Traffic safety demonstration program modeling system. Volume III: citizens band radio model. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Model police traffic services directives (policy, procedures, rules) for police executives. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

A manual of model police traffic services: policies, procedures, rules. Phase III - model police traffic services rules.

National Highway Traffic Safety Administration, Washington, D.C.

Model minimum performance specifications for police traffic radar devices.

Mode of access: Internet.

User's manual: FHWA highway traffic noise prediction model - SNAP 1.0. Final report.

Federal Highway Administration, Environmental Design and Control Division, Washington, D.C.

NETGRAF: a computer graphics aid to the operation and interpretation of NETSIM, a traffic simulation model. Part 1: overview and experimental results. Final report.

Transportation Department, Office of University Research, Washington, D.C.

NETGRAF: a computer graphics aid to the operation and interpretation of NETSIM, a traffic simulation model. Part 3: programmer's guide.

Transportation Department, Office of University Research, Washington, D.C.

User's manual: FHWA Level 2 highway traffic noise prediction model - STAMINA 1.0. Final report.

Federal Highway Administration, Environmental Design and Control Division, Washington, D.C.

A new continuum model for traffic flow /

"Final report under contract DOT-RD-82018."

User's manual, FHWA level 2 highway traffic noise prediction model, stamina 1.0 /

Mode of access: Internet.

Simulation of air traffic controllers' behaviour using the operator choice model

The Operator Choice Model (OCM) was developed to model the behaviour of operators attending to complex tasks involving interdependent concurrent activities, such as in Air Traffic Control (ATC). The purpose of the OCM is to provide a flexible framework for modelling and simulation that can be used for quantitative analyses in human reliability assessment, comparison between human computer interaction (HCI) designs, and analysis of operator workload. The OCM virtual operator is essentially a cycle of four processes: Scan Classify Decide Action Perform Action. Once a cycle is complete, the operator will return to the Scan process. It is also possible to truncate a cycle and return to Scan after each of the processes. These processes are described using Continuous Time Probabilistic Automata (CTPA). The details of the probability and timing models are specific to the domain of application, and need to be specified using domain experts. We are building an application of the OCM for use in ATC. In order to develop a realistic model we are calibrating the probability and timing models that comprise each process using experimental data from a series of experiments conducted with student subjects. These experiments have identified the factors that influence perception and decision making in simplified conflict detection and resolution tasks. This paper presents an application of the OCM approach to a simple ATC conflict detection experiment. The aim is to calibrate the OCM so that its behaviour resembles that of the experimental subjects when it is challenged with the same task. Its behaviour should also interpolate when challenged with scenarios similar to those used to calibrate it. The approach illustrated here uses logistic regression to model the classifications made by the subjects. This model is fitted to the calibration data, and provides an extrapolation to classifications in scenarios outside of the calibration data. A simple strategy is used to calibrate the timing component of the model, and the results for reaction times are compared between the OCM and the student subjects. While this approach to timing does not capture the full complexity of the reaction time distribution seen in the data from the student subjects, the mean and the tail of the distributions are similar.

Fluctuation-driven traffic congestion in a scale-free model of the Internet

In studies of complex heterogeneous networks, particularly of the Internet, significant attention was paid to analysing network failures caused by hardware faults or overload. There network reaction was modelled as rerouting of traffic away from failed or congested elements. Here we model network reaction to congestion on much shorter time scales when the input traffic rate through congested routes is reduced. As an example we consider the Internet where local mismatch between demand and capacity results in traffic losses. We describe the onset of congestion as a phase transition characterised by strong, albeit relatively short-lived, fluctuations of losses caused by noise in input traffic and exacerbated by the heterogeneous nature of the network manifested in a power-law load distribution. The fluctuations may result in the network strongly overreacting to the first signs of congestion by significantly reducing input traffic along the communication paths where congestion is utterly negligible. © 2013 IEEE.