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Microscopic traffic simulation models require the calibration of car-following (or vehicle-following) models. The parameters of vehicle-following models control individual driver’s spacing, time gap, speed variation and acceleration during different driving conditions. In recent studies, these parameters have been determined for different vehicle classes separately since heavy vehicles generally keep longer spacing and time gap than light vehicles. These parameters have been commonly estimated based on the observed macroscopic traffic flow data such as average volume and speed. However, these data cannot reflect actual vehicle-following behavior of individual vehicles. Also, the effect of the lead vehicle class on the following vehicle’s behavior has been neglected in the parameter estimation. Thus, this study estimates the driving behavior parameters for cars and heavy vehicles in the Wiedemann 99 vehicle-following model. For the estimation, 2169 vehicle trajectories were obtained from a 640-m segment of US-101 in Los Angeles, California during the morning peak hours. Separate parameters were estimated for three vehicle classes (cars, heavy vehicles, and motorcycles) and three vehicle-following cases (car following car, car following heavy vehicle, and heavy vehicle following car). From the comparison of the driving behavior parameters between cars and heavy vehicles, it was found that heavy vehicles keep longer spacing and time gap with the lead vehicle, are less sensitive to the lead car behavior, and apply smaller acceleration when they start from stationary position compared to cars. It was also found that the parameters significantly varied across different vehicle pairs even for the same vehicle class and the same vehicle-following case. The estimated parameters were also validated as the VISSIM simulation with the estimated parameters better reflected the observed cumulative average speed and acceleration distributions than the simulation with the default parameters. The results indicate that differences in the parameters among different vehicle-following cases and the variability of parameters for different vehicle pairs must be considered in the fixed parameters for each vehicle class currently used in the Wiedemann’s model. [ABSTRACT FROM AUTHOR]

Building on the literature related to evidence-based practice, knowledge management, and learning organizations, this cross-case analysis presents twelve works-in-progress in ten local public human service organizations seeking to develop their own knowledge sharing systems. The data for this cross-case analysis can be found in the various contributions to this Special Issue. The findings feature the developmental aspects of building a learning organization that include knowledge sharing systems featuring transparency, self-assessment, and dissemination and utilization. Implications for practice focus on the structure and processes involved in building knowledge sharing teams inside public human service organizations. [ABSTRACT FROM AUTHOR]

Abstract: This study presents a real-time crash prediction model and uses this model to investigate the effect of the local traffic-responsive ramp metering strategy on freeway safety. Safety benefits of ramp metering are quantified in terms of the reduced crash potential estimated by the real-time crash prediction model. Driver responses to ramp metering and the consequent traffic flow changes were observed using a microscopic traffic simulation model and crash potential was estimated for a 14.8km section of I-880 in Hayward, California and a hypothetical isolated on-ramp network. The results showed that ramp metering reduced crash potential by 5–37% compared to the no-control case. It was found that safety benefits of local ramp metering strategy were only restricted to the freeway sections in the vicinity of the ramp, and were highly dependent on the existing traffic conditions and the spatial extent over which the evaluation was conducted. The results provide some insight into how a local ramp metering strategy can be modified to improve safety (by reducing total crash potential) on longer stretch of freeways over a wide range of traffic conditions. [Copyright &y& Elsevier]

One of the biggest challenges facing human service organizations is the proliferation of information from inside and outside the agency that needs to be managed if it is to be of use. The concepts of tacit and explicit knowledge can inform an approach to this challenge. Tacit knowledge is stored in the minds of practitioners (often called practice wisdom) and the explicit knowledge is often found in organizational procedure manuals and educational and training materials. Building on this perspective, this analysis provides a preliminary definition of research-minded practitioners by explicating the elements of curiosity, critical reflection, and critical thinking. The organizational implications of developing a cadre of research-minded practitioners include the commitment of top management to support "link officers", evidence request services, research and development units, and service standards. The challenges include the capacity to identify/support research-minded practitioners, promote an organizational culture of evidence-informed practice, redefine staff development and training, redefine job descriptions, and specify the nature of managerial leadership., (Copyright © Taylor & Francis Group, LLC)