67,876 research outputs found
Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics
The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns.
The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%
DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion
Non-recurring traffic congestion is caused by temporary disruptions, such as
accidents, sports games, adverse weather, etc. We use data related to real-time
traffic speed, jam factors (a traffic congestion indicator), and events
collected over a year from Nashville, TN to train a multi-layered deep neural
network. The traffic dataset contains over 900 million data records. The
network is thereafter used to classify the real-time data and identify
anomalous operations. Compared with traditional approaches of using statistical
or machine learning techniques, our model reaches an accuracy of 98.73 percent
when identifying traffic congestion caused by football games. Our approach
first encodes the traffic across a region as a scaled image. After that the
image data from different timestamps is fused with event- and time-related
data. Then a crossover operator is used as a data augmentation method to
generate training datasets with more balanced classes. Finally, we use the
receiver operating characteristic (ROC) analysis to tune the sensitivity of the
classifier. We present the analysis of the training time and the inference time
separately
Thematic Issue on the Hydrological Effects of the Vegetation-Soil Complex
Peer reviewedPublisher PD
PICES Press, Vol. 20, No. 2, Summer 2012
•The 2012 Inter-sessional Science Board Meeting: A Note from Science Board Chairman (pp. 1-4)
â—ľPICES Interns (p. 4)
â—ľ2012 Inter-sessional Workshop on a Roadmap for FUTURE (pp. 5-8)
◾Second Symposium on “Effects of Climate Change on the World’s Oceans” (pp. 9-13)
◾2012 Yeosu Workshop on “Framework for Ocean Observing” (pp. 14-15)
◾2012 Yeosu Workshop on “Climate Change Projections” (pp. 16-17)
◾2012 Yeosu Workshop on “Coastal Blue Carbon” (pp. 18-20)
â—ľPolar Comparisons: Summary of 2012 Yeosu Workshop (pp. 21-23)
◾2012 Yeosu Workshop on “Climate Change and Range Shifts in the Oceans" (pp. 24-27)
◾2012 Yeosu Workshop on “Beyond Dispersion” (pp. 28-30)
◾2012 Yeosu Workshop on “Public Perception of Climate Change” (pp. 31, 50)
â—ľPICES Working Group 20: Accomplishments and Legacy (pp. 32-33)
â—ľThe State of the Western North Pacific in the Second Half of 2011 (pp. 34-35)
â—ľAnother Cold Winter in the Gulf of Alaska (pp. 36-37)
â—ľThe Bering Sea: Current Status and Recent Events (pp. 38-40)
â—ľPICES/ICES 2012 Conference for Early Career Marine Scientists (pp. 41-43)
◾Completion of the PICES Seafood Safety Project – Indonesia (pp. 44-46)
â—ľOceanography Improves Salmon Forecasts (p. 47)
â—ľ2012 GEOHAB Open Science Meeting (p. 48-50)
â—ľShin-ichi Ito awarded 2011 Uda Prize (p. 50
Marine baseline and monitoring strategies for Carbon Dioxide Capture and Storage (CCS)
The QICS controlled release experiment demonstrates that leaks of carbon dioxide (CO2) gas can be detected by monitoring acoustic, geochemical and biological parameters within a given marine system. However the natural complexity and variability of marine system responses to (artificial) leakage strongly suggests that there are no absolute indicators of leakage or impact that can unequivocally and universally be used for all potential future storage sites. We suggest a multivariate, hierarchical approach to monitoring, escalating from anomaly detection to attribution, quantification and then impact assessment, as required. Given the spatial heterogeneity of many marine ecosystems it is essential that environmental monitoring programmes are supported by a temporally (tidal, seasonal and annual) and spatially resolved baseline of data from which changes can be accurately identified. In this paper we outline and discuss the options for monitoring methodologies and identify the components of an appropriate baseline survey
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