Van crashworthiness and aggressivity study, test report 3: accident investigation studies. Final report.

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

Multidisciplinary accident investigation reports. Final report.

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

Status report of accident investigation data, motorcycle accident cause factors and identification of countermeasures. Preliminary report.

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

Multidisciplinary accident investigation - school bus locomotive-caboose collision, Lafayette, Oregon, September 8, 1976. MDAI team report.

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

Summary of 1968-70 multidisciplinary accident investigation reports: Volume 2 of 2.

Mode of access: Internet.

MMF - final report 1971. Multidisciplinary accident investigation.

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

Multidisciplinary accident investigation. Final report III.

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

Accident investigation technician instructor training institute. Volume 1. Final report.

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

Design for the evaluation of FMVSS 208 using police bi-level accident investigation. Final report.

National Highway Traffic Safety Administration, National Center for Statistics and Analysis, Washington, D.C.

Recreational vehicle accident investigation study. Volume I. Final report.

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

Accident investigation : group discussion exercise, photography.

Shipping list no.: 87-661-P.

Tracking rail safety : incident and accident investigation in Australia

Scoping Project: Currently no national or structured learning framework available in Aus or NZ Current Project: Develop a national training program & capability framework for rail incident investigators - Establish the potential market demand - Define the curricula for a multi-level national training program - Explore training providers & delivery options

Modeling random effect and excess zeros in road traffic accident prediction

Poisson distribution has often been used for count like accident data. Negative Binomial (NB) distribution has been adopted in the count data to take care of the over-dispersion problem. However, Poisson and NB distributions are incapable of taking into account some unobserved heterogeneities due to spatial and temporal effects of accident data. To overcome this problem, Random Effect models have been developed. Again another challenge with existing traffic accident prediction models is the distribution of excess zero accident observations in some accident data. Although Zero-Inflated Poisson (ZIP) model is capable of handling the dual-state system in accident data with excess zero observations, it does not accommodate the within-location correlation and between-location correlation heterogeneities which are the basic motivations for the need of the Random Effect models. This paper proposes an effective way of fitting ZIP model with location specific random effects and for model calibration and assessment the Bayesian analysis is recommended.

The airEXODUS evacuation model and its application to evacuation certification, crew training and accident investigation.

Computer based mathematical models describing the aircraft evacuation process have a vital role to play in the design and development of safer aircraft, in the implementation of safer and more rigorous certification criteria, cabin crew training and in post mortuum accident investigation. As the risk of personal injury and costs involved in performing large-scale evacuation experiments for the next generation 'Ultra High Capacity Aircraft' (UHCA) are expected to be high, the development and use of these evacuation modelling tools may become essential if these aircraft are to prove a viable reality. In this paper the capabilities and limitations of the airEXODUS evacuation model are described. Its successful application to the prediction of a recent certification trial, prior to the actual trial taking place, is described. Also described is a newly defined parameter known as OPS which can be used as a measure of evacuation trial optimality. In addition, sample evacuation simulations in the presence of fire atmospheres are described. Finally, the data requiremnets of the airEXODUS evacuation model is discussed along with several projects currently underway at the the Univesity of Greenwich designed to obtain this data. Included in this discussion is a description of the AASK - Aircraft Accident Statistics and Knowledge - data base which contains detailed information from aircraft accident survivors.

The design and implementation of a traffic accident analysis system

We designed and implemented a traffic accident analysis system (TAAS) in the paper. TAAS is the system faced traffic accident analysis, which uses the traffic rules (law) as knowledge sources to judge if the driver is responsible for a traffic accident. TAAS has characteristics of separating knowledge base and inference engine, using production rule and backward chaining. Besides, TAAS used predefined text and tracing program to realize explanation mechanism.