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Estimation of bicycling volumes is essential for the strategic implementation of infrastructure and related transport elements and policies. Link-level volume estimation models (models that estimate volumes on individual street segments) allow for understanding variation in bicycling volumes across an entire network at higher spatial resolution than area-level models. Such models assist transport planners to efficiently monitor network usage, to identify opportunities to enhance safety and to evaluate the impact of policy and infrastructure interventions. However, given the sparsity and scarcity of bicycling data as compared to its motorised counterparts, link-level bicycling volume estimation literature is relatively limited. This paper conducts a scoping review of link-level bicycling volume estimation methods by implementing systematic search strategies across relevant databases, thereby identifying appropriate studies for the review. The review resulted in some interesting findings. Among all the methods implemented, direct demand modelling was the predominant one. Not a single study implemented multiple modelling approaches in the same study area, thereby not allowing for comparison of these approaches. Most studies were conducted in the United States. It was also observed that there exists a lot of heterogeneity in the reporting of basic study characteristics and validation results, sometimes to the extent of not reporting these at all. The study presents the different types of data used in modelling (count, travel survey, GPS data) along with an array of popular explanatory variables that can inform future studies about data collection and variable selection for modelling. The study discusses the strengths and limitations of different methods and finally presents recommendations for future research. [ABSTRACT FROM AUTHOR]

Over 70 million individuals are infected with hepatitis C virus (HCV) worldwide. Yet most prevalence data are in the adult population, with little focus on paediatrics, partially due to the scarcity of public data. The objective of this paper is to examine HCV prevalence in children by estimating prevalence rates among women, given the assumption that most cases are vertically transmitted. Between 2001 and 2017, maternal HCV infection affected ~ 0.24% of all births, with prevalence increasing by at least 261%. On average, approximately 0.01% of the total number of live births were infected with HCV, with a 245% increase in the number of children born with the infection. HCV epidemiology has evolved, with women of childbearing age representing a greater proportion of infected individuals in the United States, and infants born to infected mothers being at risk. We therefore recommend a greater public health focus of HCV on the paediatric population. [ABSTRACT FROM AUTHOR]

Natural disasters affect structural health of buildings, thus directly impacting public safety. Continuous structural monitoring can be achieved by deploying an Internet of things network of distributed sensors in buildings to capture floor movement. These sensors can be used to compute the displacements of each floor, which can then be employed to assess building damage after a seismic event. The peak relative floor displacement is computed, which is directly related to damage level according to the United States federal agencies standards. With this information, the building inventory can be classified into immediate occupancy, life safety, or collapse prevention categories. In this paper, we propose a zero velocity update technique to minimize displacement estimation error. Theoretical derivation and experimental validation are presented. In addition, we investigate modeling sensor error and interstory drift ratio distribution. Moreover, we discuss the impact of sensor error on the achieved building classification accuracy. [ABSTRACT FROM AUTHOR]

Right-of-way (R/W) acquisition, one of the most basic of preconstruction activities on a highway project, is an important subject by many state DOTs because the failure to make timely acquisitions of R/W causes significant delays and conflicts. Various management practices and strategies have been designed and implemented to deliver R/W acquisition on schedule but completing this preconstruction process is still not an easy activity for many state DOTs. The Texas DOT (TXDOT) is no exception. The research reported in this paper has identified all drivers that can be considered influential in the determination of durations for R/W acquisition; it was undertaken to improve the R/W duration-prediction capabilities of practitioners involved in regional districts of TXDOT. In the research process, each driver's importance level was evaluated by such practitioners. The practitioners, all of whom had different levels of accuracy at estimating durations, showed significantly different perceptions of the identified duration-drivers. Based on this observation, the research reported in this paper suggests that all drivers which show such significantly different levels of importance should be determined when predicting durations for R/W acquisition in a project's early-planning phase. The research reported in this paper demonstrates that determining the influential drivers is critical to accurately estimate durations for R/W acquisition on highway projects. [ABSTRACT FROM AUTHOR]

While digital map has been applied to many advanced driver assistance system applications, one critical attribute - road/lane curvature—is not available in the existing digital maps. Curvature derivation using splines is a well-known method in the computer graphics modelling community; however, it was also found that the direct application of the spline method is often not appropriate for real-time safety applications due to the resultant discontinuities in the curvature estimates and the lack of robustness against map data errors [CAMP, Enhanced digital mapping project final report, United States Department of Transportation, Washington, DC, 2004]. This paper presents a method to reconstruct road curvature attributes in real time using digital map data based on the proposed circle centre search and circle selection algorithms. Simulation and experimental results demonstrated that the proposed method can deliver curvature estimates that meet the desired accuracy. The method was also applied to the curve over-speed warning system in an on-going research project: Vehicle Infrastructure Integration—California. Some preliminary experimental results are also presented in this paper. [ABSTRACT FROM AUTHOR]

The prevention of infectious diseases is a global health priority area. The early detection of possible epidemics is the first and important defense line against infectious diseases. However, conventional surveillance systems, e.g., the Centers for Disease Control and Prevention (CDC), rely on clinical data. The CDC publishes the surveillance results weeks after epidemic outbreaks. To improve the early detection of epidemic outbreaks, we designed a syndromic surveillance system to predict the epidemic trends based on disease-related Google search volume. Specifically, we first represented the epidemic trend with multiple alert levels to reduce the noise level. Then, we predicted the epidemic alert levels using a continuous density HMM, which incorporated the intrinsic characteristic of the disease transmission for alert level estimation. Respective models are built to monitor both national and regional epidemic alert levels of the U.S. The proposed system can provide real-time surveillance results, which are weeks before the CDC’s reports. This paper focusses on monitoring the infectious disease in the U.S., however, we believe similar approach may be used to monitor epidemics for the developing countries as well. [ABSTRACT FROM PUBLISHER]