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Leaning Robust Sequence Features via Dynamic Temporal Pattern Discovery
Title
Leaning Robust Sequence Features via Dynamic Temporal Pattern Discovery.
Creator
Hu, Hao, Wang, Liqiang, Zhang, Shaojie, Liu, Fei, Qi, GuoJun, Zhou, Qun, University of Central Florida
Abstract / Description
As a major type of data, time series possess invaluable latent knowledge for describing the real world and human society. In order to improve the ability of intelligent systems for understanding the world and people, it is critical to design sophisticated machine learning algorithms for extracting robust time series features from such latent knowledge. Motivated by the successful applications of deep learning in computer vision, more and more machine learning researchers put their attentions...
Show moreAs a major type of data, time series possess invaluable latent knowledge for describing the real world and human society. In order to improve the ability of intelligent systems for understanding the world and people, it is critical to design sophisticated machine learning algorithms for extracting robust time series features from such latent knowledge. Motivated by the successful applications of deep learning in computer vision, more and more machine learning researchers put their attentions on the topic of applying deep learning techniques to time series data. However, directly employing current deep models in most time series domains could be problematic. A major reason is that temporal pattern types that current deep models are aiming at are very limited, which cannot meet the requirement of modeling different underlying patterns of data coming from various sources. In this study we address this problem by designing different network structures explicitly based on specific domain knowledge such that we can extract features via most salient temporal patterns. More specifically, we mainly focus on two types of temporal patterns: order patterns and frequency patterns. For order patterns, which are usually related to brain and human activities, we design a hashing-based neural network layer to globally encode the ordinal pattern information into the resultant features. It is further generalized into a specially designed Recurrent Neural Networks (RNN) cell which can learn order patterns in an online fashion. On the other hand, we believe audio-related data such as music and speech can benefit from modeling frequency patterns. Thus, we do so by developing two types of RNN cells. The first type tries to directly learn the long-term dependencies on frequency domain rather than time domain. The second one aims to dynamically filter out the ``noise" frequencies based on temporal contexts. By proposing various deep models based on different domain knowledge and evaluating them on extensive time series tasks, we hope this work can provide inspirations for others and increase the community's interests on the problem of applying deep learning techniques to more time series tasks.
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Date Issued
2019
Identifier
CFE0007470, ucf:52679
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007470
Arterial-level real-time safety evaluation in the context of proactive traffic management
Title
Arterial-level real-time safety evaluation in the context of proactive traffic management.
Creator
Yuan, Jinghui, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Cai, Qing, Wang, Liqiang, University of Central Florida
Abstract / Description
In the context of pro-active traffic management, real-time safety evaluation is one of the most important components. Previous studies on real-time safety analysis mainly focused on freeways, seldom on arterials. With the advancement of sensing technologies and smart city initiative, more and more real-time traffic data sources are available on arterials, which enables us to evaluate the real-time crash risk on arterials. However, there exist substantial differences between arterials and...
Show moreIn the context of pro-active traffic management, real-time safety evaluation is one of the most important components. Previous studies on real-time safety analysis mainly focused on freeways, seldom on arterials. With the advancement of sensing technologies and smart city initiative, more and more real-time traffic data sources are available on arterials, which enables us to evaluate the real-time crash risk on arterials. However, there exist substantial differences between arterials and freeways in terms of traffic flow characteristics, data availability, and even crash mechanism. Therefore, this study aims to deeply evaluate the real-time crash risk on arterials from multiple aspects by integrating all kinds of available data sources. First, Bayesian conditional logistic models (BCL) were developed to examine the relationship between crash occurrence on arterial segments and real-time traffic and signal timing characteristics by incorporating the Bluetooth, adaptive signal control, and weather data, which were extracted from four urban arterials in Central Florida. Second, real-time intersection-approach-level crash risk was investigated by considering the effects of real-time traffic, signal timing, and weather characteristics based on 23 signalized intersections in Orange County. Third, a deep learning algorithm for real-time crash risk prediction at signalized intersections was proposed based on Long Short-Term Memory (LSTM) and Synthetic Minority Over-Sampling Technique (SMOTE). Moreover, in-depth cycle-level real-time crash risk at signalized intersections was explored based on high-resolution event-based data (i.e., Automated Traffic Signal Performance Measures (ATSPM)). All the possible real-time cycle-level factors were considered, including traffic volume, signal timing, headway and occupancy, traffic variation between upstream and downstream detectors, shockwave characteristics, and weather conditions. Above all, comprehensive real-time safety evaluation algorithms were developed for arterials, which would be key components for future real-time safety applications (e.g., real-time crash risk prediction and visualization system) in the context of pro-active traffic management.
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Date Issued
2019
Identifier
CFE0007743, ucf:52398
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007743
DESIGNING AN EXPERIENTIAL WEB-BASED LEARNING MODEL TO DELIVER THE ACQUISITION AND APPLICATION OF KNOWLEDGE TO HOSPITALITY EVENT MANAGEMENT STUDENTS USING ROLE-PLAY SIMULATIONS
Title
DESIGNING AN EXPERIENTIAL WEB-BASED LEARNING MODEL TO DELIVER THE ACQUISITION AND APPLICATION OF KNOWLEDGE TO HOSPITALITY EVENT MANAGEMENT STUDENTS USING ROLE-PLAY SIMULATIONS.
Creator
Hogg, James, Gunter, Glenda, University of Central Florida
Abstract / Description
ABSTRACT Most hospitality institutions have increasingly moved classes online but are concerned about migrating classes and instructional content online. The concern is most Web-based models are designed to deliver the acquisition of knowledge but lack the ability to transform that knowledge into applied career skills for practical use in the industry. The purpose of this study was to test a new Web-based instructional model. The model supported delivering both the acquisition and application...
Show moreABSTRACT Most hospitality institutions have increasingly moved classes online but are concerned about migrating classes and instructional content online. The concern is most Web-based models are designed to deliver the acquisition of knowledge but lack the ability to transform that knowledge into applied career skills for practical use in the industry. The purpose of this study was to test a new Web-based instructional model. The model supported delivering both the acquisition and application of knowledge. Educators, researchers, and practitioners can utilize the new model to enhance the application of career skills and enhance organizational objectives by providing just-in-time training. The new Web-based instructional model can be delivered through multiple platforms including computers, electronic devices, wireless devices and mobile devices. The application of knowledge was delivered through experiential role-play exercises delivered live to the comparison group and virtual, inside Second Life, to the treatment group. An Analysis of Co-Variance (ANCOVA) revealed a significant difference between groups with higher application scores for the students who received the role-play live compared to virtual. In addition, an analysis was conducted to explore factors to consider when examining the cost effectiveness of Web-based instructional content. Factors determined to be important were developmental costs, delivery costs, and reusability of the Web-based instruction.
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Date Issued
2010
Identifier
CFE0003044, ucf:48341
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003044
Applications of Deep Learning Models for Traffic Prediction Problems
Title
Applications of Deep Learning Models for Traffic Prediction Problems.
Creator
Rahman, Rezaur, Hasan, Samiul, Abdel-Aty, Mohamed, Zaki Hussein, Mohamed, University of Central Florida
Abstract / Description
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.
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Date Issued
2019
Identifier
CFE0007516, ucf:52654
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007516
OPTIMAL DETOUR PLANNING AROUND BLOCKED CONSTRUCTION ZONES
Title
OPTIMAL DETOUR PLANNING AROUND BLOCKED CONSTRUCTION ZONES.
Creator
Jardaneh , Mutasem, Khalafallah, Ahmed, University of Central Florida
Abstract / Description
Construction zones are traffic way areas where construction, maintenance or utility work is identified by warning signs, signals and indicators, including those on transport devices that mark the beginning and end of construction zones. Construction zones are among the most dangerous work areas, with workers facing workplace safety challenges that often lead to catastrophic injuries or fatalities. In addition, daily commuters are also impacted by construction zone detours that affect their...
Show moreConstruction zones are traffic way areas where construction, maintenance or utility work is identified by warning signs, signals and indicators, including those on transport devices that mark the beginning and end of construction zones. Construction zones are among the most dangerous work areas, with workers facing workplace safety challenges that often lead to catastrophic injuries or fatalities. In addition, daily commuters are also impacted by construction zone detours that affect their safety and daily commute time. These problems represent major challenges to construction planners as they are required to plan vehicle routes around construction zones in such a way that maximizes the safety of construction workers and reduces the impact on daily commuters. This research aims at developing a framework for optimizing the planning of construction detours. The main objectives of the research are to first identify all the decision variables that affect the planning of construction detours and secondly, implement a model based on shortest path formulation to identify the optimal alternatives for construction detours. The ultimate goal of this research is to offer construction planners with the essential guidelines to improve construction safety and reduce construction zone hazards as well as a robust tool for selecting and optimizing construction zone detours.
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Date Issued
2011
Identifier
CFE0003586, ucf:48900
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003586
EFFECTIVE SCHOOL CHARACTERISTICS AND STUDENT ACHIEVEMENT CORRELATES AS PERCEIVED BY TEACHERS IN AMERICAN STYLE INTERNATIONAL SCHOOLS
Title
EFFECTIVE SCHOOL CHARACTERISTICS AND STUDENT ACHIEVEMENT CORRELATES AS PERCEIVED BY TEACHERS IN AMERICAN STYLE INTERNATIONAL SCHOOLS.
Creator
Doran, James, Allen, Kay, University of Central Florida
Abstract / Description
The purpose of this study is to investigate the relationships between effective school characteristics and norm referenced standardized test scores in American-style international schools. In contrast to schools in traditional effective schools research, international schools typically have middle to high SES families, and display average to above average achievement. Eleven effective school characteristics were identified and correlated with standardized test scores for grades 4, 6, and 8...
Show moreThe purpose of this study is to investigate the relationships between effective school characteristics and norm referenced standardized test scores in American-style international schools. In contrast to schools in traditional effective schools research, international schools typically have middle to high SES families, and display average to above average achievement. Eleven effective school characteristics were identified and correlated with standardized test scores for grades 4, 6, and 8 and high school SAT scores. Data was gathered from an online teacher questionnaire designed for this study. All eleven characteristics were present in high performing international schools while frequent analysis of student progress, high academic expectations and positive school environment were more prominent. Positive school environment, high academic expectations, strong instructional leadership and cultural diversity were chosen as important characteristics of an effective international school. Learning time is maximized was the only characteristic that was significantly correlated with achievement and only in grades 4, 6 and 8. There was no statistically significant relationship found between norm referenced test scores and the aggregate effective school characteristics score.
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Date Issued
2004
Identifier
CFE0000245, ucf:46244
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000245