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The rich data on intelligent transportation systems (ITS) are a precious resource for transportation researchers and practitioners. However, the usability of this resource is greatly limited by missing data. Many imputation methods have been proposed in the past decade. However, some issues are still not addressed or are not sufficiently addressed, for example, the missing of entire records, temporal correlation in observations, natural characteristics in raw data, and unbiased estimates for missing values. This paper proposes an advanced imputation method based on recent development in other disciplines, especially applied statistics. The method uses a Bayesian network to learn from the raw data and a Markov chain Monte Carlo technique to sample from the probability distributions learned by the Bayesian network. It imputes the missing data multiple times and makes statistical inferences about the result. In addition, the method incorporates a time series model so that it allows data missing in entire rows–-an unfavorable missing pattern frequently seen in ITS data. Empirical study shows that the proposed method is robust and accurate. It is ideal for use as a high-quality imputation method for off-line application.

Modeling processes and model testing processes are discussed as parts of the model life cycle, and the tasks of these processes and their relations are highlighted. Of particular interest is the model validation process, which ensures that the model closely simulates what the real system does. A collection of validation techniques is presented to facilitate a systematic check of model performance from various perspectives. Under the qualitative category, a few graphical techniques are presented to help a visual examination of the differences between the simulation and the observation. Under the quantitative category, several statistical measures are discussed to quantify the goodness of fit; to achieve a higher level of confidence about model performance, a simultaneous statistical inference technique is proposed that tests both model accuracy and precision. As an illustrative example, these validation techniques are comprehensively applied to test an enhanced macroscopic simulation model, KWaves, in a systematic manner.

With the wide adoption of XML as a standard means of data representation and exchange, more XML-aware applications and open resources are added to the library. It is therefore beneficial for the transportation community to develop an industrywide common language not only to leverage the open resources but also to ease external and internal communication. A prototype of XML-based language, TrafficXML, was developed for data representation in traffic simulation, and the prototype was formally defined in an XML schema. Discussions include the role of XML in traffic simulation, procedures for developing and applying the prototype, and issues of validation with an XML schema. As an illustrative example, the prototype was applied to KWaves, an enhanced macroscopic traffic simulation software built on Java and XML technologies.

Police radar is known to have an effect on the speed of drivers. This effect derives from the presence of vehicles equipped with radar detectors in the traffic stream. The most common method for determining radar detector use is visual examination of the traffic stream. Other methods employ specially developed receivers, often called radar detector detectors. As a response to the development of such a radar detector detector, radar detector manufacturers inserted countermeasures in their designs with the objective of avoiding their detection. Presented is the Georgia Institute of Technology Research Institute radar detector detector, which was developed by using advanced surveillance technology and handles the countermeasures of current radar detectors. This system was used to determine radar detector densities at three sites (rural two-lane road, four-lane state route, and six-lane Interstate) around the Atlanta, Georgia, metro area. The data collected were analyzed and compared against commonly used statistical probability distributions. Common distributions were fitted to the data, whenever appropriate. The determined radar densities by site and time of day were compared by using a nonparametric analysis of variance test. This analysis revealed that facility type has a significant impact on radar detector density, whereas time of day showed a significant effect for only one of the sites (state route).

For the large array of self-peptide/MHC class II (pMHC-II) complexes displayed in the body, it is unclear whether CD4+ T cell tolerance must be imparted for each individual complex or whether pMHC-II–nonspecific bystander mechanisms are sufficient to confer tolerance by acting broadly on T cells reactive to multiple self-pMHC-II ligands. Here, via reconstitution of T cell–deficient mice, we demonstrate that altered T cell selection on a single prostate-specific self-pMHC-II ligand renders recipient mice susceptible to prostate-specific T cell infiltration. Mechanistically, this self-pMHC-II complex is required for directing antigen-specific cells into the Foxp3+ regulatory T cell lineage but does not induce clonal deletion to a measurable extent. Thus, our data demonstrate that polyclonal T reg cells are unable to functionally compensate for a breach in tolerance to a single self-pMHC-II complex in this setting, revealing vulnerabilities in antigen-nonspecific bystander mechanisms of immune tolerance.

New emissions models for predicting carbon monoxide, hydrocarbons, and oxides of nitrogen require as input not only average speed but various measures of dynamic vehicular activity such as accelerations, decelerations, and idle events as well. Current travel demand modeling of transportation networks does not provide estimates of dynamic vehicular activity but, instead, forecasts traffic volumes and travel speeds. Simulation models could provide estimates of dynamic vehicular activity, but simulation models are not used or validated for the development of regional emissions inventories. Until simulation models are used for regional planning purposes, improvements to travel demand models (TDMs) must be forthcoming if the benefits of new emissions models are to be realized. There are at least two solutions for bridging the gap between TDM outputs and new, data-intensive emissions models. The first solution is the development of statistical models that forecast dynamic vehicular activity as a function of TDM outputs: average traffic speeds and volumes. The second solution is the identification of mutually exclusive and collectively exhaustive regions of the speed-flow regime whereby representative dynamic driving sequences or cycles are characteristically different, particularly with respect to vehicular emissions. The present focus is on dynamic activity on freeways. A brief background of the research problem is first provided, along with stated research goals. The field study conducted to collect the necessary data on freeways is then described. The statistical task of identifying homogeneous regions of the speed-flow regime with respect to emissions activity is then discussed. Finally, the process by which typical dynamic driving activity is generated is given, followed by research conclusions and issues that require further study.

Simulation is often used to address issues that cannot be effectively resolved using the Highway Capacity Manual(HCM) or other analytical procedures. However, guidelines are not available for appropriate application of simulation as a complement or substitute for HCM procedures. The purpose of this paper is threefold: (a) to provide an overview of simulation models in a context consistent with facilities and situations addressed by the HCM, (b) to present a framework for the appropriate selection of simulation models when procedures of the HCM are not adequate, and (c) to provide brief examples demonstrating application of the proposed framework. The paper is divided into five sections. The first section presents simulation concepts and definitions. The second presents a suggested framework for application of simulation within an HCM context. The third and fourth sections apply this framework to the evaluation of uninterrupted- and interrupted-flow facilities, respectively. In these, appropriate application of the proposed framework for integration of simulation into an HCM-type analysis framework is presented. Last, the relationship between the methodological framework presented in this paper and the upcoming HCM 2000 is discussed.

An alternative approach to the estimation of the critical cornering speed of large combination vehicles is presented. Existing techniques are based on analytical models subject to a series of simplifying assumptions, and do not explicitly consider grades and other three‐dimensional roadway features of the real problem. One result of this study indicates that these simplified models may overestimate critical cornering speed by as much as 50%. The proposed technique combines detailed simulation of the dynamics of the motion of large combination vehicles through a three‐dimensional roadway, with a Monte Carlo sampling of possible vehicle configurations and component mechanical properties to produce databases for statistical analysis. This approach is applied to the study of critical cornering speed at freeway‐to‐freeway connectors. Databases for several samples of vehicles are generated, and a series of regression models that predict critical cornering speed is created. Results indicate that these regression models may be used to predict critical cornering speed to a high degree of accuracy. Finally, critical cornering speeds estimated using these models compare reasonably with actual speed data collected at five freeway connectors.

Presents a letter to the editor in response to an article about regulations imposed on sick notes, published in the January 25, 2005 issue of the periodical "Doctor."