Predictive models for charitable giving using machine learning techniques

Private giving represents more than three fourths of all U.S. charitable donations, about 2% of total Gross Domestic Product (GDP). Private giving is a significant factor in funding the nonprofit sector of the U.S. economy, which accounts for more than 10% of total GDP. Despite the abundance of data available through tax forms and other sources, it is unclear which factors influence private donation, and a reliable predictive mechanism remains elu- sive. This study aims to develop predictive models to accurately estimate future charitable giving based on a set of potentially influential factors. We have selected several factors, including unemployment rate, household income, poverty level, population, sex, age, ethnic- ity, education level, and number of vehicles per household. This study sheds light on the relationship between donation and these variables. We use Stepwise Regression to identify the most influential variables among the available variables, based on which predictive mod- els are developed. Multiple Linear Regression (MLR) and machine learning techniques, including Artificial Neural Networks (ANN) and Support Vector Regression (SVR) are used to develop the predictive models. The results suggest that population, education level, and the amount of charitable giving in the previous year are the most significant, independent variables. We propose three predictive models (MLR, ANN, and SVR) and validate them using 10-fold cross-validation method, then evaluate the performance using 9 different mea- suring criteria. All three models are capable of predicting the amount of future donations in a given region with good accuracy. Based on the evaluation criteria, using a test data set, ANN outperforms SVR and MLR in predicting the amount of charitable giving in the following year

Predictive models for charitable giving using machine learning techniques.

Private giving represents more than three fourths of all U.S. charitable donations, about 2% of total Gross Domestic Product (GDP). Private giving is a significant factor in funding the nonprofit sector of the U.S. economy, which accounts for more than 10% of total GDP. Despite the abundance of data available through tax forms and other sources, it is unclear which factors influence private donation, and a reliable predictive mechanism remains elusive. This study aims to develop predictive models to accurately estimate future charitable giving based on a set of potentially influential factors. We have selected several factors, including unemployment rate, household income, poverty level, population, sex, age, ethnicity, education level, and number of vehicles per household. This study sheds light on the relationship between donation and these variables. We use Stepwise Regression to identify the most influential variables among the available variables, based on which predictive models are developed. Multiple Linear Regression (MLR) and machine learning techniques, including Artificial Neural Networks (ANN) and Support Vector Regression (SVR) are used to develop the predictive models. The results suggest that population, education level, and the amount of charitable giving in the previous year are the most significant, independent variables. We propose three predictive models (MLR, ANN, and SVR) and validate them using 10-fold cross-validation method, then evaluate the performance using 9 different measuring criteria. All three models are capable of predicting the amount of future donations in a given region with good accuracy. Based on the evaluation criteria, using a test data set, ANN outperforms SVR and MLR in predicting the amount of charitable giving in the following year

Allocation of Bulk Tanks to Improve Industrial Gas Distribution to Customers with Time-Varying Demand

In this paper, we describe the results from an academic and industry collaboration to address the bulk tank allocation problem for industrial gas distribution systems where customer demand varies over time. The bulk tank allocation problem determines the preferred size of bulk tanks to assign to customer sites to minimize recurring gas distribution costs and initial tank installation costs. The problem is modeled as a mixed-integer programming model, and three solution approaches are presented. In the first two approaches, the problem is decomposed and a restricted master problem is solved. The third approach is a two phase periodically restricting heuristic approach. The results demonstrate the opportunities for substantial improvements in resource allocation and reductions in operational costs

Integrating ride-hailing services with transit: An exploratory planning framework

Ride-hailing programs established through partnerships between transit agencies and transportation network companies (TNCs) are an emerging innovative practice in public transportation services. Literature and practice attest to the potential of these programs. Recent research reports have produced how-to guides and other useful material to guide transit agencies through the various stages involved in exploring, understanding, defining, and establishing such a program. However, these materials lack adequate representation of programs established within small transit agencies because very few of the existing case studies are of ride-hailing programs operating in rural and small urban areas or small University/College/campus towns. In this study, to address this gap, we focus on the early planning stages of establishing a ride-hailing program within a small transit agency. We propose a framework based on multi-criteria decision analysis methods that can be used to explore the perceived and actual potential benefits, costs, and performance of different ride-hailing service models. We illustrate this methodology using the case study of Mountain Line Transit Authority, operating in Monongalia County, West Virginia, United States (U.S.). The results highlight the need for a local exploration of both perceived and actual measures of the benefits and costs of different service models. Concerning the empirical results, the findings universally suggest that any ride-hailing service model is perceived as an enhancement of the existing transit services. Finally, there is a consensus that the area and the agency would benefit the least from implementing a late night/early morning ride-hailing service model