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Showing 1 to 13 of 13 for “"Traffic Forecasting"”.
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STREETS: a benchmark dataset for suburban traffic forecasting
… we introduce and benchmark STREETS, a novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. STREETS addresses multiple limitations of existing vehicular traffic datasets. Many current datasets lack a coherent traffic network graph to describe the …
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Implementing deep learning techniques for network-scale traffic forecasting
In the past few years, Deep learning has re-emerged as a powerful tool to solve complex problems and create prediction models that can outperform a lot of the existing state-of-the-art methods. This is primarily due to two main reasons; the rise of big data, where huge amounts of information has …
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Short-term traffic forecasting for a smart satellite communications system
… resources is the change in user terminal traffic during a complete cycle of the system: collecting data, generating a constellation setting solution, transmitting the new solution to each satellite and executing changes to the satellite's parameters. As the system's cycle time grows the …
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Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting
… surge in the development of spatio-temporal forecasting models in many applications, and traffic forecasting is one of the most important ones. Long-range traffic forecasting, however, remains a challenging task due to the intricate and extensive spatio-temporal correlations observed in …
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Admission Control in Sliced Networks, with Predictive Analytics
… function can be improved by incorporating traffic forecasting into the admission control process. In this work, we present the state-of-the-art in admission control in sliced networks and the state-of-the-art of the application of predictive analytics to admission control. We design and …
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Design and evaluation of a novel convolutional neural network for short-term vehicle multi-traffic prediction
Short-term vehicle traffic forecasting is about predicting how traffic indicators are going to be in the near future. The main traffic parameters are: traffic volume, traffic speed, and congestion state. In this thesis, we propose a convolutional neural net-work model that performs traffic …
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A framework for smart traffic management using heterogeneous data sources
Traffic congestion constitutes a social, economic and environmental issue to modern cities as it can negatively impact travel times, fuel consumption and carbon emissions. Traffic forecasting and incident detection systems are fundamental areas of Intelligent Transportation Systems (ITS) that have …
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Mining Massive Moving Object Datasets From RFID Flow Analysis to Traffic Mining
Mining traffic anomalies. Identification and characterization of traffic anomalies on massive road networks is a vital component of traffic monitoring [44]. Anomaly identification can be used to reduce congestion, increase safety, and provide transportation engineers with better information for …
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An analysis of residential trip generation in Cape Town
… generation sub-model within the conventional traffic forecasting model is highlighted. A review of past and current practice in the analysis of residential trip generation is presented. The least-squares and category analysis techniques are compared, and the dummy variable method is briefly …
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The role of private participation in enhancing the Indian transport sector
… defined for the toll road projects in India. Traffic forecasting techniques should be improved, and the government should promote public acceptance for private participation in transport sector.
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Auto-Calibration of WIM Using Traffic Stream Characteristics
… the WIM measured weights of vehicles from the traffic stream to reference values, using five-axle tractor-trailer configured trucks for comparisons, e.g. Federal Highway Administration (FHWA) Class 9. Parameters of the existing algorithms including the Front Axle Weight (FAW) reference value, …
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Graph-based Time-series Forecasting in Deep Learning
Time-series forecasting has long been studied and remains an important research task. In scenarios where multiple time series need to be forecast, approaches that exploit the mutual impact between time series results in more accurate forecasts. This has been demonstrated in various applications, …
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Toward Robust and Generalizable Spatiotemporal Modeling for Tasks beyond Forecasting and Classification
… In diverse operational domains such as traffic systems, neurophysiological monitoring, and drilling operations, spatiotemporal data is often noisy, irregular, and subject to distribution shifts — exposing the brittleness of conventional forecasting and classification pipelines. This …