Annual average daily traffic (AADT) data are important for various transportation research areas, including travel model calibration and validation, pavement design, roadway design, and air quality compliance. Specifically for model calibration and validation in long-range transportation planning, a base-year model requires numerous count locations across the study region. Sometimes count data for the lower classified roadways are not readily available. Detailed models require traffic counts for not only higher classifications of roadways such as freeways and arterials but also collector and, in some instances, local roadways. To predict AADT better for desired count locations on nonfreeway facilities, spatial dependency is considered. The theory behind the use of spatial dependency is that the traffic volume at one monitoring station is correlated with the volumes at neighboring stations. The spatial regression model takes into account both spatial trend (mean) and spatial correlation, which is modeled by a geostatistical approach called kriging. The spatial regression model is applied to AADT in Wake County, North Carolina. Results indicate that the overall predictive capability of the spatial regression model is much better than that of the ordinary regression model. In addition, the urban area has more reliable prediction than the rural area. Finally, the spatial regression model is expected to provide better predictions for desired count locations where no observed data currently exists due to budget limitations.
This paper explores the use of passively collected data on the location of mobile phones in the development of external travel models, which capture trips to, from, and through an area. The data were collected 24 h a day for 1 month during May 2013 for the North Carolina Department of Transportation. The data cover the French Broad River Metropolitan Planning Organization and surrounding counties in North Carolina. This paper details the format of the data collected and the required processing and cleaning to support the development of the external trip models. The process for developing a through-trip table and estimating external trip interchanges in the region is presented, along with validation techniques for both. The results of this effort show that passively collected mobile phone data can be a good source of local information for developing external trip models.
Background:In four-step travel demand models, average trip generation rates are traditionally applied to static household type definitions. In reality, however, trip generation is more heterogeneous with some households making no trips and other households making more than a dozen trips, even if they are of the same household type. Objective:This paper aims at improving trip-generation methods without jumping all the way to an activity-based model, which is a very costly form of modeling travel demand both in terms of development and computer processing time. Method:Two fundamental improvements in trip generation are presented in this paper. First, the definition of household types, which traditionally is based on professional judgment rather than science, is revised to optimally reflect trip generation differences between the household types. For this purpose, over 67 million definitions of household types were analyzed econometrically in a Big-Data exercise. Secondly, a microscopic trip generation module was developed that specifies trip generation individually for every household. Results:This new module allows representing the heterogeneity in trip generation found in reality, with the ability to maintain all household attributes for subsequent models. Even though the following steps in a trip-based model used in this research remained unchanged, the model was improved by using microscopic trip generation. Mode-specific constants were reduced by 9%, and the Root Mean Square Error of the assignment validation improved by 7%.
This paper discusses the link performance functions used in travel demand models with a focus on the strengths and weaknesses of the most commonly used volume–delay functions. These include the Bureau of Public Roads function, the conical delay function, Highway Capacity Manual procedures, and the Akcelik function. Improvements to the volume–delay functions used in travel demand models are of particular importance in light of the increased emphasis on reliable speed outputs to support air quality initiatives, improved accessibility measures for various submodels, and the desire to evaluate a broader range of policy issues. One of the key challenges that analysts face in the development of locally calibrated volume–delay functions is how best to represent the regime in which the volume—or, more aptly stated, the demand—exceeds capacity, a regime that cannot be directly observed, even though it is required for highway assignment. This paper explores the use of freeway detector data along with bottleneck and queue analysis as a relatively straightforward approach for estimating demand beyond capacity for fitting locally calibrated volume–delay functions. The results of this study show that bottleneck analysis and queue length estimation are effective means of accomplishing this goal, providing a valuable tool for improving models with locally collected data.
Numerous unconventional intersection and interchange designs (UIIDs) exist with documented benefits, but implementation is slow. There are public, professional, and political barriers. Numerous papers explain the benefits of one design or compare designs, but few identify common barriers. This research surveyed transportation professionals to identify the barriers inhibiting the implementation of promising UIIDs and to identify solutions. Engineers believe that the public's main concerns are driver confusion and fear of the unknown. Survey results highlight education as important, and that public opinion generally improves once an UIID is constructed. Professionals and politicians want proof that a design will work and are reluctant to try nonstandard designs. An important political consideration is cost, but the life cycle cost can be competitive. We need guidelines to assist designers, increased focus on alternatives analysis, and inclusion of UIIDs in planning. This will make the designs more familiar to the public and decrease opposition.
Travel demand models are valuable tools in the transportation planning process; based on sound theory, these models bring a quantitative element to what is predominantly a political process. The forecasts output from these models guide decision makers in evaluating and selecting transportation programs and projects. Developing a better understanding of the factors that influence travel behavior, the changes in travel behavior over time, and the variables that best capture these changes may lead to the development of models that are more stable over time, increase the analyst's confidence in model results and lead to more cost-effective investment decisions. This paper investigates the life cycle as one such class of variables. In this context, “life cycle” is defined as the stage a family is in at a given time as related to factors such as the number and age of adults in the household; the presence, number, and age of children; and worker status. Using various statistical tests to evaluate the usefulness of the life cycle, the paper presents evidence to indicate that the life cycle has a strong influence on trip-making behavior while also improving stability in trip rates over time. These findings suggest that advanced trip generation models that accommodate more independent variables may lead to improved models, are more temporally stable, and better capture the dynamics that influence trip making.
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