David Ripplinger

Rural School Vehicle Routing Problem

The school bus routing problem traditionally has been defined in an urban context. However, because of the unique attributes of the problem in rural areas, traditional heuristic methods for solving the problem may produce impractical results. In many cases, these characteristics also provide the opportunity to investigate what size and mix of vehicles, whether large or small buses, conforming vans, or other modes, are most efficient. In addition, these vehicles may be further differentiated by the presence of equipment for transporting students with special needs. To address this situation, a mathematical model of the problem was constructed and a new heuristic was developed. This heuristic consists of two parts: constructing the initial route and then improving it by using a fixed tenure tabu search algorithm. This rural routing heuristic, in addition to several existing ones, is then applied to a randomly generated school district with rural characteristics. For the relevant measure, a function of stude...

An Assessment of Demand for Rural Intercity Transportation Services in a Changing Environment

With higher fuel costs and changing economic conditions, travel behavior and the level and allocation of resources in highways, rail, air, and transit service in rural areas may be changing. The objective of this study is to determine the attitude of would-be passengers in their choice of mode and the factors determining their choice in rural and small urban areas. A stated preference survey was developed and administered to residents of North Dakota and northwest and west central Minnesota. The survey asked respondents to identify their mode of choice in different hypothetical situations in which five modes were available—automobile, air, bus, train, and van—under differing mode and trip characteristics. A multinomial logit model was used to estimate the likelihood that an individual would choose a given mode on the basis of the characteristics of the mode, the characteristics of the individual, and the characteristics of the trip. Results show that travelers, especially those of lower income, respond to higher gasoline prices by choosing alternative modes in greater numbers, suggesting rural intercity bus, van, and rail ridership would increase if gasoline prices rose.

Insuring Against Losses from Transgenic Contamination: The Case of Pharmaceutical Maize

Concerns about the risk of food supply contamination have limited the development and commercialization of certain pharmaceutical plants. This article develops an insurance pricing model that helps translate these concerns into a cost-benefit analysis. The model first estimates the physical dispersal of maize pollen subject to a number of weather parameters. This distribution is then validated with the limited amount of currently available field trial data. The physical distribution is then used to calculate the premium for a fair-valued insurance policy that would fund the destruction of possibly contaminated fields. The flexible framework can be readily adapted to other crops, management practices, and regions.

The Value of Switching Production Options in a Flexible Biorefinery

This study analyses the value of a switching option in a flexible biorefinery plant that produces ethanol and sugar juice in a single plant using energy beets. A real-options approach is used to compute threshold prices and optimal switching decision rules for switching between sugar and ethanol production modes. The analysis shows that it is economically optimal to keep producing ethanol then switching to sugar juice, given the stochastic price parameters of the two products.

Marginal Cost Pricing and Subsidy of Small Urban Transit

This study analyzes economies of scale and density as a rationale for subsidizing transit agencies in small urban areas. A long-run cost model is estimated with data from 2006 to 2009 for 168 transit agencies that directly operated fixed-route bus service in small urban areas. With vehicle revenue miles used as transit output, results show that small urban transit agencies experience economies of scale and density. A full-cost model is estimated; the model includes the addition of external costs and benefits. External benefits result from the reduced waiting times that follow an increase in service frequency. Results are used to estimate the optimal fare, which is equal to marginal social cost of service. The needed subsidy is calculated as the difference between the revenue generated by the optimal fare and that needed to maintain efficient levels of production. The rationale for subsidies is an important issue, as many agencies have experienced recent reductions in operational funding.

Classifying Rural and Small Urban Transit Agencies

Rural and small urban transit agencies are classified into peer groups by the hierarchical cluster analysis and data from the Rural National Transit Database. The objective is to provide a basis for the comparison of individual agency performance with peer group performance as well as econometric analysis between and within peer groups. Rural and small urban transit agencies are first assigned to three groups by service provided: demand–response, fixed-route, and demand–response and fixed-route service. A fourth group is created to accommodate the many transit agencies providing demand–response service that did not report vehicle hour data. The four groups are then clustered by using vehicle mile, vehicle hour (when available), and fleet size variables. Operating statistics for each cluster by group are presented. The process for comparing individual agency performance with its respective cluster is described. The Rural National Transit Database demonstrates its usefulness as a consistent, uniform national data set. However, additional service area information would accommodate clustering based on exogenous, as opposed to endogenous, variables, as necessary with the current data set.