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.^

Shareholder ratification of the auditor and audit market competition

In my dissertation, I examine factors associated with firms’ submission of auditor selection for shareholder ratification and test if shareholder ratification of auditor selection is associated with the extent of price competition in the audit market (as measured by audit fees) and audit quality (as measured by clients’ earnings management). The dissertation is motivated from the recent recommendation of the U.S. Treasury’s Advisory Committee on Auditing Profession (ACAP) regarding the submission of auditor selection for shareholder ratification votes. The ACAP suggests that this practice may improve the competition in the audit market; yet, there is no empirical evidence supporting the ACAP’s recommendation. My dissertation attempts to fill the gap in the literature on an issue of current interest to the auditing profession. I find that firm size, CEO-Chair duality, insider ownership and institutional ownership are associated with the submission of auditor selection for shareholder ratification vote. However, I do not find an association between audit committee variables and the submission of auditor selection for shareholder ratification vote. The second essay investigates the association between auditor ratification and audit fees. Audit fees are higher in firms that submit auditor selection for shareholder ratification. The finding is not consistent with the increased price competition predicted by the ACAP. The third essay of my dissertation examine whether the submission of auditor selection for shareholder ratification is associated with earnings management. I find that firms that submit auditor selection for shareholder ratification are more likely to have lower level of earnings management. Overall, the results suggest that the same factors that are associated with higher quality monitoring also may be associated with the submission of auditor selection for shareholder ratification vote. The results call into question the one-size-fits-all approach recommended by the ACAP.

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.

Earnings management using estimates and the timing of adoption of accounting standards: An empirical examination of pensions

This study investigates the relationship between adoption timing of Statement of Financial Accounting Standards 87 and earnings management after adoption. Earnings management, defined consistent with Schipper (1989), is tested through hypotheses using (1) a portfolio approach and (2) pension rates. One Hypothesis uses a Modified Jones (1991) Model as a proxy for discretionary accruals and the other uses pension rate estimates.^ Statistically significant relationships are found between adoption timing and (1) discretionary accruals and (2) estimated rate-of-return (ROR) on pension plan assets. Early adopting firms tend to have lower discretionary accruals after adoption than on-time adopters. They also tend to use higher ROR estimates which are not supported by higher actual returns. Thus, while early adopters may be using ROR to manage income, this tends to not result in higher discretionary accruals. ^

Embedded heat pipes in cofired ceramic substrates for enhanced thermal management of electronics

A novel and new thermal management technology for advanced ceramic microelectronic packages has been developed incorporating miniature heat pipes embedded in the ceramic substrate. The heat pipes use an axially grooved wick structure and water as the working fluid. Prototype substrate/heat pipe systems were fabricated using high temperature co-fired ceramic (alumina). The heat pipes were nominally 81 mm in length, 10 mm in width, and 4 mm in height, and were charged with approximately 50–80 μL of water. Platinum thick film heaters were fabricated on the surface of the substrate to simulate heat dissipating electronic components. Several thermocouples were affixed to the substrate to monitor temperature. One end of the substrate was affixed to a heat sink maintained at constant temperature. The prototypes were tested and shown to successful and reliably operate with thermal loads over 20 Watts, with thermal input from single and multiple sources along the surface of the substrate. Temperature distributions are discussed for the various configurations and the effective thermal resistance of the substrate/heat pipe system is calculated. Finite element analysis was used to support the experimental findings and better understand the sources of the system's thermal resistance. ^

A non-linear discrete stochastic optimization model for Advanced Access patient appointment scheduling

Access to healthcare is a major problem in which patients are deprived of receiving timely admission to healthcare. Poor access has resulted in significant but avoidable healthcare cost, poor quality of healthcare, and deterioration in the general public health. Advanced Access is a simple and direct approach to appointment scheduling in which the majority of a clinic's appointments slots are kept open in order to provide access for immediate or same day healthcare needs and therefore, alleviate the problem of poor access the healthcare. This research formulates a non-linear discrete stochastic mathematical model of the Advanced Access appointment scheduling policy. The model objective is to maximize the expected profit of the clinic subject to constraints on minimum access to healthcare provided. Patient behavior is characterized with probabilities for no-show, balking, and related patient choices. Structural properties of the model are analyzed to determine whether Advanced Access patient scheduling is feasible. To solve the complex combinatorial optimization problem, a heuristic that combines greedy construction algorithm and neighborhood improvement search was developed. The model and the heuristic were used to evaluate the Advanced Access patient appointment policy compared to existing policies. Trade-off between profit and access to healthcare are established, and parameter analysis of input parameters was performed. The trade-off curve is a characteristic curve and was observed to be concave. This implies that there exists an access level at which at which the clinic can be operated at optimal profit that can be realized. The results also show that, in many scenarios by switching from existing scheduling policy to Advanced Access policy clinics can improve access without any decrease in profit. Further, the success of Advanced Access policy in providing improved access and/or profit depends on the expected value of demand, variation in demand, and the ratio of demand for same day and advanced appointments. The contributions of the dissertation are a model of Advanced Access patient scheduling, a heuristic to solve the model, and the use of the model to understand the scheduling policy trade-offs which healthcare clinic managers must make. ^

Knowledge assisted data management and retrieval in multimedia database systems

With the proliferation of multimedia data and ever-growing requests for multimedia applications, there is an increasing need for efficient and effective indexing, storage and retrieval of multimedia data, such as graphics, images, animation, video, audio and text. Due to the special characteristics of the multimedia data, the Multimedia Database management Systems (MMDBMSs) have emerged and attracted great research attention in recent years. Though much research effort has been devoted to this area, it is still far from maturity and there exist many open issues. In this dissertation, with the focus of addressing three of the essential challenges in developing the MMDBMS, namely, semantic gap, perception subjectivity and data organization, a systematic and integrated framework is proposed with video database and image database serving as the testbed. In particular, the framework addresses these challenges separately yet coherently from three main aspects of a MMDBMS: multimedia data representation, indexing and retrieval. In terms of multimedia data representation, the key to address the semantic gap issue is to intelligently and automatically model the mid-level representation and/or semi-semantic descriptors besides the extraction of the low-level media features. The data organization challenge is mainly addressed by the aspect of media indexing where various levels of indexing are required to support the diverse query requirements. In particular, the focus of this study is to facilitate the high-level video indexing by proposing a multimodal event mining framework associated with temporal knowledge discovery approaches. With respect to the perception subjectivity issue, advanced techniques are proposed to support users' interaction and to effectively model users' perception from the feedback at both the image-level and object-level.

An examination of accounting and auditing issues related to strategic environmental initiatives

Although corporate environmental accountability is receiving unprecedented attention in the United States from policy makers, the capital market, and the public at large, extant research is limited in its examination of the implications of strategic corporate environmental initiatives on accounting and auditing. The purpose of my dissertation is to address these implications by examining the association between firm environmental initiatives and audit fees, capital expenditures, and earnings quality using multivariate regression analysis. I find that firms engaged in more strategic environmental initiatives tend to have significantly higher audit fees and capital expenditures, and significantly lower levels of earnings manipulation measured using discretionary accruals. These results support the notion that auditors do recognize the importance of environmental initiatives when conducting the year-end financial statement audit, an idea that positively reflects upon the auditor’s monitoring role. The results also demonstrate the increased amount of capital resources required to participate in strategic environmental initiatives, an anecdotal notion that had yet to be empirically supported. This empirical support provides valuable insights on how environmental initiatives materially impact corporate financial statements. Finally, my results extend the extant literature by demonstrating that the superior financial performance reported by environmentally active firms is less likely driven by earnings manipulation by management, and by implication, more likely a result of real economic gains. Taken together, my dissertation establishes a strong and timely foundation for current and future research to explore corporate environmental initiatives in the United States and globally, a topic increasingly gaining momentum in today’s more eco-conscious world.^

A comprehensive study of bankruptcy prediction: Accounting ratios, market variables, and microstructure

Bankruptcy prediction has been a fruitful area of research. Univariate analysis and discriminant analysis were the first methodologies used. While they perform relatively well at correctly classifying bankrupt and nonbankrupt firms, their predictive ability has come into question over time. Univariate analysis lacks the big picture that financial distress entails. Multivariate discriminant analysis requires stringent assumptions that are violated when dealing with accounting ratios and market variables. This has led to the use of more complex models such as neural networks. While the accuracy of the predictions has improved with the use of more technical models, there is still an important point missing. Accounting ratios are the usual discriminating variables used in bankruptcy prediction. However, accounting ratios are backward-looking variables. At best, they are a current snapshot of the firm. Market variables are forward-looking variables. They are determined by discounting future outcomes. Microstructure variables, such as the bid-ask spread, also contain important information. Insiders are privy to more information that the retail investor, so if any financial distress is looming, the insiders should know before the general public. Therefore, any model in bankruptcy prediction should include market and microstructure variables. That is the focus of this dissertation. The traditional models and the newer, more technical models were tested and compared to the previous literature by employing accounting ratios, market variables, and microstructure variables. Our findings suggest that the more technical models are preferable, and that a mix of accounting and market variables are best at correctly classifying and predicting bankrupt firms. Multi-layer perceptron appears to be the most accurate model following the results. The set of best discriminating variables includes price, standard deviation of price, the bid-ask spread, net income to sale, working capital to total assets, and current liabilities to total assets.

Franchising in the Hospitality Industry: Accounting Aspects

Franchised businesses are a powerful factor in the American economy. The author provides a general overview of the area, citing statistics supporting its growth in the industry. Attention will be focused on accounting aspects of franchising, placing major emphasis on issues associated with the recognition of franchise fee revenue.

Controllers' Perceptions of the Importance of Accounting Skills to Lodging Managers

Are managerial accounting skills important to all managers? Which of the common managerial accounting skills are the most important to the non- accounting manager? The authors report on their descriptive research gathered from controllers in the hospitality industry which provides guide- lines for managers in these areas.

Industry Expresses Attitudes Toward Accounting, Finance Curricula

A survey of hospitality financial executives provides guidance for those planning accounting and finance curricula for schools of hospitality management. The authors discuss the results of their survey sponsored by the Association of Hospitality Financial Management Educators.

Embedded Heat Pipes in Cofired Ceramic Substrates for Enhanced Thermal Management of Electronics

A novel and new thermal management technology for advanced ceramic microelectronic packages has been developed incorporating miniature heat pipes embedded in the ceramic substrate. The heat pipes use an axially grooved wick structure and water as the working fluid. Prototype substrate/heat pipe systems were fabricated using high temperature co-fired ceramic (alumina). The heat pipes were nominally 81 mm in length, 10 mm in width, and 4 mm in height, and were charged with approximately 50-80 mL of water. Platinum thick film heaters were fabricated on the surface of the substrate to simulate heat dissipating electronic components. Several thermocouples were affixed to the substrate to monitor temperature. One end of the substrate was affixed to a heat sink maintained at constant temperature. The prototypes were tested and shown to successful and reliably operate with thermal loads over 20 Watts, with thermal input from single and multiple sources along the surface of the substrate. Temperature distributions are discussed for the various configurations and the effective thermal resistance of the substrate/heat pipe system is calculated. Finite element analysis was used to support the experimental findings and better understand the sources of the system's thermal resistance.