Identifying and understanding the relationships between observed driving behavior over long-term periods and corresponding crash involvement rates is paramount to enhancing safety improvement programs and providing useful insights for transportation safety engineers, policy markers, insurance industries, and the public. Unlike previous data collection methods, recent advancement in mobile computing and accuracy of global positioning systems (GPS) allow researchers to monitor driving activities of large fleets of vehicles, for long-time study periods, at great detail. This study investigates the driving patterns of drivers who have and who have not experienced crashes during a 14-month study period using the longitudinally collected GPS data during a six-month Commute Atlanta study. This investigation allows an empirical investigation to assess whether drivers with recent crash experiences exhibit different driving or activity patterns (travel mileage, travel duration, speed, acceleration, speed stability duration, frequency of unfamiliar roadway activities, frequency of turn movement activities, and previous crash location exposures). This study also discusses various techniques of implementing GPS data streams in safety analyses. Finally, this study provides useful guidance for researchers who plan to evaluate the relationships between driver driving behavior and crash risk with large sample data and proposes driving behavior activity exposure metrics of individual drivers for possible safety surrogate measures as well as for driver re-training and education programs.

Early life factors play important roles in disease susceptibility in later life. However, the relationship between dietary factors in early life on childhood growth, especially linear growth, remains unclear. This research aimed to improve our understanding of the associations between dietary factors in early life (i.e., infant feeding practices and age of introduction of solid foods) and childhood growth, especially using ulnar length as a surrogate measure of length/height, in a cross-sectional study of 1634 mother-child dyads across eight study centers in the National Children's Study Formative Research in Anthropometry in the United States from 2011-2012 (Chapter 1). Chapter 2 described the data acquisition and preprocessing procedures used in this research and provide practical guidelines of data quality control. In Chapter 3, predictive models for exclusive breastfeeding (XBR) initiation and duration was developed. Discriminant analysis revealed maternal sociodemographic factors had greater discriminating abilities to predict XBR initiation and XBR for 6 months, compared to child birth characteristics and maternal perinatal factors. Chapter 4 demonstrated that ulnar length can serve as an accurate and reliable surrogate measure of recumbent length in healthy infants/children aged 0-1.9 years and of height in healthy children aged 2-5.9 years, respectively. Bland-Altman plots and mixed-effects linear regression analyses showed that the three simple and portable tools (i.e., caliper, ruler, and grid) used to measure ulnar length could be used interchangeably in terms of prediction accuracy. Chapter 5 focused on assessing the interplay among gestational weight gain (GWG), birthweight, infant feeding practices, and childhood anthropometrics. Longer duration of breastfeeding reduced the positive associations of GWG and birthweight with weight-for-age z-scores, weight-for-height/length z-scores, and body mass index-for age z-scores in non-Hispanic Whites. These findings underscore the importance of promoting breastfeeding among women with excessive GWG to mitigate childhood obesity. Longer breastfeeding and a later age at introduction of solid foods had positive effects on ulnar length, a linear growth parameter of upper extremity, in Hispanics. Future prospective research aiming to investigate the underlying mechanisms that drive ethnic variation in these associations between early life dietary factors and childhood growth is warranted (Chapter 6). / text

Cycling has always been considered a sustainable and healthy mode of transport. Moreover, during Covid-19 period, cycling was further appreciated. by citizens as an individual opportunity of mobility. As a counterpart of the growth in the num.ber ofbicyclists and of riding k:ilometres, bicyclist safety has become a challenge as the unique road transport mode with an increasing trend of crash fatalities in EU (Figure 1). When compared to the traditional road safety network screening. availability of suitable data for crashes involving bicyclists is more difficult because of underreporting and traffic flow issues. In such framework, new technologies and digital transformation in smart cities and communities is offering new opportunities of data availability which requires also different approaches for collection and analysis. An experimental test was carried out to collect data ftom different users with an instrumented bicycle equipped with Global Navigation Satellite Systems (GNSS) and cameras. A panel of experts was asked to review the collected data to identify and score the severity of the safety critical events (CSE) reaching a good consensus. Anyway, manual observation and classi.fication of CSE is a time consu.ming and unpractical approach when large amount of data must be analysed. Moreover, due to the complex correlation between precrash driving behaviour and due to high dimensionality of the data, traditional statistical methods might not be appropriate in t.bis context. Deep learning-based model have recently gained significant attention in the lit.erature for time series data analysis and for anomaly detection, but generally applied to vehicles' mobility and not to micro-mobility. We present and discuss data requirements and treatment to get suitable infonnation from the GNSS devices, the development of an experimental :framework: where convolutional neural networks (CNN) is applied to integrate multiple GPS data streams of bicycle kinematics to detect the occurrence of a CSE.