Teresa M. Adams

A spatial data model design for feature-based geographical information systems

Abstract In state-of-the-art GIS, geographical features are represented as geometric objects with associated topological relations and classification attributes. Semantic relations and intrinsic interrelations of the features themselves are generally neglected. In this paper, a feature-based model that enhances the representation of geographical features is described. Features, as the fundamental depiction of geographical phenomena, encompass both real world entities and digital representation. A feature-based object incorporates both topological relations among geometric elements and non-topological (semantic) relations among features. The development of an object-oriented prototype feature-based GIS that supports relations between feature attributes and feature classes is described. Object-oriented concepts such as class inheritance and polymorphism facilitate the development of feature-based GTS.

A data model for multi-dimensional transportation applications

The distinctive data management needs of transportation agencies in developing transportation-based applications dictate the presence of essential elements that are beyond existing transportation location referencing system data models. These elements include multi-dimensional location referencing, multiscale representation, navigation, and temporal GIS. This paper looks at the foundation for these elements, describes the characteristics of these elements and offers solutions through a transportation-based multi-dimensional data model. By managing data expressed in one to four dimensions, the Multi-Dimensional Location Referencing System (MDLRS) data model allows organizations to implement improved solutions for transportation systems using advanced spatial technologies (e.g. GPS).

A fuzzy navigation system for mobile construction robots

Abstract Fuzzy navigation systems control a robot by implementing a fuzzy logic controller (FLC). Fuzzy navigation systems are simpler to implement than other navigation systems because they can handle infinite navigation situations with a finite set of rules. Existing fuzzy navigation systems for path finding in an unknown environment tend to find the shortest path in convex obstacle fields, but fail when obstacles are concave or placed continuously in certain configurations. This paper presents a fuzzy navigation system that can escape from concave and maze-like obstacle fields in an unknown environment. The system combines a tangent algorithm for path planning with sets of linguistic fuzzy control rules. In particular, we introduce the control rules for a Tracking mode of the FLC and improvements to the commonly used, intuitively reasonable tangent algorithm.

Carsharing in a University Community: Assessing Potential Demand and Distinct Market Characteristics

As of September 2007, more than 70 colleges and universities in the United States have partnered with carsharing organizations, and this market segment is expected to continue growing. To maximize the benefits of these partnerships, it is important to understand both the unique features of academic institutions as markets for carsharing and ways to predict university-based demand for carsharing services. A study was done to estimate the potential carsharing market at the University of Wisconsin–Madison by (a) using a stated preference survey to collect information on university affiliates’ transportation habits and carsharing preferences, (b) developing a set of probabilistic models of willingness to join a carsharing program based on the stated preference survey data, and (c) applying these models to predict the potential market share under different conditions. Through this process, the relative impact of respondents’ socioeconomic characteristics, current travel habits, attitudes on transportation and the environment, and familiarity with carsharing on their decisions to use carsharing were examined. The results show that a respondents status at the university (e.g., faculty, student, or staff) had a strong influence over her individual acceptance of car-sharing, even more so than socioeconomic variables such as income or vehicle ownership, and that peoples attitudes play an important role in their decision making. Furthermore, the ease of accessing a car is also a critical factor. Although the University of Wisconsin–Madison population was the focus of the analysis, the findings provide useful insights for targeting carsharing programs in other university communities.

Emissions and Air Quality Impacts of Truck-to-Rail Freight Modal Shifts in the Midwestern United States

We present an examination of the potential emissions and air quality benefits of shifting freight from truck to rail in the upper Midwestern United States. Using a novel, freight-specific emissions inventory (the Wisconsin Inventory of Freight Emissions, WIFE) and a three-dimensional Eulerian photochemical transport model (the Community Multiscale Air Quality Model, CMAQ), we quantify how specific freight mode choices impact ambient air pollution concentrations. Using WIFE, we developed two modal shift scenarios: one focusing on intraregional freight movements within the Midwest and a second on through-freight movements through the region. Freight truck and rail emissions inventories for each scenario were gridded to a 12 km × 12 km horizontal resolution as input to CMAQ, along with emissions from all other major sectors, and three-dimensional time-varying meteorology from the Weather Research and Forecasting model (WRF). The through-freight scenario reduced monthly mean (January and July) localized concentrations of nitrogen dioxide (NO2) by 28% (-2.33 ppbV) in highway grid cells, and reduced elemental carbon (EC) by 16% (-0.05 μg/m(3)) in highway grid cells. There were corresponding localized increases in railway grid cells of 25% (+0.83 ppbV) for NO2, and 22% (+0.05 μg/m(3)) for EC. The through-freight scenario reduced CO2 emissions 31% compared to baseline trucking. The through-freight scenario yields a July mean change in ground-level ambient PM2.5 and O3 over the central and eastern part of the domain (up to -3%).

Performance Measures for Winter Operations

New winter maintenance vehicles are being equipped with differential Global Positioning System (DGPS) receivers and numerous sensors that collect environmental data (e.g., pavement and air temperature), equipment status data (e.g., plow up, plow down), and material usage data (e.g., salt application rate). These data can be both telemetered to a dispatch center and recorded on magnetic media for later downloading. Data are transmitted and recorded as often as every 2 s. Such data, both type and quantity, have only recently become available. Vehicles were instrumented with the reasonable expectation that information can be used to improve winter maintenance. As a result, these technology demonstration projects tend to be guided by loosely defined “try-and-see” data requirements. Now, with the availability of these data, agencies are beginning to explore the possibilities for improving the performance of winter maintenance operations. A comprehensive set of performance measures for winter maintenance that can be computed from data collected by DGPS receivers and sensors on winter maintenance vehicles is described. The performance measures are indicators of how well winter maintenance operations meet and satisfy expectations. Consideration of the business goals and objectives determined during a series of meetings with state transportation agency professionals from all levels, including winter operations engineers, county commissioners, patrol supervisors, and program managers, led directly to identification of the performance measures. Consequently, the measures directly tie to the business processes and performance of operations. After baseline values of the measures are established, changes in performance can be related to cost of the technology.

A Statistical Analysis of the Role of Benefit–Cost Analysis in Awarding TIGER Grants

As directed by the American Recovery and Reinvestment Act of 2009, the U.S. Department of Transportation (DOT) created the Transportation Investment Generating Economic Recovery (TIGER) discretionary grant program for surface transportation infrastructure projects. TIGER used a multistep competitive application process to award surface transportation funds. TIGER applications were initially screened by U.S. DOT’s staff of technical and economic experts and the final awardees were selected by a Review Team of Modal Administrators and DOT Office of the secretary-level officials. The purpose of the research was to determine if the most deserving projects, based on an applicant’s benefit–cost analysis and the likelihood that benefits exceeded costs, were more likely to receive grant funding. We base the findings on pair-wise comparisons and on logistic regression models. Based on these analyses, we found that the outcome of the benefit–cost analysis (both quality and expected net benefits) was not a statistically significant factor.

Regression Tree Models to Predict Winter Storm Costs

Winter maintenance can consume one-third or more of highway maintenance budgets. Tools for estimating winter maintenance costs can enhance allocation, accountability, and management of expenditures. Historical weather forecasts and associated maintenance resources are used to create statistical models to estimate county-level resources to fight a forecast snow or freezing rain event. County-level analysis allows for model refinement for slightly different business practices and areas small enough to assume uniform weather effects. The statistical models are organized as regression trees that accommodate variables for operation of winter maintenance, such as service level expectations, range of county size, and weekend and overtime events. The regression trees fit subsets of data to form families of multiple linear models. In this way, models can be refined for important categorical variables such as service level and county size. The models presented here estimate labor, equipment, and material resources required to fight a storm in counties having 87 to 1,460 lane mi to maintain. The models estimate resources, not cost. Accordingly, the models are independent of unit costs of labor, equipment, and material that change over time and vary from county to county. Unit costs for labor, material, and equipment at each county are needed to convert resource estimates to resource costs. Statewide or regional storm costs can be computed by summing county-level costs.

ESTABLISHING MR&R COSTS FOR A NETWORK-LEVEL BRIDGE MANAGEMENT SYSTEM

The development of standardized maintenance, repair, and rehabilitation (MR&R) cost data for a network-level bridge management system (BMS) is presented. The Pontis BMS requires several data inputs, including the agency’s costs to perform bridge maintenance actions. Most states that are implementing Pontis do not have historical cost data standardized according to MR&R action, element, and condition state as required. The development of a questionnaire for collecting MR&R costs is described. The questionnaire was used to collect the estimated costs of MR&R actions on 25 bridge elements. Standard definitions of the physical characteristics for the elements were developed. Direct and indirect factors that influence MR&R costs were identified and organized into scenarios corresponding to the extreme minimum and maximum costs. The cost data collected by the questionnaire were analyzed. Procedures for assessing the variability of the estimated costs are presented. The coefficient of variation was used to measure the relative variability of estimated costs. One-way analysis of variance was used to assess the variation in estimated costs among the elements and MR&R actions. A multiple comparisons with the best analysis was used to identify certain elements and MR&R actions with greatest and least variations in cost. The results of this paper include recommended costs for the MR&R actions on 25 elements for the state of Wisconsin.

Relating Cost to Condition in Routine Highway Maintenance

When transportation agencies prepare a design for new highway construction or major improvements to existing highways, the life-cycle, agency, and user costs are considered in project design decisions. However, after highway projects are completed, maintenance budgets often do not keep pace with maintenance needs since they are rarely adjusted to accommodate the routine maintenance of new lane miles. Instead, maintenance budgets per lane mile remain relatively constant, regardless of increase in the number of vehicle miles traveled per highway mile. Thus, disparity increases between maintenance budgets and maintenance requirements, leading to difficult choices for maintenance priorities. Concerns about safety and mobility tend to trump preservation of capital investment. A study is presented of the relationship between maintenance cost and condition. With use of regression tree analysis, the study identified physical, environmental, operational, and socioeconomic parameters that influence maintenance costs for asphalt and concrete pavements, shoulders, litter pickup, vegetation control, and ditches. As a result, valid model equations will show just what kind of effect a certain investment has on the level of service. Other models will show how much investment is needed to get to a certain level of service. These models will provide information to maintenance managers about the trade-offs they will get based on the relationship of cost and maintenance, so they can make the best decision in allocating available funds.