Georgia Department of Transportation

More than 25% of all roadway fatalities in the U.S. are associated with a horizontal curve, and the average crash rate for horizontal curves is about three times that of other types of highway segments. A focus on horizontal curves can prove to be a cost-effective approach to reducing safety issues. Accurate crash prediction models (CPMs) on horizontal curves can help roadway safety practitioners assess and prioritize safety improvements. Although many CPMs have been developed, there are no extant studies that compare different CPMs on a singular, real-world, large-scale, and comprehensive dataset to evaluate their capability for horizontal curve crash prediction. This study critically evaluated commonly used CPMs, including multiple linear regression (MLR), Poisson regression (PR), negative binomial regression (NBR), support vector machine, random forest (RF), and fully connected neural network (FCNN) models, on rural curves extracted from 18,000 centerline miles of Georgia, U.S.’s state-maintained routes and statewide historical crash data set from 2013 to 2021. Results show PR and NBR models outperform MLR by around 6%. Moreover, the FCNN and RF models further improved this performance by around an additional 6% over the PR and NBR models. Overall, machine learning (ML)-based models outperform generalized linear regression models. The results prove ML-based models can be recommended to transportation agencies to forecast horizontal curve crashes more accurately.

SummarySARS-CoV-2 variants of concern (VOCs) arise against the backdrop of increasingly heterogeneous human connectivity and population immunity. Through a large-scale phylodynamic analysis of 115,622 Omicron genomes, we identified >6,000 independent introductions of the antigenically distinct virus into England and reconstructed the dispersal history of resulting local transmission. Travel restrictions on southern Africa did not reduce BA.1 importation intensity as secondary hubs became major exporters. We explored potential drivers of BA.1 spread across England and discovered an early period during which viral lineage movements mainly occurred between larger cities, followed by a multi-focal spatial expansion shaped by shorter distance mobility patterns. We also found evidence that disease incidence impacted human commuting behaviours around major travel hubs. Our results offer a detailed characterisation of processes that drive the invasion of an emerging VOC across multiple spatial scales and provide unique insights on the interplay between disease spread and human mobility.HighlightsOver 6,000 introductions ignited the epidemic wave of Omicron BA.1 in EnglandImportations prior to international travel restrictions were responsible for majority of local BA.1 infections but importations continued from sources other than southern AfricaHuman mobility at regional and local spatial scales shaped dissemination and growth of BA.1Changes in human commuting patterns are associated with higher case incidence in travel hubs across England

Roundabouts are classified as alternative intersection forms because they provide operational conditions not found at conventional intersections (e.g., a low-speed environment and fewer possible vehicle conflicts). Such conditions provide a combination of safety and operational benefit that normally is not achievable at a conventional intersection. The ability to predict capacity accurately is important when alternative intersection designs are examined. Highway Capacity Manual 2010 provides a current method for determining the capacity of a roundabout approach in which vehicles that exit on the same approach are considered not to influence capacity. However, the inclusion of exiting vehicles in the model creates a different circulating vehicle gap distribution (dividing larger gaps into smaller gaps) that reduces the number of opportunities to measure follow-up headway. Also, most critical headway values for the analysis are smaller than the critical headway values found when exiting vehicles are excluded. Capacity equations were developed from video imagery recorded for 28 approaches at 13 roundabouts, and the impact of including exiting vehicles in the analysis of single-lane roundabout capacity was evaluated. Vehicles exiting the roundabout were found to have a measurable impact on the estimated capacity of the roundabout approach. Both follow-up and critical headway values decreased when exiting vehicles were included. Also, the estimated capacity including exiting vehicles was found to increase or decrease (relative to capacity estimates that excluded exiting vehicles) depending on the percentage of conflicting vehicles that were exiting vehicles.

This paper demonstrates the use of large streams of truck GPS data from the American Transportation Research Institute for the estimation of statewide freight truck flows in Florida. Raw GPS data streams, which comprised more than 145 million GPS records, were used to derive a database of more than 1.2 million truck trips that started or ended in Florida. The paper sheds light on the extent to which the trips derived from the GPS data captured the observed truck traffic flows in Florida. The paper includes insights into ( a) the truck type composition, ( b) the proportion of the observed truck traffic flows covered by the data, and ( c) the geographical differences in the coverage. The paper applies origin–destination (O-D) matrix estimation to combine the GPS data with the observed truck traffic volumes at different locations within and outside Florida to derive an O-D table of truck flows within, into, and out of the state. The procedures, implementation details, and findings discussed in the paper are expected to be useful to agencies that are considering the use of GPS data in freight travel demand modeling.

More than 20% of the bridges in the United States were built more than 50 years ago, at a time in which intense precipitation events were much less common. However, very little work has been done on the use of scour risk-assessment models to assess how climate change increases bridge failure probabilities. This paper develops a risk-assessment framework based on HYRISK, a model developed to assess the probability of a bridge failure due to scour, and illustrates oneway in which current engineering risk-assessment models can be used to quantify the additional risks and expected economic losses associated with a changing climate. Application of this framework to all bridges in the United States that carry vehicular traffic over water finds that economic losses due to climate change factors will increase by at least 15% over current losses and that the expected number of annual bridge failures in the United States will increase by at least 10% over current failures. Climate-based risk measures, such as those developed as part of this study, could be included in asset management systems to help state DOTs prioritize maintenance, operation, and replacement schedules. DOI: 10.1061/(ASCE)IS.1943-555X.0000109. © 2013 American Society of Civil Engineers. CE Database subject headings: Bridges; Scour; Risk management; Climate change. Author keywords: Bridge scour; Risk assessment; Climate change.

The thesis represents the forecasting system as an organic and important part of the management for the various types of organizationsand enterprises. Forecasting, with the strategic planning, is complexly discussed and considered necessary and obligatory backgroundfor the development of any company. The publication describes the forecasting system as combined method of forecasting and unifiesquantitative and expert (judgmental) forecasting methods.The methodology of the expert’s competence’s determination considers the expert’s skill in specific field by the following assessmentcriteria: education quality, general work experience, work experience in specific field, expert’s experience, awareness in the field andexpert effectiveness.

Faulting has traditionally been collected by using manual methods, which are labor intensive, time-consuming, and hazardous to workers and drivers. Therefore, alternative methods for effectively and safely collecting faulting data are needed. With emerging laser technology originally designed for crack detection, high-resolution, full lane-width coverage, three-dimensional (3D) continuous pavement profile data can now be acquired. This paper critically assesses the feasibility of using this 3D continuous pavement profile data for measuring faulting with a special focus on accuracy and repeatability. Controlled field tests were conducted to evaluate the accuracy for faulting in different ranges. Field tests were conducted at highway speeds on I-16 in Georgia to evaluate the repeatability and feasibility of the proposed method. Results show the proposed method can estimate faulting with an average error of less than 1 mm compared with those measured using the Georgia fault meter, and it can achieve reasonable repeatability with a standard deviation less than 1 mm in repeated runs at different highway speeds. Two tests have demonstrated that it is feasible to collect faulting data using 3D continuous pavement profile data. Recommendations for future research are also discussed.

Abstract – The spotfin chub Erimonax monachus is a rare cyprinid fish endemic to the Tennessee River drainage, and it is federally listed as threatened in the USA. Microhabitat use of spotfin chubs was studied via stream snorkelling in the Emory River watershed, Tennessee, one of the last remaining populations of this species. We used a Bayesian generalised linear mixed model to evaluate microhabitat covariates related to the presence/absence of spotfin chubs among eight stream sites across three seasons (early summer, late summer and fall). In general, spotfin chubs were more likely to be present in microhabitats characterised by boulder/bedrock substrates, medium to high velocity, and medium depth (typical of the run habitat). However, the patterns were not necessarily consistent among seasons or stream sites, due partly to interactions between microhabitat and macrohabitat covariates. Specifically, spotfin chubs were more selective of bedrock and boulder substrate at smaller stream sites where they were less abundant, and they were more selective of higher velocity at warmer stream temperatures (early and late summer). Our data indicate that spotfin chubs may exhibit flexible microhabitat use to some extent, and their microhabitat use may differ by macrohabitat characteristics such as stream size and water temperature. This study provided a refined understanding of microhabitat use of spotfin chubs and suggests that effective conservation of this declining species should identify and protect available suitable habitat across space and time.