A static traffic assignment model combined with an artificial neural network delay model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

How people resolve dilemmas: An elicitation method for subjective decision factors

In human society, people encounter various deontic conflicts every day. Deontic decisions are those that include moral, ethical, and normative aspects. Here, the concern is with deontic conflicts: decisions where all the alternatives lead to the violation of some norms. People think critically about these kinds of decisions. But, just ‘what’ they think about is not always clear. ^ People use certain estimating factors/criteria to balance the tradeoffs when they encounter deontic conflicts. It is unclear what subjective factors people use to make a deontic decision. An elicitation approach called the Open Factor Conjoint System is proposed, which applies an online elicitation methodology which is a combination of two well-know research methodologies: repertory grid and conjoint analysis. This new methodology is extended to be a web based application. It seeks to elicit additional relevant (subjective) factors from people, which affect deontic decisions. The relative importance and utility values are used for the development of a decision model to predict people’s decisions. ^ Fundamentally, this methodology was developed and intended to be applicable for a wide range of elicitation applications with minimal experimenter bias. Comparing with the traditional method, this online survey method reduces the limitation of time and space in data collection and this methodology can be applied in many fields. Two possible applications were addressed: robotic vehicles and the choice of medical treatment. In addition, this method can be applied to many research related disciplines in cross-cultural research due to its online ability with global capacity. ^

Motion correction algorithm of lung tumors for respiratory gated PET images

Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.

The possible selves method in evaluating youth development interventions: The use of Integrated Qualitative /Quantitative Data Analytic Strategies (IQ -DAS)

This study investigated the feasibility of using qualitative methods to provide empirical documentation of the long-term qualitative change in the life course trajectories of “at risk” youth in a school based positive youth development program (the Changing Lives Program—CLP). This work draws from life course theory for a developmental framework and from recent advances in the use of qualitative methods in general and a grounded theory approach in particular. Grounded theory provided a methodological framework for conceptualizing the use of qualitative methods for assessing qualitative life change. The study investigated the feasibility of using the Possible Selves Questionnaire-Qualitative Extension (PSQ-QE) for evaluating the impact of the program on qualitative change in participants' life trajectory relative to a non-intervention control group. Integrated Qualitative/Quantitative Data Analytic Strategies (IQ-DAS) that we have been developing a part of our program of research provided the data analytic framework for the study. ^ Change was evaluated in 85 at risk high school students in CLP high school counseling groups over three assessment periods (pre, post, and follow-up), and a non-intervention control group of 23 students over two assessment periods (pre and post). Intervention gains and maintenance and the extent to which these patterns of change were moderated by gender and ethnicity were evaluated using a mixed design Repeated Measures Multivariate Analysis of Variance (RMANOVA) in which Time (pre, post) was the within (repeated) factor and Condition, Gender, and Ethnicity the between group factors. The trends for the direction of qualitative change were positive from pre to post and maintained at the year-end follow-up. More important, the 3-way interaction for Time x Gender x Ethnicity was significant, Roy's Θ =. 205, F(2, 37) = 3.80, p <.032, indicating that the overall pattern of positive change was significantly moderated by gender and ethnicity. Thus, the findings also provided preliminary evidence for a positive impact of the youth development program on long-term change in life course trajectory, and were suggestive with respect to the issue of amenability to treatment, i.e., the identification of subgroups of individuals in a target population who are likely to be the most amenable or responsive to a treatment. ^

Novel online data cleaning protocols for data streams in trajectory, Wireless Sensor Networks

The promise of Wireless Sensor Networks (WSNs) is the autonomous collaboration of a collection of sensors to accomplish some specific goals which a single sensor cannot offer. Basically, sensor networking serves a range of applications by providing the raw data as fundamentals for further analyses and actions. The imprecision of the collected data could tremendously mislead the decision-making process of sensor-based applications, resulting in an ineffectiveness or failure of the application objectives. Due to inherent WSN characteristics normally spoiling the raw sensor readings, many research efforts attempt to improve the accuracy of the corrupted or "dirty" sensor data. The dirty data need to be cleaned or corrected. However, the developed data cleaning solutions restrict themselves to the scope of static WSNs where deployed sensors would rarely move during the operation. Nowadays, many emerging applications relying on WSNs need the sensor mobility to enhance the application efficiency and usage flexibility. The location of deployed sensors needs to be dynamic. Also, each sensor would independently function and contribute its resources. Sensors equipped with vehicles for monitoring the traffic condition could be depicted as one of the prospective examples. The sensor mobility causes a transient in network topology and correlation among sensor streams. Based on static relationships among sensors, the existing methods for cleaning sensor data in static WSNs are invalid in such mobile scenarios. Therefore, a solution of data cleaning that considers the sensor movements is actively needed. This dissertation aims to improve the quality of sensor data by considering the consequences of various trajectory relationships of autonomous mobile sensors in the system. First of all, we address the dynamic network topology due to sensor mobility. The concept of virtual sensor is presented and used for spatio-temporal selection of neighboring sensors to help in cleaning sensor data streams. This method is one of the first methods to clean data in mobile sensor environments. We also study the mobility pattern of moving sensors relative to boundaries of sub-areas of interest. We developed a belief-based analysis to determine the reliable sets of neighboring sensors to improve the cleaning performance, especially when node density is relatively low. Finally, we design a novel sketch-based technique to clean data from internal sensors where spatio-temporal relationships among sensors cannot lead to the data correlations among sensor streams.

A Static Traffic Assignment Model Combined with an Artificial Neural Network Delay Model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

Motion Correction Algorithm of Lung Tumors for Respiratory Gated PET Images

Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.

How People Resolve Dilemmas: An Elicitation Method for Subjective Decision Factors

In human society, people encounter various deontic conflicts every day. Deontic decisions are those that include moral, ethical, and normative aspects. Here, the concern is with deontic conflicts: decisions where all the alternatives lead to the violation of some norms. People think critically about these kinds of decisions. But, just ‘what’ they think about is not always clear. People use certain estimating factors/criteria to balance the tradeoffs when they encounter deontic conflicts. It is unclear what subjective factors people use to make a deontic decision. An elicitation approach called the Open Factor Conjoint System is proposed, which applies an online elicitation methodology which is a combination of two well-know research methodologies: repertory grid and conjoint analysis. This new methodology is extended to be a web based application. It seeks to elicit additional relevant (subjective) factors from people, which affect deontic decisions. The relative importance and utility values are used for the development of a decision model to predict people’s decisions. Fundamentally, this methodology was developed and intended to be applicable for a wide range of elicitation applications with minimal experimenter bias. Comparing with the traditional method, this online survey method reduces the limitation of time and space in data collection and this methodology can be applied in many fields. Two possible applications were addressed: robotic vehicles and the choice of medical treatment. In addition, this method can be applied to many research related disciplines in cross-cultural research due to its online ability with global capacity.