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- Title
- Applications of Deep Learning Models for Traffic Prediction Problems.
- Creator
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Rahman, Rezaur, Hasan, Samiul, Abdel-Aty, Mohamed, Zaki Hussein, Mohamed, University of Central Florida
- Abstract / Description
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Deep learning coupled with existing sensors based multiresolution traffic data and future connected technologies has immense potential to improve traffic operation and management. But to deal with complex transportation problems, we need efficient modeling frameworks for deep learning models. In this study, we propose two different modeling frameworks using Deep Long Short-Term Memory Neural Network (LSTM NN) model to predict future traffic state (speed and signal queue length). In our first...
Show moreDeep learning coupled with existing sensors based multiresolution traffic data and future connected technologies has immense potential to improve traffic operation and management. But to deal with complex transportation problems, we need efficient modeling frameworks for deep learning models. In this study, we propose two different modeling frameworks using Deep Long Short-Term Memory Neural Network (LSTM NN) model to predict future traffic state (speed and signal queue length). In our first problem, we present a modeling framework using deep LSTM NN model to predict traffic speeds in freeways during regular traffic condition as well as under extreme traffic demand, such as a hurricane evacuation. The approach is tested using real-world traffic data collected during hurricane Irma's evacuation for the interstate 75 (I-75), a major evacuation route in Florida. We perform several experiments for predicting speeds for 5 min, 10 min, and 15 min ahead of current time. The results are compared against other traditional prediction models such as K-Nearest Neighbor, Analytic Neural Network (ANN), Auto-Regressive Integrated Moving Average (ARIMA). We find that LSTM-NN performs better than these parametric and non-parametric models. Apart from the improvement in traffic operation, the proposed method can be integrated with evacuation traffic management systems for a better evacuation operation. In our second problem, we develop a data-driven real-time queue length prediction technique using deep LSTM NN model. We consider a connected corridor where information from vehicle detectors (located at the intersection) will be shared to consecutive intersections. We assume that the queue length of an intersection in the next cycle will depend on the queue length of the target and two upstream intersections in the current cycle. We use InSync Adaptive Traffic Control System (ATCS) data to train a Long Short-Term Memory Neural Network model capturing time-dependent patterns of a queue of a signal. To select the best combination of hyperparameters, we use sequential model-based optimization (SMBO) technique. Our experiment results show that the proposed modeling framework performs very well to predict the queue length. Although we run our experiments predicting the queue length for a single movement, the proposed method can be applied for other movements as well. Queue length prediction is a crucial part of an ATCS to optimize control parameters and this method can improve the existing signal optimization technique for ATCS.
Show less - Date Issued
- 2019
- Identifier
- CFE0007516, ucf:52654
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007516
- Title
- THE IMPACT OF MEDITATION AND MINDFULNESS IN THE ELEMENTARY CLASSROOM: A REVIEW OF RESEARCH LITERATURE ACROSS FIVE DISCIPLINES.
- Creator
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Routhier-Martin, Kayli, Killingsworth Roberts, Sherron, University of Central Florida
- Abstract / Description
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Mindfulness meditation programs, benefits, and outcomes were examined through research previously conducted and published by professionals within five differing disciplines: health and wellness, psychology, elementary education, exceptional education, and medicine. The goal was to find common themes within the differing disciplines in order to gather information about the effectiveness of a mindfulness meditation program to be used in an elementary classroom. In this thesis, the chapter of...
Show moreMindfulness meditation programs, benefits, and outcomes were examined through research previously conducted and published by professionals within five differing disciplines: health and wellness, psychology, elementary education, exceptional education, and medicine. The goal was to find common themes within the differing disciplines in order to gather information about the effectiveness of a mindfulness meditation program to be used in an elementary classroom. In this thesis, the chapter of Health and Wellness is a review of literature that tells the benefits found within meditators, which are not found within non-meditators. The chapter of Psychology explains the social-emotional needs of students, the causes of stress and anxieties amongst students, and the benefits that meditation provides in order to counter the negative effects of stress, anxiety, poverty, etc. The chapter of Elementary Education reviews research literature on the existing mindfulness meditation programs within the United States. This chapter also describes the implementation of such a program in an elementary school, as well as the documented data of the outcomes of the programs. The chapter of Exceptional Education is a review of the research literature on the benefits mindfulness meditation has on students with exceptionalities, such as specific learning disabilities, attention deficit hyperactivity disorder, and autism spectrum disorder. The chapter of Medicine is a retelling of previously published scholarly articles that list the neurological benefits of meditation, and also references the negative side effects to the currently prescribed medications that are being used in the treatment of ADHD.
Show less - Date Issued
- 2015
- Identifier
- CFH0004877, ucf:45424
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH0004877
