This article provides a systematic review of the network formation literature in the public sector. In particular, we code and categorize the theoretical mechanisms used in empirical network research to motivate collaboration and tie formation. Based on a review of the 107 articles on network formation found in 40 journals of public administration and policy from 1998 to 2019, we identify 15 distinct theoretical categories. For each category, we describe the theory, highlight its use in the literature, and identify limitations and concerns with current applications. Overall, we find that most studies rely on a similar set of general theories of network formation. More importantly, we find that most theoretical mechanisms are not well specified, and empirical tests are often unable to directly assess the specific underlying mechanism. The results of our review highlight the need for our field to embrace experimental designs, develop panel network datasets, and engage in more network-level research.
FOREWORDThe work performed under this project included multiple research efforts designed to investigate the use of simulators in training commercial motor vehicle drivers. The topics addressed in this project include the effectiveness of entry-level training (including simulation-based training) for commercial licensing testing and the effectiveness of different training methods in terms of licensing skills test scores and driving records post-licensure. As part of this project, several simulated advanced scenarios (e.g., tanker trailer, tire blowout) are showcased as well. A cost analysis of different training methods was performed as part of this project, along with a case study of existing uses of commercial vehicle driving simulators. This report documents the method, results, and conclusions of these efforts. NOTICEThis document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The United States Government assumes no liability for its contents or the use thereof.The contents of this Report reflect the views of the contractor, who is responsible for the accuracy of the data presented herein. The contents do not necessarily reflect the official policy of the Department of Transportation.This Report does not constitute a standard, specification, or regulation.The United States Government does not endorse products or manufacturers named herein. Trade or manufacturers' names appear herein only because they are considered essential to the objective of this document. This study examined the effectiveness of a driving simulator for entry-level commercial motor vehicle (CMV) driver training and testing. Four training groups of 107 individuals (conventional 8-week certified course, conventional 8-week certified course with 60 percent of driving in a simulator, informal training with friends/relatives, and commercial's driver license [CDL] test-focused short courses) were followed from training into employment. There were no group differences in Division of Motor Vehicles (DMV) road tests. There were group differences in DMV range tests and validated real truck and simulator re-creations of DMV road and range tests. Conventional and simulator groups generally scored higher than informal and CDL test-focused participants. A 4-month follow-up after being hired as a CDL driver indicated no differences in performance, safety, self-or supervisory-ratings. Findings support the use of CMV driving simulator-based training, but simulator-based testing does not appear to be feasible at this point. Cost analysis indicated simulator training using the study simulator was $35/participant less expensive than conventional training. The simulator was examined in a demonstration of extreme conditions and emergency maneuvers under different vehicle configurations with 48 other drivers. Also provided is a case study of existing implementations of CMV simulator training, indicating benefits, drawbacks, and drivers' overall opinions.17.
Montelukast improves central and especially peripheral airways function in the first month of treatment, as evaluated by IOS, a technique based on tidal breathing analysis which is more sensitive than conventional forced spirometry.
Two microscopic simulation methods are compared for driver behavior: the Gazis–Herman–Rothery (GHR) car-following model and a proposed agent-based neural network model. To analyze individual driver characteristics, a back-propagation neural network is trained with car-following episodes from the data of one driver in the naturalistic driving database to establish action rules for a neural agent driver to follow under perceived traffic conditions during car-following episodes. The GHR car-following model is calibrated with the same data set, using a genetic algorithm. The car-following episodes are carefully extracted and selected for model calibration and training as well as validation of the calibration rules. Performances of the two models are compared, with the results showing that at less than 10-Hz data resolution the neural agent approach outperforms the GHR model significantly and captures individual driver behavior with 95% accuracy in driving trajectory.
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