“Vision Zero,” first adopted by Sweden in 1997, is a road safety policy that aims to achieve a transportation system having zero fatalities or serious injuries for all modes of transportation. It takes a proactive approach to road safety system by identifying risk and taking steps to prevent injuries. Historically, traffic related crashes have disproportionately impacted vulnerable communities and system users including people of color, low income individuals, seniors, children, and pedestrians, bicyclists, and transit users (who typically walk to and from public transport). These inequities are addressed in the Vision Zero framework by prioritizing interventions in areas that need safety improvements the most. In 2016, the Alexandria City Council voted unanimously to develop a “Vision Zero” policy and program as a part of its updated transportation master plan. It required an initial equity analysis to assess the impact of traffic crashes on the traditionally underserved communities / groups (groups from at least one of these categories: low-income; minority; elderly; children; limited English proficiency; persons with disabilities; and/or pedestrians/ bicyclists/ transit users). This study combines three different methods to investigate the equity issues regarding traffic safety: 1) descriptive analysis of the spatial pattern of crashes and their relationship with the demographic profiles of neighborhoods at census block group level (for 2010-2014 period); 2) descriptive analysis of the crash trends in Alexandria; and 3) exploratory regression analyses for two different units of analysis (an aggregate regression analysis of crashes at census block group, and a disaggregate regression analysis of the individual level crash reports of traffic crashes). The analysis found that the elderly, school aged children, rail/subway users, and pedestrians had a higher risk of fatalities and severe injuries in traffic crashes. Higher job densities, alcohol impairment, and speeding were significantly related to higher KSI, whereas, smaller block sizes (higher number of street segments per sq. mile area of census block group), higher housing density, and use of safety equipment were related to lower KSI. / Master of Urban and Regional Planning

In this study, a panel data set of flights made by employees at the Royal Institute of Technology (KTH) in Sweden is analyzed using generalized linear modeling approaches, with the aim to create a model with high predictive capability of the quarterly CO2 emission and the number of flights, for a year not included in the model estimation. A Zero-inflated Gamma regression model is fitted to the CO2 emission variable and a Zero-inflated Negative Binomial regression model is used for the number of flights. To build the models, cross-validation is performed with the observations from 2018 as the training set and the observations from the next year, 2019, as the test set. One at a time, the variable that best improves the prediction of the test set data (either as included in the count model or the zero-inflation model) is selected until an additional variable turns out insignificant on a 5% significance level in the estimated model. In addition to the variables in the data, three lags of the dependent variables (CO2 emission and flights) were included, as well as transformed versions of the continuous variables, and a random intercept each for the categorical variables indicating quarter and department at KTH, respectively. Neither model selected through the cross-validation process turned out to be particularly good at predicting the values for the upcoming year, but a number of variables were proven to have a statistically significant association with the respective dependent variable.

The determinants of health care use among rural, low-income mothers and their children were assessed using a multi-state, longitudinal data set, Rural Families Speak. The results indicate that rural mothers’ decisions regarding health care utilization for themselves and for their child can be best modeled using a simultaneous systems approach to negative binomial regression. Mothers’ visits to a health care provider increased with higher self-assessed depression scores, increased number of child’s doctor visits, greater numbers of total children in the household, greater numbers of chronic conditions, need for prenatal or post-partum care, development of a new medical condition, and having health insurance (Medicaid/equivalent and HMO/private). Child’s visits to a health care provider, on the other hand, increased with greater numbers of chronic conditions, development of a new medical condition, and increased mothers’ visits to a doctor. Child’s utilization of pediatric health care services decreased with higher levels of maternal depression, greater numbers of total children in the household, if the mother had HMO/private health care coverage, if the mother was pregnant, and if the mother was Latina/African American. Mother’s use of health care services decreased with her age, increased number of child’s chronic conditions, income as a percent of the federal poverty line, and if child had HMO/private health care insurance. The study expands the econometric techniques available for assessing maternal and pediatric health care use and the results contribute to an understanding of how rural, low-income mothers choose the level of health care services use for themselves and for their child. Additionally, the results would assist in formulating policies to reorient the type of health care services provided to this vulnerable population.