograms showed that the data has a positively skewed distribution extremely picked at the beginning. Two- level negative binomial regression model was fitted to spot out the determinants of fertility among married women in Ethiopia because it has the smallest value for the fit statistics and the variance of the data was higher than its mean.Conclusion: Findings from the study revealed that contraception method used, residence, educational level of women, women’s age at first birth, and proceeding birth interval were the major predictors of fertility among married women in Ethiopia. Moreover, the estimates from the random effect result revealed that there is more fertility variation between the enumeration areas than within the enumeration areas. Application of standard models by ignoring this variation ought to embrace spurious results, then multilevel modeling is recommended for such types of hierarchical data.

scholarly journals Models for Analyzing Zero-Inflated and Overdispersed Count Data: An Application to Cigarette and Marijuana Use

2018 ◽  
Vol 22 (8) ◽  
pp. 1390-1398 ◽  
Author(s):  
Brian Pittman ◽  
Eugenia Buta ◽  
Suchitra Krishnan-Sarin ◽  
Stephanie S O’Malley ◽  
Thomas Liss ◽  
...  
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Research Question ◽  
Model Fit ◽  
Cigarette Use ◽  
Zero Inflation ◽  
Fit Statistics ◽  
Spurious Results

Abstract Introduction This article describes different methods for analyzing counts and illustrates their use on cigarette and marijuana smoking data. Methods The Poisson, zero-inflated Poisson (ZIP), hurdle Poisson (HUP), negative binomial (NB), zero-inflated negative binomial (ZINB), and hurdle negative binomial (HUNB) regression models are considered. The different approaches are evaluated in terms of the ability to take into account zero-inflation (extra zeroes) and overdispersion (variance larger than expected) in count outcomes, with emphasis placed on model fit, interpretation, and choosing an appropriate model given the nature of the data. The illustrative data example focuses on cigarette and marijuana smoking reports from a study on smoking habits among youth e-cigarette users with gender, age, and e-cigarette use included as predictors. Results Of the 69 subjects available for analysis, 36% and 64% reported smoking no cigarettes and no marijuana, respectively, suggesting both outcomes might be zero-inflated. Both outcomes were also overdispersed with large positive skew. The ZINB and HUNB models fit the cigarette counts best. According to goodness-of-fit statistics, the NB, HUNB, and ZINB models fit the marijuana data well, but the ZINB provided better interpretation. Conclusion In the absence of zero-inflation, the NB model fits smoking data well, which is typically overdispersed. In the presence of zero-inflation, the ZINB or HUNB model is recommended to account for additional heterogeneity. In addition to model fit and interpretability, choosing between a zero-inflated or hurdle model should ultimately depend on the assumptions regarding the zeros, study design, and the research question being asked. Implications Count outcomes are frequent in tobacco research and often have many zeros and exhibit large variance and skew. Analyzing such data based on methods requiring a normally distributed outcome are inappropriate and will likely produce spurious results. This study compares and contrasts appropriate methods for analyzing count data, specifically those with an over-abundance of zeros, and illustrates their use on cigarette and marijuana smoking data. Recommendations are provided.

scholarly journals Cardiovascular hospitalisation before and during the financial crisis among occupations in Taiwan

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
Y Chang
Keyword(s):  
Financial Crisis ◽  
Sex Education ◽  
Negative Binomial ◽  
Random Effect ◽  
Unskilled Worker ◽  
Research Database ◽  
Skilled Workers ◽  
Occupational Groups ◽  
2008 Financial Crisis ◽  
The 2008 Financial Crisis

Abstract Background Cardiovascular diseases (CVD) were related to financial stress. Little was known about the effects of financial crisis on cardiovascular health by occupations. This study examined CVD hospitalisations before and during the 2008 financial crisis among five occupational groups in Taiwan. Methods Data were collected from the Taiwan Survey on Hypertension, Hyperglycemia and Hyperlipidaemia 2007, including 4,673 participants aged 20 and above, categorized into five types of occupations, i.e., professional & manager (PM), office clerk & administrative staff (OA), skilled work (SW), unskilled worker (UW) and non-worker (NW). We abstracted their CVD hospitalisation records in the three years before (September 2005 to August 2008) and during the 2008 financial crisis (September 2008 to August 2011) from the National Health Insurance Research Database. Using incidence rate ratios (IRRs), we compared CVD hospitalisation of the first, second, third year from September 2008 to the three-year average before September 2008 for five occupational groups. Random effect negative binomial models were performed to estimate IRRs. Results After adjusting for covariates including age, sex, education, smoking, alcohol drinking, exercise and body mass index, there was an increase of CVD hospitalisation incidence for NW in the first year of the financial crisis (IRR=1.46, 95% Confidence Interval [95% CI]=1.19-1.77); in the second year, SW had a raised risk of CVD hospitalisation (IRR= 2.71, 95% CI = 1.59-4.60). For all occupational groups, the incidence rates of CVD hospitalisation reached the peak in the third year (PM: IRR=2.68, 95% CI = 1.05-6.83; OA: IRR=2.70, 95% CI = 1.18-6.19; SW: IRR=5.13, 95% CI = 2.89-9.09; UW: IRR=2.12, 95% CI = 1.02-4.41; NW: IRR=1.85, 95% CI = 1.18-2.67). Conclusions CVD hospitalisation of all occupations were affected by the financial crisis; when non-workers were the early victims, skilled workers may be the most vulnerable in the 2008 financial crisis. Key messages This study investigated the effects of the 2008 financial crisis on cardiovascular disease hospitalization by five occupational types in Taiwan. All occupations, particularly skilled workers, were affected by the financial crisis.

scholarly journals A comparison of zero-inflated and hurdle models for modeling zero-inflated count data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Cindy Xin Feng
Keyword(s):  
Health Services ◽  
Count Data ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Simulation Studies ◽  
Final Choice ◽  
Hurdle Models ◽  
Count Distribution ◽  
Careful Assessment

AbstractCounts data with excessive zeros are frequently encountered in practice. For example, the number of health services visits often includes many zeros representing the patients with no utilization during a follow-up time. A common feature of this type of data is that the count measure tends to have excessive zero beyond a common count distribution can accommodate, such as Poisson or negative binomial. Zero-inflated or hurdle models are often used to fit such data. Despite the increasing popularity of ZI and hurdle models, there is still a lack of investigation of the fundamental differences between these two types of models. In this article, we reviewed the zero-inflated and hurdle models and highlighted their differences in terms of their data generating processes. We also conducted simulation studies to evaluate the performances of both types of models. The final choice of regression model should be made after a careful assessment of goodness of fit and should be tailored to a particular data in question.

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