In the past two decades, cell phone and smartphone use in the United States has increased substantially. Although mobile phones provide a convenient way for people to communicate, the distraction caused by the use of these devices has led to unintended traffic safety and operational consequences. Although it is well recognized that distracted driving is extremely dangerous for all road users (including pedestrians), the potential impacts of distracted walking have not been as comprehensively studied. Although practitioners should design facilities with the safety, efficiency, and comfort of pedestrians in mind, it is still important to investigate certain pedestrian behaviors at existing facilities to minimize the risk of pedestrian–vehicle crashes, and to reduce behaviors that may unnecessarily increase delay at signalized intersections. To gain new insights into factors associated with distracted walking, pedestrian violations, and walking speed, 3,038 pedestrians were observed across four signalized intersections in New York and Arizona using high-definition video cameras. The video data were reduced and summarized, and an ordinary least squares (OLS) regression model was estimated to analyze factors affecting walking speeds. In addition, binary logit models were estimated to analyze both pedestrian distraction and pedestrian violations. Ultimately, several site- and pedestrian-specific variables were found to be significantly associated with pedestrian distraction, violation behavior, and walking speeds. The results provide important information for researchers, practitioners, and legislators, and may be useful in planning strategies to reduce or mitigate the impacts of pedestrian behavior that may be considered unsafe or potentially inefficient.
Automated traffic signal performance measures (ATSPMs) are an effort to equip traffic signal controllers with high-resolution data-logging capabilities and utilize this data to generate performance measures. These measures allow practitioners to improve operations as well as to maintain and operate their systems in a safe and efficient manner. Although these measures have changed the way that operators manage their systems, several shortcomings of the tool, identified by talking with signal operators, are a lack of data quality control and the extent of resources required to properly use the tool for system-wide management. To address these shortcomings, intelligent traffic signal performance measurements (ITSPMs) are presented in this paper, using the concepts of machine learning, traffic flow theory, and data visualization to reduce the operator resources needed for overseeing data-driven traffic signal management systems. In applying these concepts, ITSPMs provide graphical tools to identify and remove logging errors and data from bad sensors, intelligently determine trends in demand, and address the question of whether or not coordination may be needed at an intersection. The focus of ATSPMs and ITSPMs on performance measures for multimodal users is identified as a pressing need for future research.
Stem cells have emerged as a promising cell source to heal, replace or regenerate tissue and organs damaged by aging, injury or diseases. The intestinal epithelium is the most rapidly renewing tissue in our body, which is maintained by intestinal stem cells (ISCs), located at the bottom of the crypts. ISCs continuously replace lost or injured intestinal epithelial cells in organisms ranging from Drosophila to humans. The adult Drosophila midgut provides an excellent in vivo model system to study ISC behavior during stress, regeneration, aging and infection. There are several signaling pathways/genes have been identified to regulate ISCs self-renewal and differentiation during normal and pathological conditions. A significant number of genetic tools and markers have been developed in the last one decade to study Drosophila ISCs behavior. Here, we describe some of the markers and methods used to study ISCs behavior in adult midgut of Drosophila.
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