Methodology to create cost estimation models for engineer-to-order operations

This research addresses the problem of cost estimation for product development in engineer-to-order (ETO) operations. An ETO operation starts the product development process with a product specification and ends with delivery of a rather complicated, highly customized product. ETO operations are practiced in various industries such as engineering tooling, factory plants, industrial boilers, pressure vessels, shipbuilding, bridges and buildings. ETO views each product as a delivery item in an industrial project and needs to make an accurate estimation of its development cost at the bidding and/or planning stage before any design or manufacturing activity starts. ^ Many ETO practitioners rely on an ad hoc approach to cost estimation, with use of past projects as reference, adapting them to the new requirements. This process is often carried out on a case-by-case basis and in a non-procedural fashion, thus limiting its applicability to other industry domains and transferability to other estimators. In addition to being time consuming, this approach usually does not lead to an accurate cost estimate, which varies from 30% to 50%. ^ This research proposes a generic cost modeling methodology for application in ETO operations across various industry domains. Using the proposed methodology, a cost estimator will be able to develop a cost estimation model for use in a chosen ETO industry in a more expeditious, systematic and accurate manner. ^ The development of the proposed methodology was carried out by following the meta-methodology as outlined by Thomann. Deploying the methodology, cost estimation models were created in two industry domains (building construction and the steel milling equipment manufacturing). The models are then applied to real cases; the cost estimates are significantly more accurate than the actual estimates, with mean absolute error rate of 17.3%. ^ This research fills an important need of quick and accurate cost estimation across various ETO industries. It differs from existing approaches to the problem in that a methodology is developed for use to quickly customize a cost estimation model for a chosen application domain. In addition to more accurate estimation, the major contributions are in its transferability to other users and applicability to different ETO operations. ^

Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

Jury decision making and the Story Model: How do deliberation style and evidence order influence the story that juries create?

This study investigated the utility of the Story Model for decision making at the jury level by examining the influence of evidence order and deliberation style on story consistency and guilt. Participants were shown a video-taped trial stimulus and then provided case perceptions including a guilt judgment and a narrative about what occurred during the incident. Participants then deliberated for approximately thirty minutes using either an evidence-driven or verdict-driven deliberation style before again providing case perceptions, including a guilt determination, a narrative about what happened during the incident, and an evidence recognition test. Multi-level regression analyses revealed that evidence order, deliberation style and sample interacted to influence both story consistency measures and guilt. Among students, participants in the verdict-driven deliberation condition formed more consistent pro-prosecution stories when the prosecution presented their case in story-order, while participants in the evidence-driven deliberation condition formed more consistent pro-prosecution stories when the defense's case was presented in story-order. Findings were the opposite among community members, with participants in the verdict-driven deliberation condition forming more consistent pro-prosecution stories when the defense's case was presented in story-order, and participants in the evidence-driven deliberation condition forming more consistent pro-prosecution stories when the prosecution's case was presented in story-order. Additionally several story consistency measures influenced guilt decisions. Thus there is some support for the hypothesis that story consistency mediates the influence of evidence order and deliberation style on guilt decisions.

Color-based surface reflectance separation for scene illumination estimation and rendering

Given the importance of color processing in computer vision and computer graphics, estimating and rendering illumination spectral reflectance of image scenes is important to advance the capability of a large class of applications such as scene reconstruction, rendering, surface segmentation, object recognition, and reflectance estimation. Consequently, this dissertation proposes effective methods for reflection components separation and rendering in single scene images. Based on the dichromatic reflectance model, a novel decomposition technique, named the Mean-Shift Decomposition (MSD) method, is introduced to separate the specular from diffuse reflectance components. This technique provides a direct access to surface shape information through diffuse shading pixel isolation. More importantly, this process does not require any local color segmentation process, which differs from the traditional methods that operate by aggregating color information along each image plane. ^ Exploiting the merits of the MSD method, a scene illumination rendering technique is designed to estimate the relative contributing specular reflectance attributes of a scene image. The image feature subset targeted provides a direct access to the surface illumination information, while a newly introduced efficient rendering method reshapes the dynamic range distribution of the specular reflectance components over each image color channel. This image enhancement technique renders the scene illumination reflection effectively without altering the scene’s surface diffuse attributes contributing to realistic rendering effects. ^ As an ancillary contribution, an effective color constancy algorithm based on the dichromatic reflectance model was also developed. This algorithm selects image highlights in order to extract the prominent surface reflectance that reproduces the exact illumination chromaticity. This evaluation is presented using a novel voting scheme technique based on histogram analysis. ^ In each of the three main contributions, empirical evaluations were performed on synthetic and real-world image scenes taken from three different color image datasets. The experimental results show over 90% accuracy in illumination estimation contributing to near real world illumination rendering effects. ^

Improved Annual Average Daily Traffic (AADT) estimation for local roads using parcel-level travel demand modeling

Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses. By definition, AADT is the average 24-hour volume at a highway location over a full year. Traditionally, AADT is estimated using a mix of permanent and temporary traffic counts. Because field collection of traffic counts is expensive, it is usually done for only the major roads, thus leaving most of the local roads without any AADT information. However, AADTs are needed for local roads for many applications. For example, AADTs are used by state Departments of Transportation (DOTs) to calculate the crash rates of all local roads in order to identify the top five percent of hazardous locations for annual reporting to the U.S. DOT. ^ This dissertation develops a new method for estimating AADTs for local roads using travel demand modeling. A major component of the new method involves a parcel-level trip generation model that estimates the trips generated by each parcel. The model uses the tax parcel data together with the trip generation rates and equations provided by the ITE Trip Generation Report. The generated trips are then distributed to existing traffic count sites using a parcel-level trip distribution gravity model. The all-or-nothing assignment method is then used to assign the trips onto the roadway network to estimate the final AADTs. The entire process was implemented in the Cube demand modeling system with extensive spatial data processing using ArcGIS. ^ To evaluate the performance of the new method, data from several study areas in Broward County in Florida were used. The estimated AADTs were compared with those from two existing methods using actual traffic counts as the ground truths. The results show that the new method performs better than both existing methods. One limitation with the new method is that it relies on Cube which limits the number of zones to 32,000. Accordingly, a study area exceeding this limit must be partitioned into smaller areas. Because AADT estimates for roads near the boundary areas were found to be less accurate, further research could examine the best way to partition a study area to minimize the impact.^