Negative Binomial Random Effects Research Articles

Negative Binomial Panel Regression Modeling on Amount of Crimes In Lampung Province

Crime in Lampung province is among the 10 highest in Indonesia in 2021. This study aims to obtain a model of the number of crimes and factors influencing it using negative binomial panel regression. The data used is in the form of panel data from the Lampung Province BPS website and publications for 2017-2021. The condition of data on the number of crimes as discrete and overdispersed data makes the negative binomial panel regression method more suitable than Poisson panel regression. Overdispersion is a state where the variance of the data is greater than the mean value of the data. Overdispersion causes the standard error (SE) of the estimated value to decrease, so that variables that should not be significant become significant. The factors thought to be the cause of crime are percentage of poverty (X1), population density (X2), expenditure per capita (X3), unemployment rate (X4), regional gross domestic income (X5), and the average duration of schooling (X6). The results of the analysis obtained for the selected panel data model are the negative binomial random effects (REBN), the influencing factors being X1, X3, X4 and X5. The districts/cities with the largest individual random effects were in the Way Kanan district and the smallest were in Metro City.

Uncovering hidden cancer self-dependencies through analysis of shRNA-level dependency scores

Large-scale short hairpin RNA (shRNA) screens on well-characterized human cancer cell lines have been widely used to identify novel cancer dependencies. However, the off-target effects of shRNA reagents pose a significant challenge in the analysis of these screens. To mitigate these off-target effects, various approaches have been proposed that aggregate different shRNA viability scores targeting a gene into a single gene-level viability score. Most computational methods for discovering cancer dependencies rely on these gene-level scores. In this paper, we propose a computational method, named NBDep, to find cancer self-dependencies by directly analyzing shRNA-level dependency scores instead of gene-level scores. The NBDep algorithm begins by removing known batch effects of the shRNAs and selecting a subset of concordant shRNAs for each gene. It then uses negative binomial random effects models to statistically assess the dependency between genetic alterations and the viabilities of cell lines by incorporating all shRNA dependency scores of each gene into the model. We applied NBDep to the shRNA dependency scores available at Project DRIVE, which covers 26 different types of cancer. The proposed method identified more well-known and putative cancer genes compared to alternative gene-level approaches in pan-cancer and cancer-specific analyses. Additionally, we demonstrated that NBDep controls type-I error and outperforms statistical tests based on gene-level scores in simulation studies.

Risk factors for poor engagement with a smart pillbox adherence intervention among persons on tuberculosis treatment in Ethiopia

BackgroundNon-adherence to tuberculosis treatment increases the risk of poor treatment outcomes. Digital adherence technologies (DATs), including the smart pillbox (EvriMED), aim to improve treatment adherence and are being widely evaluated. As part of the Adherence Support Coalition to End TB (ASCENT) project we analysed data from a cluster-randomised trial of DATs and differentiated care in Ethiopia to examine individual-factors for poor engagement with the smart pillbox.MethodsData were obtained from a cohort of trial participants with drug-sensitive tuberculosis (DS-TB) whose treatment started between 1 December 2020 and 1 May 2022, and who were using the smart pillbox. Poor engagement with the pillbox was defined as (i) > 20% days with no digital confirmation and (ii) the count of days with no digital confirmation, and calculated over a two evaluation periods (56-days and 168-days). Logistic random effects regression was used to model > 20% days with no digital confirmation and negative binomial random effects regression to model counts of days with no digital confirmation, both accounting for clustering of individuals at the facility-level.ResultsAmong 1262 participants, 10.8% (133/1262) over 56-days and 15.8% (200/1262) over 168-days had > 20% days with no digital confirmation. The odds of poor engagement was less among participants in the higher stratum of socio-economic position (SEP) over 56-days. Overall, 4,689/67,315 expected doses over 56-days and 18,042/199,133 expected doses over 168-days were not digitally confirmed. Compared to participants in the poorest SEP stratum, participants in the wealthiest stratum had lower rates of days not digitally confirmed over 168-days (adjusted rate ratio [RRa]:0.79; 95% confidence interval [CI]: 0.65, 0.96). In both evaluation periods (56-days and 168-days), HIV-positive status (RRa:1.29; 95%CI: 1.02, 1.63 and RRa:1.28; 95%CI: 1.07, 1.53), single/living independent (RRa:1.31; 95%CI: 1.03, 1.67 and RRa:1.38; 95%CI: 1.16, 1.64) and separated/widowed (RRa:1.40; 95%CI: 1.04, 1.90 and RRa:1.26; 95%CI: 1.00, 1.58) had higher rates of counts of days with no digital confirmation.ConclusionPoorest SEP stratum, HIV-positive status, single/living independent and separated/ widowed were associated with poor engagement with smart pillbox among people with DS-TB in Ethiopia. Differentiated care for these sub-groups may reduce risk of non-adherence to TB treatment.

Modelling the effects of fuel price changes on road traffic collisions in the European Union using panel data

Road deaths globally have steadily climbed in recent years with the increase in motorisation. Yet in many economically developed countries they have actually fallen. Explanations for this reduction include the role of improved vehicle and highway design, better enforcement and the impact of the economic downturn, whilst there is also some evidence that microeconomic factors like changes in road fuel prices could be contributing to this situation. This paper investigates the effects of fuel prices on road collision frequency in countries where fuel prices are relatively high. Monthly panel data from 28 EU member states from 2005 to 2018 was analysed for both petrol and diesel prices, and for fatalities, total injury collisions and total collisions using: 1) random effect negative binomial (RENB); and 2) population-averaged negative binomial using generalised estimating equations (GEE). The findings indicate that higher fuel prices lead to lower road traffic deaths, injury collisions and overall collisions across all 28 EU member states. Thus, for every 10 percent increase in fuel prices, there will be a 2.6 percent and 2.2 percent reduction in fatalities for petrol and diesel models, respectively. Similarly, total collisions fall by 1.4 percent for petrol and 1.2 for diesel models, and total casualties fall by 1.6 percent for petrol and 1.4 percent for diesel with every 10 percent rise in fuel price. These results could be due to drivers reducing speed to achieve better fuel efficiency and to people driving less to save money – in particular younger, riskier drivers – hence reducing exposure. These findings suggest that policies replacing petrol and diesel vehicles with alternative fuel sources over the next 20 years could have negative repercussions for road safety if not adequately considered.

The relationship between problematic gambling severity and engagement with gambling products: Longitudinal analysis of the Emerging Adults Gambling Survey.

To measure the association between problem gambling severity and 19 different gambling activities among emerging adults (aged 16-26). An online non-probability longitudinal survey collecting data in two waves: wave 1, July/August 2019; wave 2, July/October 2020. Great Britain PARTICIPANTS: A total of 2080 young adults participating in both waves. Problem gambling scores were collected using the Problem Gambling Severity Index (PGSI). Binary variables recorded past year participation in 19 different gambling forms, ranging from lotteries to online casino and gambling-like practices within digital games (e.g. loot box purchase, skin betting). Controls included socio-demographic/economic characteristics, the Eysenck Impulsivity Scale and the number of gambling activities undertaken. Zero inflated negative binomial model lacked evidence of an effect between past year participation in any individual activities and subsequent PGSI scores. However, negative binomial random effects models for current gamblers (n= 497) showed that skin betting (incidence-rate ratio [IRR] = 2.32; 95% CI = 1.69-3.19), fixed odd betting terminals (IRR = 2.21, 95% CI = 1.61-3.05), slot/fruit machines (IRR = 1.43, 95% CI = 1.07-1.91), online betting on horse/dog races (IRR = 1.53, 95% CI = 1.17-2.00) and online betting on non-sports events (IRR = 1.44, 95% CI = 1.11-1.89) were associated with increased PGSI scores. Online casino gambling had a significant interaction by wave; the impact of online casino betting in wave 2 on PGSI scores increased by a factor of 1.61. Past year participation of young adults (aged 16-26) in certain forms of gambling does not appear to be associated with future Problem Gambling Severity Index scores. Among young adults who are current gamblers, past year participation in certain land-based (e.g. electronic gaming machines) and online forms (e.g. skin betting) of gambling appears to be strongly associated with elevated Problem Gambling Severity Index scores.

Investigating factors affecting rural crash frequency at marginal areas around cities in Iran: a random parameter modeling approach

ABSTRACT A significant portion of rural crashes in Iran occur in the marginal areas around cities. Exclusive crash frequency models should be developed to identify the factors affecting the occurrence of crashes in these areas. In this study, the rural crash data of highways leading to the city of Isfahan were employed to develop a negative binomial (NB) model as a constant-parameter model, along with random effects negative binomial (RENB) and random parameters negative binomial (RPNB) models as the models based on the random parameter approach. The modeling results indicated the superiority of the RPNB model over the two other models. According to the findings of this study, the traffic volume of roads and the proportion of local and non-local vehicles, the proportion of longitudinal distance-limit violation, the way of controlling access points, and the access to rest areas affected the crash frequency in the marginal areas around cities.

Factors influencing the usage of shared E-scooters in Chicago

The rapid popularity growth of shared e-scooters creates the necessity of understanding the determinants of shared e-scooter usage. This paper estimates the impacts of temporal variables (weather data, weekday/weekend, and gasoline prices) and time-invariant variables (socio-demographic, built environment, and neighborhood characteristics) on the shared e-scooter demand by using four months (June 2019- October 2019) period of data from the shared e-scooter pilot program in Chicago. The study employs a random-effects negative binomial (RENB) model that effectively models shared e-scooter trip origin and destination count data with over-dispersion while capturing serial autocorrelation in the data. Results of temporal variables indicate that shared e-scooter demand is higher on days when the average temperature is higher, wind speed is lower, there is less precipitation (rain), weekly gasoline prices are higher, and during the weekend. Results related to time-invariant variables indicate that densely populated areas with higher median income, mixed land use, more parks and open spaces, public bike-sharing stations, higher parking rates, and fewer crime rates generate a higher number of e-scooter trips. Moreover, census tracts with a higher number of zero-car households and workers commuting by public transit generate more shared e-scooter trips. On the other hand, results reveal mixed relationships between shared e-scooter demand and public transportation supply variables. This study's findings will help planners and policymakers make decisions and policies related to shared e-scooter services.

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

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.

Gasoline prices and their relationship to the number of fatal crashes on U.S. roads

Economic factors are assumed to have substantial effects on transportation crash trends. This study makes a comprehensive examination of the relationship between the retail gasoline price, and fatal traffic crashes from 2007 to 2016 in the U.S. Random effect negative binomial (RENB) regression models are used to investigate the relationship while controlling for the other effective factors. This is achieved by developing appropriate crash prediction models. Based on the role of gender and rider's transportation mode in interpreting the prediction results, seven models are developed to investigate the relationship between the inflation adjusted gasoline price and the number of fatal crashes in male, female, motorcyclist, non-motorcyclist, bicyclist, pedestrian, and total groups. Our findings suggest that increasing gasoline prices will not significantly alter the number of total fatal crashes. On the other hand, it is estimated that a one-dollar increase in adjusted gasoline price is associated with a 24.4% increase in the number of fatal motorcycle crashes (p-value = 3.7e-14), a 1.9% decrease in the number of fatal non-motorcycle crashes (p-value = 0.02), and a 8% decrease in the number of fatal pedestrian crashes (p-value = 0.08). There is no noticeable difference between males and females in response to the gasoline price changes.

Incidence Trends of Central-Line–Associated Bloodstream Infections in Acute-Care Hospitals, NHSN, 2009–2018

Background: Central-line–associated bloodstream infections (CLABSIs) are an important cause of healthcare-associated morbidity and mortality in the United States. CLABSI surveillance in the CDC NHSN began in 2005 and has been propelled by state CLABSI reporting requirements, first introduced in 2005, and subsequently by the CMS requirements for intensive care units (ICUs) in 2011 and select ward locations in 2015. Although trend analyses were previously reported, no recent assessment of the NHSN CLABSI incidence rate changes has been performed. In this analysis, we evaluated trends in CLABSI rates in nonneonatal ICUs and all wards reported from acute-care hospitals. Methods: CLABSI rates, including blood stream infections attributed to mucosal barrier injury reported to the NHSN from 2009 to 2018, were analyzed. To evaluate trends in CLABSI incidence and to account for the potential impact of definitional changes in catheter-associated urinary tract infections (CAUTIs) that indirectly impacted CLABSI rates, as well as the CMS mandate for select wards, we conducted an interrupted time-series analysis using negative binomial random-effects modeling with an interruption in 2015. ICUs and ward locations were analyzed separately. Models were adjusted for patient care location type and hospital-level characteristics: hospital type, medical affiliation, teaching status, bed size, number of ICU beds, and average length of inpatient stay. Random intercept and slope models were used to account for differential baseline incidence and trends among reporting hospitals. Results: The overall crude incidence of CLABSI per 1,000 central-line days decreased from 1.6 infections in 2009 to 0.9 infections in 2018, except for an increase in 2015. Similar trends were observed by location type. Among the ICUs, adjusted CLABSI incidence decreased by 10% annually in 2009–2014, increased nearly 29% in 2015, and thereafter decreased at an average of 6.8% per year. Among the wards, adjusted CLABSI incidence decreased at an average of 7.9% annually, except for a 29.3% increase in 2015. Conclusions: Substantial progress has been made in reducing CLABSIs in both ICUs and wards over the last 10 years. Indirect effects of CAUTI definitional changes may explain the immediate increase in ICUs, whereas the CMS mandate may explain the similar increase in wards in 2015. Despite this increase, these findings suggest that policies and practices aimed at prevention of CLABSI have likely been effective on a national level.Funding: NoneDisclosures: None

Modeling determinants of ridesourcing usage: A census tract-level analysis of Chicago

Ridesourcing services provided by companies like Uber, Lyft, and Didi have grown rapidly over the past decade and now serve a sizable portion of trips in many metropolitan areas. An understanding of these services (e.g. to whom, where, when, and for what purposes do they provide service?) is critical for regulating, planning, and managing urban multi-modal transportation systems effectively. Unfortunately, little is known about ridesourcing travel because private companies providing ridesourcing services were not previously subject to data sharing requirements. Fortunately, the city of Chicago recently collected and released spatially (census tract) and temporally (15-minute interval) aggregated data on ridesourcing trips collected from private companies. This study analyzes the Chicago ridesourcing data to examine factors influencing ridesourcing usage. The study employs a random-effects negative binomial (RENB) regression approach to model ridesourcing usage. Determinants considered in the model include weekend vs. weekday and weather variables as well as census tract socio-demographics and commute characteristics, land-use variables, places of interest, transit supply, parking features, and crime. The model results indicate ridesourcing demand is higher on days when temperatures are lower, there is less precipitation, and on the weekend, as well as in census tracts with (i) higher household incomes, (ii) a higher percentage of workers who carpool or take transit to work, (iii) a higher percentage of households with zero vehicles, (iv) higher population and employment density, (v) higher land-use diversity, (vi) fewer parking spots and higher parking rates, (vii) more restaurants, and (viii) more homicides. The results also demonstrate a non-linear (and insightful) relationship between ridesourcing demand and transit supply variables. The paper discusses the implications of these model results to inform transportation planning and policymaking as well as future research.

Deep neural network-based predictive modeling of road accidents

This work proposes to use deep neural networks (DNN) model for prediction of road accidents. DNN consists of two or more hidden layers with large number of nodes. Accident data of non-urban sections of eight highways were collected from official records, and dataset consists of a total of 2680 accidents. The data of 16 explanatory variables related to road geometry, traffic and road environment were collected from official records as well as through field studies. Out of a total of 222 data points of accident frequency, 148 were used for training and remaining 74 to test the models. To compare the performance of DNN-based modeling approach, gene expression programming (GEP) and random effect negative binomial (RENB) models were used. A correlation coefficient value of 0.945 (root mean square error = 5.908) was achieved by DNN in comparison with 0.914 (RMSE = 7.474) by GEP, and 0.891 (RMSE = 8.862) by RENB with the test dataset, indicating an improved performance by DNN in prediction of road accidents. In comparison with DNN, though lower value of correlation coefficient was achieved by GEP model, it quantified the effects of various variables on accident frequency and provided a ranked list of variables based upon their importance.

Modeling Ridesourcing Trip Generation: Chicago Case Study

Ridesourcing services provided by companies like Uber, Lyft, and Didi have grown rapidly over the past decade and now serve a sizable portion of trips in many metropolitan areas. An understanding of these services (e.g. to whom, where, when, and for what purposes do they provide service?) is critical for regulating, planning, and managing urban multi-modal transportation systems effectively. Unfortunately, little is known about ridesourcing travel because private companies providing ridesourcing services were not previously subject to data sharing requirements. Fortunately, the city of Chicago recently collected and released spatially (census tract) and temporally (15-minute interval) aggregated data on ridesourcing trips collected from private companies. This study analyzes the Chicago ridesourcing data to examine factors influencing ridesourcing usage. The study employs a random-effects negative binomial (RENB) regression approach to model ridesourcing usage. Determinants considered in the model include weekend vs. weekday and weather variables as well as census tract socio-demographics and commute characteristics, land-use variables, places of interest, transit supply, parking features, and crime. The model results indicate ridesourcing demand is higher on days when temperatures are lower, there is less precipitation, and on the weekend, as well as in census tracts with (i) higher household incomes, (ii) a higher percentage of workers who carpool or take transit to work, (iii) a higher percentage of households with zero vehicles, (iv) higher population and employment density, (v) higher land-use diversity, (vi) fewer parking spots and higher parking rates, (vii) more restaurants, and (viii) more homicides. The results also demonstrate a non-linear (and insightful) relationship between ridesourcing demand and transit supply variables. The paper discusses the implications of these model results to inform transportation planning and policymaking as well as future research.

Macroscopic road safety impacts of public transport: A case study of Melbourne, Australia

Mode shift from private vehicle to public transport is often considered as a potential means of improving road safety, given public transport’s lower fatality rates. However, little research has examined how public transport travel contributes to road safety at a macroscopic level. Further, there is a limited understanding of the individual effects of different public transport modes. This paper explores the effects of commuting by public transport on road safety at a macroscopic level, using Melbourne as a case study. A random effect negative binomial (RENB) and a conditional autoregressive (CAR) model are adopted to explore links between total and severe crash data to commuting mode shares and a range of other zonal explanatory factors. Overall, results show the great potential of public transport as a road safety solution. It is evident that mode shift from private vehicle to public transport (i.e. train, tram, and bus), for commuting would reduce not only total crashes, but also severe crashes. Modelling also demonstrated that CAR models outperform RENB models. In addition, results highlight safety issues related to commuting by motorbike and active transport. Effects of sociodemographic, transport network, and land use factors on crashes at the macroscopic level are also discussed.

Prosocial Identities and Youth Violence

Objective: Despite a recent surge of interest in the important role that identity change plays in the desistance process, much of the empirical work has been qualitative and conducted with small samples, usually of serious adult offenders. Drawing on a nationally representative sample of adolescents in South Korea, this study explores how the development of their prosocial identity is related to their own social bond and network and collectively how this process relates to a downward trend in violent behavior. Method: Negative binomial random effects models were estimated to assess the within-individual effects of the proposed predictor and mediators on the outcome variable. Then, longitudinal path analyses were conducted to explore the overall and specific mediation processes. Conclusion: First, there is an inverse relationship between prosocial identity and violent behavior across time. Second, our own identity of self might not be entirely a social construction based on others’ appraisals but is intimately connected to the actions that we intentionally take. Third, positive effects of a prosocial identity on subsequent violence are mediated primarily by the avoidance of association with delinquent peers. Theoretical implications and limitations are discussed.

Investigating factors of crash frequency with random effects and random parameters models: New insights from Chinese freeway study

In response to the rapid economic growth in China, its freeway system has become the longest in the world and likely will continue to expand. Unfortunately, the safety issues on freeways in China have grown as well and are of great concern to Chinese transportation authorities and drivers. While many proven safety countermeasures developed and implemented by other countries are available for reference, they may be not fully transferrable to China due to the differences in driving cultures and conditions. As a result, an investigation of China’s unique safety factors and effective relevant countermeasures are urgently needed.The study presented in this paper thoroughly investigated the factors contributing to freeway crashes in China based on detailed crash data, traffic characteristics, freeway geometry, pavement conditions, and weather conditions. To properly account for the over-dispersion of data and unobserved heterogeneity, a random effects negative binomial (RENB) model and a random parameters negative binomial (RPNB) model were applied, along with a negative binomial (NB) model. The analysis revealed a large number of crash frequency factors, including several interesting and important factors rarely studied in the past, such as the safety effects of climbing lanes. Moreover, the RENB and RPNB models were found to considerably outperform the NB model; however, although the RPNB exhibited better goodness-of-fit than the RENB model, the difference was rather small. The findings of this study shed more light on the factors influencing freeway crashes in China. The results will be useful to highway designers and engineers for creating, building, and operating safe freeways as well as to safety management departments for developing effective safety countermeasures. The study presented in this paper also provides additional guidance for choosing relevant methods to analyze safety and to identify safety factors.

Comparison of modelling methods accounting for temporal correlation in crash counts

ABSTRACTTemporal correlation in crash counts is an important issue that road safety researchers face when developing crash prediction models. Several modelling methods have been proposed to date to address this issue. Random effects negative binomial (RENB) model, random parameters negative binomial (RPNB) model, multilevel negative binomial (MLNB) model, negative multinomial (NM) model, and generalized estimating equation (GEE) model are the most frequently used ones. Although those models have been applied successfully, it is not clear to researchers what their differences are and how to make a choice among them. This study attempts to answer these questions through describing the main features of the five models, showing their connections, illustrating their applications to predict the crash frequency on basic freeway segments, and discussing their differences that can guide the choice making. The application results show that the RENB, MLNB, and RPNB models are superior to others in terms of predictability (for the current data set), capability, usability, and interpretability. Three fundamental differences, including the ways to handle temporal correlation, whether to account for other heterogeneities, and the interpretation of estimated coefficients, provide some guidance on the model choice.

Application of a random effects negative binomial model to examine crash frequency for freeways in China

The freeway system of China is the longest in the world and planned to expand in the following years. However, the safety conditions of freeways are drawing increasing concerns both from the authorities and the public in the country. The safety design and management for freeways are urgently needed considering the current safety situation and great demands of new freeways. The study presented in this paper thoroughly investigated factors affecting safety using detailed data of crashes, traffic characteristics and freeway geometry. A random effects negative binomial (RENB) model was applied to account for spatial variations within groups together with a negative binomial (NB) model. The results indicated a better goodness of fit of RENB model than the NB model. In addition, a good number of factors significantly contributed to crash, such as truck proportion, presence of climbing lane, median barrier offset, curvature and longitudinal grade, were identified. This study was expected to provide a better understanding of how traffic condition and freeway design affect safety and should be useful to freeway engineers to design safe freeways, or develop effective safety countermeasures.

Evaluating the effectiveness of Behavior-Based Safety education methods for commercial vehicle drivers

Risky driving behavior is one of the main causes of commercial vehicle related crashes. In order to achieve safer vehicle operation, safety education for drivers is often provided. However, the education programs vary in quality and may not always be successful in reducing crash rates. Behavior-Based Safety (BBS) education is a popular approach found effective by numerous studies, but even this approach varies as to the combination of frequency, mode and content used by different education providers. This study therefore evaluates and compares the effectiveness of BBS education methods. Thirty-five drivers in Shanghai, China, were coached with one of three different BBS education methods for 13 weeks following a 13-week baseline phase with no education. A random-effects negative binomial (NB) model was built and calibrated to investigate the relationship between BBS education and the driver at-fault safety-related event rate. Based on the results of the random-effects NB model, event modification factors (EMF) were calculated to evaluate and compare the effectiveness of the methods. Results show that (1) BBS education was confirmed to be effective in safety-related event reduction; (2) the most effective method among the three applied monthly face-to-face coaching, including feedback with video and statistical data, and training on strategies to avoid driver-specific unsafe behaviors; (3) weekly telephone coaching using statistics and strategies was rated by drivers as the most convenient delivery mode, and was also significantly effective.

Investments, incentives, and innovation: geographical clustering dynamics as drivers of sustainable entrepreneurship

This study attempts to fill the literature gap regarding sustainable entrepreneurship viewed through geographical clustering lenses by examining how institutional factors such as local entrepreneurship and innovation climates, knowledge networks, policies, resource availability, and awareness affect entrepreneurial firm formation in the USA. Drawing from various theoretical views within the interdisciplinary literature on the topic, we develop cross-cluster hypotheses regarding firm formation and geographic clustering of clean-technology companies. We test them using a negative binomial random effects model in a panel sample of US clean-technology startup firms and find general support.