Bruce A. Ralston

Investment Selection Model for Multicommodity Multimodal Transportation Networks

A Multicommodity Multimodal Network Design (MCMND) model is presented; the model is to be used as a planning tool for determining investment priorities for freight intercity networks. The MCMND model is designed to select the best set of investment options for a multimodal regional network, given a limited investment budget. The main component of the model comprises the solution of a nonlinear bilevel network design problem formulated to choose investments that minimize both the transportation costs incurred by users and the environmental impacts of less efficient modes of transportation. Investment options to be considered by the model involve the addition of new physical links to the network, the improvement of existing links, and the location of intermodal transfer facilities at specified nodes of the network. The representation of the multimodal network is at a level of detail appropriate for strategic planning for a large region. The demand for transportation services is fixed and exogenous to the mo...

GIS and ITS Traffic Assignment: Issues in Dynamic User-Optimal Assignments

Dynamic traffic assignment (DTA) is at the heart of much ITS research. Assigning traffic, whether for planning purposes or for real time route guidance, is a difficult problem. Recent advances in user-optimal dynamic traffic assignment have built on the methods developed for static user-optimal assignments. Since assignment models are complex, they will not use many of the network analysis functions found in commercial GIS packages. Custom software will have to be developed. In this paper we explore the problems faced in solving static and dynamic assignments and relate those problems to information that is likely to be based in a traffic control center’s GIS database. Because of the size of the problem and the need for faster-than-real-time analysis, how and when data is transferred between a GIS to analysis modules is important. Further, many approaches for GIS software design and spatial data handling, such as OOP and dynamic segmentation, may impose too much overhead to be of much use in time-sensitive ITS applications.

The Dynamics of Communication

Communication is studied here from a supply side point of view. That is, emphasis is placed on supplying information to the public, rather than on the public’s use and evaluation of information. The models used to study the communication channels, however, are based upon well known empirical regularities from the communication and diffusion of innovation literature. The main body of the paper begins with a description of, and the derivation of models for, differing communication channels. The characteristics of changes in agency’s optimal use of these channels are discussed, and accompanied by numerical examples. In the final two sections communication channels are incorporated into two spatial-temporal models. The former, diffusion through an urban system, is implicitly spatial, while the latter, random field models of diffusion, is an explicit spatial-temporal model.

Efficient generation of area thematic maps in KML

Thematic maps using KML and KMZ are common for point and line features, but scarce for area features. We present a method and website for generation of the thematic map KMLs based on US Census geography. By taking advantage of various preprocessing and indexing methods, thematic maps viewable in any KML compliant viewer (eg, Google Maps, Google Earth, or ArcGIS Explorer) can be generated very quickly. Our implementation covers counties, census tracts, and zip code tabulation areas for the entire United States and its territories and 31 socio-demographic variables.

Developing rich web GIS applications for visual analytics

The development of Rich Internet Application tools and web mapping services, coupled with the dissemination of detailed, up-to-date socioeconomic data, present an opportunity to develop intuitive geospatial visual analytic tools. This paper presents our efforts at developing such tools that combine Google Maps, Flex, and data from the American Community Survey.

Pre-evacuation Trip Behavior

Evacuation models, which seek to calculate evacuation time estimates (ETEs) and evaluate evacuation strategies, are based on assumptions concerning human behavior. One of the most consistent assumptions is that once an evacuation order is given, people will proceed to an evacuation point. ETEs are further assumed to be a function of network congestion along arcs. We test these assumptions through a survey of residents of Key West, Florida who experienced evacuation orders associated with Hurricane Wilma in 2005. Of particular interest are the trips residents made between the time the evacuation order was given and the deadline for evacuation, what we call pre-evacuation trips. Such trips were made by people who evacuated as well as by those who waited out the storm. That is, traffic on the transportation network is not solely limited to people seeking to evacuate. Survey data indicate three important aspects of pre-evacuation trip making behavior that run counter to common evacuation modeling assumptions. First, trip delays at stops are longer than delays on links. Second, trip delays are associated with trip purposes, which often are not to evacuate an area, and there is evidence of trip chaining. Finally, patterns of pre-evacuation trips show a dispersal of origins and destinations resulting in pre-evacuation trips that flow in many directions, not just toward evacuation points.

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.