Learning a Precipitation Indicator from Traffic Speed Variation Patterns

It is common sense that traffic participants tend to drive slower under rain or snow conditions, which has been confirmed by many studies in the field of transportation research. When analyzing the relation between precipitation events and traffic speed observations, it was shown that by using extra weather information, road speed prediction models can be improved. Conversely, traffic speed variation patterns of multiple roads may also provide an indirect indication of weather conditions. In this paper, we attempt to learn such a model, which can detect the appearance of precipitation events, using only road speed observations, for the case of New York City. With a seasonal trend decomposition model Prophet, residuals between the observations and the model were used as features to represent the level of anomaly as compared to the normal traffic situation. Based on the timestamps of weather records on sunny days versus rainy or snowy days, features were extracted from traffic data and assigned to the corresponding labels. A binary classifier was then trained on six-month training data and achieved an accuracy of 91.74% when tested on the remaining two-month test data. We show that there is a significant correlation between the precipitation events and speed variation patterns of multiple roads, which can be used to train a binary indicator. This indicator can detect those precipitation events, which have a significant influence on the city traffic. The method has also a great potential to improve the emergency response of cities where massive real-time traffic speed observations are available. © 2020 The Authors. Published by Elsevier B.V

Web Weather 2.0: Improving Weather Information with User-Generated Observations

Introducing web weather 2.0, this paper suggests that active participation by civil society may arise through sharing of environmental data through observations of weather and other measurable variables in the environment performed by individuals. Collecting data from individuals is here suggested for improving weather data currently used by weather research centers and practitioners. Extending these current sets of weather data by using web 2.0 may address some issues stated by the World Meteorological Organization (WMO) regarding spatial and temporal resolutions of meteorological data including knowledge on different processes between the air and other environmental systems. To test the concept of web weather 2.0, the usability of weather data collected from individuals and the expected quantities of such data need to be determined. In addition, collection methods should be developed. Aiming at the design of an artifact that can meet these needs, this paper presents some important steps of the design process of a “share weather” system, including several demonstrations and experiments performed on different user groups, i.e. school children performing weather observations as a part of their daily tasks and education, and adults interested in weather due to their daily dependence on traffic conditions. This paper provides new knowledge about user-generated observations of weather, including quality and motivation to contribute, and guidance on how future systems for collection of environmental data from individuals may be created. After testing the feasibility of the designed “share weather” artifact, we conclude that the potential role of individuals in producing valuable information beneficial to society should be considered within several branches of environmental sciences as well as policy-making

Transportation asset management and climate change: an adaptive risk-oriented approach

Transportation Asset Management (TAM) systems are in use at many transportation agencies both in the United States and around the world. These asset management systems serve as strategic resource allocation frameworks and their degree of implementation and maturity varies. Climatic change, with its potentially adverse impacts on both the built and natural environments, has become of increasing concern around the globe. Given the uncertainties associated with changing climatic conditions, transportation agency stakeholders utilize risk-based decision-making approaches to identify climate change impacts that pose the greatest risk to transportation infrastructure assets. In conjunction with criticality assessments, emerging conceptual frameworks seek to identify higher-risk infrastructure assets, which are both critical to system operations and vulnerable to potential climate change impacts, through standalone study efforts. This research develops a risk-oriented decision-making framework to identify vulnerable, higher-risk transportation infrastructure assets within the context of existing transportation asset management systems. The framework assesses the relative maturity of an agency’s transportation asset management system and provides guidance as to how an agency’s existing tools and processes can be used to incorporate climate change considerations. This risk-based decision-making framework is applied to three case studies: one at the Metropolitan Atlanta Rapid Transit Authority, another at the Metropolitan Planning Commission in Savannah – Chatham County, and a statewide case study at the Georgia Department of Transportation. The results of this research demonstrate that readily-available climate projection data can be analyzed and displayed geospatially so that the potential impacts of climatic change on transportation infrastructure can be determined for specific geographic regions. In addition, existing roadway and bridge infrastructure datasets can also be displayed geospatially. The framework uses geospatially-referenced roadway and bridge asset data and multi-criteria decision analysis procedures to develop and visually display criticality scores. Overlaying climate projection data and criticality data helps identify higher-risk transportation infrastructure assets. This research demonstrates that climate change considerations can be effectively incorporated in existing decision-making processes at various levels of maturity of formal TAM systems, making this more broadly accessible to agencies and communities with potential climate hazards.Ph.D

Aeronautical Engineering: A special bibliography with indexes, supplement 54

This bibliography lists 316 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1975

U.S. Population, Energy & Climate Change

Explains how U.S. population trends tend to exacerbate both the causes and effects of climate change. Outlines how population density and composition affect energy and land use, the role each U.S. region plays in climate change, and the risks they face

Modeling hourly weather-related road traffic variations for different vehicle types in Germany

Weather has a substantial influence on people’s travel behavior. In this study we analyze if meteorological variables can improve predictions of hourly traffic counts at 1400 stations on federal roads and highways in Germany. Motorbikes, cars, vans and trucks are distinguished. It is evaluated in how far the mean squared error of Poisson regression models for hourly traffic counts is reduced by using precipitation, temperature, cloud cover and wind speed data. It is shown that in particular motorbike counts are strongly weather-dependent. On federal roads the mean squared error is reduced by up to 60% in models with meteorological predictor variables, when compared to models without meteorological variables. A detailed analysis of the models for motorbike counts reveals non-linear relationships between the meteorological variables and motorbike counts. Car counts are shown to be specifically sensitive to weather in touristic regions like seaside resorts and nature parks. The findings allow for several potential applications like improvements of route planning in navigation systems, implementations in traffic management systems, day-ahead planning of visitor numbers in touristic areas or the usage in road crash modelling

Assessment of Urban Flood Vulnerability Using theSocial-Ecological-Technological Systems Framework in Six US cities

As urban populations continue to grow through the 21 st century, more people are projected to be at risk of exposure to climate change-induced extreme events. To investigate the complexity of urban floods, this study applied an interlinked social-ecological-technological systems (SETS) vulnerability framework by developing an urban flood vulnerability index for six US cities. Indicators were selected to reflect and illustrate exposure, sensitivity, and adaptive capacity to flooding for each of the three domains of SETS. We quantified 18 indicators and normalized them by the cities’ 500-yr floodplain area at the census block group level. Clusters of flood vulnerable areas were identified differently by each SETS domain, and some areas were vulnerable to floods in more than one domain. Results are provided to support decision-making for reducing risks to flooding, by considering social, ecological, and technological vulnerability as well as hotspots where multiple sources of vulnerability coexist. The spatially explicit urban SETS flood vulnerability framework can be transferred to other regions facing challenging urban floods and other types of environmental hazards. Mapping SETS flood vulnerability helps to reveal intersections of complex SETS interactions and inform policy-making for building more resilient cities in the face of extreme events and climate change impacts

Environmental Technology Applications in the Retrofitting of Residential Buildings

The impact of buildings on the environment is nothing short of devastating. In recent years, much attention has been given to creating an environmentally friendly built environment. Nonetheless, it has been levied on new buildings. Residential buildings make up at least 80% of the built environment, most of which were built before any energy efficiency guidelines or regulations were introduced. Retrofitting existing residential buildings is a key yet neglected priority in effecting the transition to an environmentally friendly, sustainable built environment. It is pivotal to reducing a building’s energy consumption while simultaneously improving indoor environmental quality and minimizing harmful emissions. This Special Issue showcases studies investigating applications of environmental technology that is tailored to enhance the sustainable performance of existing residential buildings. It helps to better understand the innovations that have been taking place in retrofitting residential buildings, as well as highlighting many opportunities for future research in this field

Assuring safe and efficient operation of UAV using explainable machine learning

The accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution to the needs of demand and capacity management (DCM) services. This solution, by deploying a synthesized fuzzy rule-based model and deep learning will address the trade-off between explicability and performance. In doing so, it will generate an intelligent system that will be explicable and reasonably comprehensible. The results show that this advisory system will be able to indicate the most appropriate regions for unmanned aerial vehicle (UAVs) operation, and it will also increase UTM airspace availability by more than 23%. Moreover, the proposed system demonstrates a maximum capacity gain of 65% and a minimum safety gain of 35%, while possessing an explainability attribute of 70%. This will assist UTM authorities through more effective airspace capacity estimation and the formulation of new operational regulations and performance requirements