Comparative evaluation of microscopic car-following behavior

Microscopic traffic-simulation tools are increasingly being applied to evaluate the impacts of a wide variety of intelligent transport, systems (ITS) applications and other dynamic problems that are difficult to solve using traditional analytical models. The accuracy of a traffic-simulation system depends highly on the quality of the traffic-flow model at its core, with the two main critical components being the car-following and lane-changing models. This paper presents findings from a comparative evaluation of car-following behavior in a number of traffic simulators [advanced interactive microscopic simulator for urban and nonurban networks (AIMSUN), parallel microscopic simulation (PARAMICS), and Verkehr in Statiten-simulation (VISSIM)]. The car-following algorithms used in these simulators have been developed from a variety of theoretical backgrounds and are reported to have been calibrated on a number of different data sets. Very few independent studies have attempted to evaluate the performance of the underlying algorithms based on the same data set. The results reported in this study are based on a car-following experiment that used instrumented vehicles to record the speed and relative distance between follower and leader vehicles on a one-lane road. The experiment was replicated in each tool and the simulated car-following behavior was compared to the field data using a number of error tests. The results showed lower error values for the Gipps-based models implemented in AIMSUN and similar error values for the psychophysical spacing models used in VISSIM and PARAMICS. A qualitative drift and goal-seeking behavior test, which essentially shows how the distance headway between leader and follower vehicles should oscillate around a stable distance, also confirmed the findings.

Community-based programs to promote care seat restraints in children 0-16 years - A systematic review

Study Objective: Community-based models for injury prevention have become an accepted part of the overall injury control strategy. This systematic review of the scientific literature examines the evidence for their effectiveness in reducing injury due to inadequate car seat restraint use in children 0-16 years of age. Methods: A comprehensive search of the literature was performed using the following study selection criteria: community-based intervention study: target population was children aged 0-16 years of age; outcome measure was either injury rates due to motor vehicle crashes or observed changes in child restraint use; and use of community control or historical control in the study design. Quality assessment and data abstraction was guided by a standardized procedure and performed independently by two authors. Data synthesis was in tabular and text form with meta-analysis not being possible due to the discrepancy in methods and measures between the studies. Results: This review found eight studies, that met all the inclusion criteria. In the studies that measured injury outcomes, significant reductions in risk of motor vehicle occupant injury (33-55%) were reported in the study communities. For those studies reporting observed car seat restraint use the community-based programs were successful in increasing toddler restraint use in 1-5 year aged children by up to 11%; child booster seat use in 4-8 year aged children by up to 13%; rear restraint use in children aged 0-15 years by 8%; a 50% increase in restraint use in pre-school aged children in a high-risk community; and a 44% increase in children aged 5-11 years. Conclusion: While this review highlights that there is some evidence to support the effectiveness of community-based programs to promote car restraint use and/or motor vehicle occupant injury, limitations in the evaluation methodologies of the studies requires the results to be interpreted with caution. There is clearly a need for further high quality program evaluation research to develop an evidence base. (C) 2004 Elsevier Ltd. All rights reserved.

A Reactive Agent-based Neural Network Car Following Model

This paper presented a novel approach to develop car following models using reactive agent techniques for mapping perceptions to actions. The results showed that the model outperformed the Gipps and Psychophysical family of car following models. The standing of this work is highlighted by its acceptance and publication in the proceedings of the International IEEE Conference on Intelligent Transportation Systems (ITS), which is now recognised as the premier international conference on ITS. The paper acceptance rate to this conference was 67 percent. The standing of this paper is also evidenced by its listing in international databases like Ei Inspec and IEEE Xplore. The paper is also listed in Google Scholar. Dr Dia co-authored this paper with his PhD student Sakda Panwai.