scholarly journals Application of Bayesian Hierarchical Negative Binomial Finite Mixture Model for Cost-Benefit Analysis of Barriers Optimization, Accounting for Severe Heterogeneity

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 288
Author(s):  
Mahdi Rezapour ◽  
Khaled Ksaibati
Keyword(s):  
Mixture Model ◽  
Negative Binomial ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Optimum Number ◽  
Benefit Analysis ◽  
Bayesian Hierarchical ◽  
Highway System

The Wyoming Department of Transportation (WYDOT) initiated a project to optimize the heights of barriers that are not satisfying the barrier design criteria, while prioritizing them based on an ability to achieve higher monetary benefits. The equivalent property damage only (EPDO) was used in this study to account for both aspects of crash frequency and severity. Data of this type are known to have overdispersion, that is having a variance greater than the mean. Thus, a negative binomial model was implemented to address the over-dispersion issue of the dataset. Another challenge of the dataset used in this study was the heterogeneity of the dataset. The data heterogeneity resulted from various factors such as data being aggregated across two highway systems, and the presence of two barrier types in the whole state. Thus, it is not practical to assign a subjective hierarchy such as a highway system or barrier types to address the issue of severe heterogeneity in the dataset. Under these conditions, a finite mixture model (FMM) was implemented to find a best distribution parameter to characterize the observations. With this technique, after the optimum number of mixtures was identified, those clusters were assigned to various observations. However, previous studies mostly employed just the finite mixture model (FMM), with various distributions, to account for unobserved heterogeneity. The problem with the FMM approach is that it results in a loss of information: for instance, it would come up with N number of equations, where each result would use only part of the whole dataset. On the other hand, some studies used a subjective hierarchy to account for the heterogeneity in the dataset, such as the effect of seasonality or highway system; however, those subjective hierarchies might not account for the optimum heterogeneity in the dataset. Thus, we implement a new methodology, the Bayesian Hierarchical Finite Mixture (BHFMM) to employ the FMM without losing information, while also accounting for the heterogeneity in the dataset, by considering objective and unbiased hierarchies. As the Bayesian technique has the shortcoming of labeling the observations due to label switching; the FMM parameters were estimated by maximum likelihood technique. Results of the identified model were converted to an equation for implementation of machine learning techniques. The heights were optimized to an optimal value and the EPDO was predicted based on the changes. The results of the cost–benefit analysis indicated that after spending about 4 million dollars, the WYDOT would not only recover the expenses, but could also expect to save more than $4 million additional dollars through traffic barrier crash reduction.

The Effects of Intensive Postrelease Correctional Supervision on Recidivism: A Natural Experiment

2021 ◽  
pp. 088740342199843
Author(s):  
Grant Duwe ◽  
Susan McNeeley
Keyword(s):  
Negative Binomial ◽  
Cox Regression ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Benefit Analysis ◽  
Intensive Supervision ◽  
Binomial Regression ◽  
The Impact

In July 2018, the Minnesota Department of Corrections revised the criteria it uses to place soon-to-be-released prisoners on intensive supervision by shifting from mostly offense-based conditions to those based exclusively on risk. In doing so, this policy change provided a unique opportunity to evaluate not only the impact of intensive supervision on recidivism but also whether risk-based policies lead to better outcomes. Using Cox regression and negative binomial regression on a sample of 1,818 persons released in 2018, we found that intensive supervised release (ISR) significantly reduced the hazard for general, felony, and violent reoffending. We also found, however, that ISR significantly increased the risk of a technical violation revocation. The findings from our cost–benefit analysis showed that, despite the relatively high costs it incurred, ISR was a cost-effective intervention because it reduced reoffending for those with a higher risk of committing serious, violent crimes.

scholarly journals Application of Bayesian Hierarchical Finite Mixture Model to Account for Severe Heterogeneous Crash Data

Signals ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 41-52
Author(s):  
Mahdi Rezapour ◽  
Khaled Ksaibati
Keyword(s):  
Mixture Model ◽  
Traffic Safety ◽  
Unobserved Heterogeneity ◽  
Final Analysis ◽  
Finite Mixture Model ◽  
Information Criteria ◽  
Finite Mixture ◽  
Maximum Likelihood Estimates ◽  
Bayesian Hierarchical ◽  
Explanatory Variables

Various techniques have been proposed in the literature to account for the observed and unobserved heterogeneity in the crash dataset. Those include techniques such as the finite mixture model (FMM), or hierarchical techniques. The FMM could provide a flexible framework by providing various distributions for various individual observations. However, the shortcoming of the standard FMM is that it cannot account for the heterogeneity in a single model’s structure, and the data needs to be disaggregated to its resultant subsamples. That would result in a loss of information. On the other hand, a second plausible approach is to use a hierarchical technique to account for the data heterogeneities, being based on various explanatory variables, and based on engineering intuition. In the context of traffic safety, while some researchers, for instance, considered the seasonality, some others considered highway systems or even genders. However, a question might arise: are the same observations within a same hierarchy homogenous? Are all the observations within different clusters heterogeneous? Additionally, how about other variables? Although the results in the literature highlighted accounting for the structure of the dataset would result in an acceptable interclass correlation (ICC), and also result in a significant improvement in terms of reduction in the deviance information criteria (DIC), there is no justification why to use those specific hierarchies and reject others. A more reasonable approach is to let the algorithm come up with the best distributions based on the provided parameters and accommodate observations to the related mixtures. In that approach those observations that belong to various subjective hierarchies, e.g., winter versus summer, but found to be similar would be set in a similar cluster. That is why we proposed this methodology to implement an objective hierarchy of the FMM to be used for the hierarchical technique. Here, due to the label switching problem of the FMM in the context of Bayesian, the FMM first conducted in the context of maximum likelihood estimates, and then assigned observations were used for the final analysis. The results of the DIC highlighted a significant improvement in the model fit compared with a subjective assigned hierarchy based on highway system. Additionally, although the subjective model resulted in a very low ICC due to so much heterogeneity in the dataset, the implemented methodology resulted in an acceptable ICC (0.3), justifying the use of hierarchy. The Bayesian hierarchical finite mixture model (BHFMM) is one of earliest application in traffic safety studies. The findings of this study have important implications for the future studies to account for a higher heterogeneity of the crash dataset based on the distance of observations to each cluster.

scholarly journals Two-Component Bayesian Hierarchical Models for Cost-Benefit Analysis of Traffic Barrier Crash Count

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 179
Author(s):  
Mahdi Rezapour ◽  
Khaled Ksaibati
Keyword(s):  
Machine Learning ◽  
Cost Benefit Analysis ◽  
Critical Role ◽  
Cost Benefit ◽  
Benefit Analysis ◽  
Machine Learning Technique ◽  
Bayesian Hierarchical ◽  
Automated Method ◽  
Two Component ◽  
Learning Technique

Road departure crashes tend to be hazardous, especially in rural areas like Wyoming. Traffic barriers could be installed to mitigate the severity of those crashes. However, the severity of traffic barriers crashes still persists. Besides various drivers and environmental characteristics, the roadways and barrier geometric characteristics play a critical role in the severity of barrier crashes. The Wyoming department of transportation (WYDOT) has initiated a project to identify and optimize the heights of those barriers that are below the design standard, while prioritizing them based on the monetary benefit. This is to optimize first barriers that need an immediate attention, considering the limited budget, and then all other barriers being under design. In order to account for both aspects of frequency and severity of crashes, equivalent property damage only (EPDO) was considered. The data of this type besides having an over-dispersion, exhibits excess amounts of zeroes. Thus, a two-component model was employed to provide a flexible way of addressing this problem. Beside this technique, one-component hierarchical modeling approach was considered for a comparison purpose. This paper presents an empirical cost-benefit analysis based on Bayesian hierarchical machine learning techniques. After identifying the best model in terms of the performance, deviance information criterion (DIC), the results were converted into an equation, and the equation was used for a purpose of machine learning technique. An automated method generated cost based on barriers’ current conditions, and then based on optimized barrier heights. The empirical analysis showed that cost-sensitive modeling and machine learning technique deployment could be used as an effective way for cost-benefit analysis. That could be achieved through measuring the associated costs of barriers’ enhancements, added benefits over years and consequently, barrier prioritization due to lack of available budget. A comprehensive discussion across the two-component models, zero-inflated and hurdle, is included in the manuscript.

The Use of Cost-Benefit Analysis for Evaluating Efficiency of Public Investment Projects (The Case of Sochi-2014 Winter Olympic Games)

2011 ◽  
pp. 57-78
Author(s):  
I. Pilipenko
Keyword(s):  
Net Present Value ◽  
Olympic Games ◽  
Cost Benefit Analysis ◽  
Public Investment ◽  
Cost Benefit ◽  
Investment Efficiency ◽  
Benefit Analysis ◽  
Step Method ◽  
Investment Projects ◽  
The Usa

The paper analyzes shortcomings of economic impact studies based mainly on input- output models that are often employed in Russia as well as abroad. Using studies about sport events in the USA and Olympic Games that took place during the last 30 years we reveal advantages of the cost-benefit analysis approach in obtaining unbiased assessments of public investments efficiency; the step-by-step method of cost-benefit analysis is presented in the paper as well. We employ the project of Sochi-2014 Winter Olympic and Paralympic Games in Russia to evaluate its efficiency using cost-benefit analysis for five accounts (areas of impact), namely government, households, environment, economic development, and social development, and calculate the net present value of the project taking into account its possible alternatives. In conclusion we suggest several policy directions that would enhance public investment efficiency within the Sochi-2014 Olympics.

Hostile Takeovers and Anti-takeover Defenses in the Russian Corporate Market

2007 ◽  
pp. 70-84 ◽  
Author(s):  
E. Demidova
Keyword(s):  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Benefit Analysis ◽  
Costs And Benefits ◽  
Hostile Takeovers ◽  
Russian Market ◽  
Takeover Defenses ◽  
The Cost ◽  
European Markets

This article analyzes definitions and the role of hostile takeovers at the Russian and European markets for corporate control. It develops the methodology of assessing the efficiency of anti-takeover defenses adapted to the conditions of the Russian market. The paper uses the cost-benefit analysis, where the costs and benefits of the pre-bid and post-bid defenses are compared.

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