Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Efficient asynchronous scheduling algorithms for cost-effective buffered crossbar switches

Buffered crossbar switches have recently attracted considerable attention as the next generation of high speed interconnects. They are a special type of crossbar switches with an exclusive buffer at each crosspoint of the crossbar. They demonstrate unique advantages over traditional unbuffered crossbar switches, such as high throughput, low latency, and asynchronous packet scheduling. However, since crosspoint buffers are expensive on-chip memories, it is desired that each crosspoint has only a small buffer. This dissertation proposes a series of practical algorithms and techniques for efficient packet scheduling for buffered crossbar switches. To reduce the hardware cost of such switches and make them scalable, we considered partially buffered crossbars, whose crosspoint buffers can be of an arbitrarily small size. Firstly, we introduced a hybrid scheme called Packet-mode Asynchronous Scheduling Algorithm (PASA) to schedule best effort traffic. PASA combines the features of both distributed and centralized scheduling algorithms and can directly handle variable length packets without Segmentation And Reassembly (SAR). We showed by theoretical analysis that it achieves 100% throughput for any admissible traffic in a crossbar with a speedup of two. Moreover, outputs in PASA have a large probability to avoid the more time-consuming centralized scheduling process, and thus make fast scheduling decisions. Secondly, we proposed the Fair Asynchronous Segment Scheduling (FASS) algorithm to handle guaranteed performance traffic with explicit flow rates. FASS reduces the crosspoint buffer size by dividing packets into shorter segments before transmission. It also provides tight constant performance guarantees by emulating the ideal Generalized Processor Sharing (GPS) model. Furthermore, FASS requires no speedup for the crossbar, lowering the hardware cost and improving the switch capacity. Thirdly, we presented a bandwidth allocation scheme called Queue Length Proportional (QLP) to apply FASS to best effort traffic. QLP dynamically obtains a feasible bandwidth allocation matrix based on the queue length information, and thus assists the crossbar switch to be more work-conserving. The feasibility and stability of QLP were proved, no matter whether the traffic distribution is uniform or non-uniform. Hence, based on bandwidth allocation of QLP, FASS can also achieve 100% throughput for best effort traffic in a crossbar without speedup.

The order selection and lot sizing problem in the make-to-order environment

This research is motivated by the need for considering lot sizing while accepting customer orders in a make-to-order (MTO) environment, in which each customer order must be delivered by its due date. Job shop is the typical operation model used in an MTO operation, where the production planner must make three concurrent decisions; they are order selection, lot size, and job schedule. These decisions are usually treated separately in the literature and are mostly led to heuristic solutions. The first phase of the study is focused on a formal definition of the problem. Mathematical programming techniques are applied to modeling this problem in terms of its objective, decision variables, and constraints. A commercial solver, CPLEX is applied to solve the resulting mixed-integer linear programming model with small instances to validate the mathematical formulation. The computational result shows it is not practical for solving problems of industrial size, using a commercial solver. The second phase of this study is focused on development of an effective solution approach to this problem of large scale. The proposed solution approach is an iterative process involving three sequential decision steps of order selection, lot sizing, and lot scheduling. A range of simple sequencing rules are identified for each of the three subproblems. Using computer simulation as the tool, an experiment is designed to evaluate their performance against a set of system parameters. For order selection, the proposed weighted most profit rule performs the best. The shifting bottleneck and the earliest operation finish time both are the best scheduling rules. For lot sizing, the proposed minimum cost increase heuristic, based on the Dixon-Silver method performs the best, when the demand-to-capacity ratio at the bottleneck machine is high. The proposed minimum cost heuristic, based on the Wagner-Whitin algorithm is the best lot-sizing heuristic for shops of a low demand-to-capacity ratio. The proposed heuristic is applied to an industrial case to further evaluate its performance. The result shows it can improve an average of total profit by 16.62%. This research contributes to the production planning research community with a complete mathematical definition of the problem and an effective solution approach to solving the problem of industry scale.

Essays on risk aversion, diversification and non -participation

My dissertation consists of three essays. The central theme of these essays is the psychological factors and biases that affect the portfolio allocation decision. The first essay entitled, “Are women more risk-averse than men?” examines the gender difference in risk aversion as revealed by actual investment choices. Using a sample that controls for biases in the level of education and finance knowledge, there is evidence that when individuals have the same level of education, irrespective of their knowledge of finance, women are no more risk-averse than their male counterparts. However, the gender-risk aversion relation is also a function of age, income, wealth, marital status, race/ethnicity and the number of children in the household. The second essay entitled, “Can diversification be learned ?” investigates if investors who have superior investment knowledge are more likely to actively seek diversification benefits and are less prone to allocation biases. Results of cross-sectional analyses suggest that knowledge of finance increases the likelihood that an investor will efficiently allocate his direct investments across the major asset classes; invest in foreign assets; and hold a diversified equity portfolio. However, there is no evidence that investors who are more financially sophisticated make superior allocation decisions in their retirement savings. The final essay entitled, “The demographics of non-participation ”, examines the factors that affect the decision not to hold stocks. The results of probit regression models indicate that when individuals are highly educated, the decision to not participate in the stock market is less related to demographic factors. In particular, when individuals have attained at least a college degree and have advanced knowledge of finance, they are significantly more likely to invest in equities either directly or indirectly through mutual funds or their retirement savings. There is also evidence that the decision not to hold stocks is motivated by short-term market expectations and the most recent investment experience. The findings of these essays should increase the body of research that seeks to reconcile what investors actually do (positive theory) with what traditional theories of finance predict that investors should do (normative theory).

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

Essays on Risk Aversion, Diversification and Non-Participation

My dissertation consists of three essays. The central theme of these essays is the psychological factors and biases that affect the portfolio allocation decision. The first essay entitled, “Are women more risk-averse than men?” examines the gender difference in risk aversion as revealed by actual investment choices. Using a sample that controls for biases in the level of education and finance knowledge, there is evidence that when individuals have the same level of education, irrespective of their knowledge of finance, women are no more risk-averse than their male counterparts. However, the gender-risk aversion relation is also a function of age, income, wealth, marital status, race/ethnicity and the number of children in the household. The second essay entitled, “Can diversification be learned?” investigates if investors who have superior investment knowledge are more likely to actively seek diversification benefits and are less prone to allocation biases. Results of cross-sectional analyses suggest that knowledge of finance increases the likelihood that an investor will efficiently allocate his direct investments across the major asset classes; invest in foreign assets; and hold a diversified equity portfolio. However, there is no evidence that investors who are more financially sophisticated make superior allocation decisions in their retirement savings. The final essay entitled, “The demographics of non-participation”, examines the factors that affect the decision not to hold stocks. The results of probit regression models indicate that when individuals are highly educated, the decision to not participate in the stock market is less related to demographic factors. In particular, when individuals have attained at least a college degree and have advanced knowledge of finance, they are significantly more likely to invest in equities either directly or indirectly through mutual funds or their retirement savings. There is also evidence that the decision not to hold stocks is motivated by short-term market expectations and the most recent investment experience. The findings of these essays should increase the body of research that seeks to reconcile what investors actually do (positive theory) with what traditional theories of finance predict that investors should do (normative theory).

An analysis of teachers' perceptions of informal science professional development

Science professional development, which is fundamental to science education improvement, has been described as being weak and fragmentary. The purpose of this study was to investigate teachers' perceptions of informal science professional development to gain an in-depth understanding of the essence of the phenomenon and related science-teaching dispositions. Based on the frameworks of phenomenology, constructivism, and adult learning theory, the focus was on understanding how the phenomenon was experienced within the context of teachers' everyday world. ^ Data were collected from eight middle-school teachers purposefully selected because they had participated in informal programs during Project TRIPS (Teaching Revitalized Through Informal Programs in Science), a collaboration between the Miami-Dade school district, government agencies (including NASA), and non-profit organizations (including Audubon of Florida). In addition, the teachers experienced hands-on labs offered through universities (including the University of Arizona), field sites, and other agencies. ^ The study employed Seidman's (1991) three-interview series to collect the data. Several methods were used to enhance the credibility of the research, including using triangulation of the data. The interviews were transcribed, color-coded and organized into six themes that emerged from the data. The themes included: (a) internalized content knowledge, (b) correlated hands-on activities, (c) enhanced science-teaching disposition, (d) networking/camaraderie, (e) change of context, and (f) acknowledgment as professionals. The teachers identified supportive elements and constraints related to each theme. ^ The results indicated that informal programs offering experiential learning opportunities strengthened understanding of content knowledge. Teachers implemented hands-on activities that were explicitly correlated to their curriculum. Programs that were conducted in a relaxed context enhanced teachers' science-teaching dispositions. However, a lack of financial and administrative support, perceived safety risks, insufficient reflection time, and unclear itineraries impeded program implementation. The results illustrated how informal educators can use this cohesive model as they develop programs that address the supports and constraints to teachers' science instruction needs. This, in turn, can aid teachers as they strive to provide effective science instruction to students; notions embedded in reforms. Ultimately, this can affect how learners develop the ability to make informed science decisions that impact the quality of life on a global scale. ^

Labor and Menu Category: Effects on Analysis

Menu analysis is the gathering and processing of key pieces of information to make it more manageable and understandable. Ultimately, menu analysis allows managers to make more informed decisions about prices, costs, and items to be included on a menu. The author discusses If labor as well as food casts need to be included in menu analysis and if managers need to categorize menu items differently when doing menu analysis based on customer eating patterns.

The Specialist: Coming Soon To Your Local Hotel

In an article entitled - The Specialist: Coming Soon To Your Local Hotel - by Stan Bromley, Regional Vice President and General Manager, Four Seasons Clift Hotel, San Francisco, the author’s introduction states: “An experienced hotelier discusses the importance of the delivery of a high “quality-to-value” ratio consistently to guests, particularly as the hotel market becomes specialized and a distinction is drawn between a “property” and a “hotel.” The author’s primary intention is to make you, the reader, aware of changes in the hospitality/hotel marketplace. From the embryo to the contemporary, the hotel market has consistently evolved; this includes but is not limited to mission statement, marketing, management, facilities, and all the tangibles and intangibles of the total hotel experience. “Although we are knocking ourselves out trying to be everything to everyone, I don't think hotel consumers are as interested in “mixing and matching” as they were in the past,” Bromley says. “Today's hotel guest is looking for “specialized care,” and is increasingly skeptical of our industry-wide hotel ads and promises of greatness.” As an example Bromley makes an analogy using retail outlets such as Macy’s, Saks, and Sears, which cater to their own unique market segment. Hotels now follow the same outline, he allows. “In my view, two key factors will make a hotel a success,” advises Bromley. “First, know your specialty and market to that segment. Second, make sure you consistently offer a high quality-to-value ratio. That means every day.” To emphasize that second point, Bromley offers this bolstering thought, “The second factor that will make or break your business is your ability to deliver a high "quality/value" ratio-and to do so consistently.” The author evidently considers quality-to-value ratio to be an important element. Bromley emphasizes the importance of convention and trade show business to the hotel industry. That business element cannot be over-estimated in his opinion. This doesn’t mean an operator who can accommodate that type of business should exclude other client opportunities outside the target market. It does mean, however, these secondary opportunities should only be addressed after pursuing the primary target strategy. After all, the largest profit margin lies in the center of the target. To amplify the above statement, and in reference to his own experience, Bromley says, “Being in the luxury end of the business I, on the other hand, need to uncover and book individuals and small corporate meetings more than convention or association business.

Web-Based Training in the U.S Lodging Industry

Menu analysis is the gathering and processing of key pieces of information to make it more manageable and understand- able. Ultimately, menu analysis allows managers to make more informed decisions about prices, costs, and items to be included on a menu. The author discusses If labor as well as food casts need to be included in menu analysis and if managers need to categorize menu items differently when doing menu analysis based on customer eating patterns.

The Role of the FIU Nature Preserve in Promoting Physical Activity

The FIU Nature Preserve was established over thirty-five years ago as an environmental educational center where visitors could experience and learn about local south Florida ecosystems and organisms. This 16-acre facility in the heart of the MMC campus has recently become a popular outdoor fitness destination since the inauguration of a jogging path during Fall 2013. This study set out to quantify how many people visit the FIU Nature Preserve annually, who they are, and what they are doing there. It is also assessing the effect of the FIU Nature Preserve on the overall health of the university community since studies have found that physical activity and contact with nature are positively associated with good health. A pilot was completed during Fall 2014, and the study is on track to finish March 22, 2015. To measure current visitation, two types of surveys were done on seven days across seven weeks during the spring of 2015, visitation counts and in-person surveys. By understanding the reasons and ways people discover and embark on regular use of natural areas, land managers and policy makers can make more informed decisions. As human population and development continue to grow, new ways to integrate natural areas into our urban environment and our lifestyles must be found. In this way, natural resource conservation could be championed as a way for communities to promote physical activity and good health beyond simply using their intrinsic value.

Branch and price solution approach for order acceptance and capacity planning in make -to -order operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. ^ For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver.^ The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. ^ The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.^

Branch and Price Solution Approach for Order Acceptance and Capacity Planning in Make-to-Order Operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.