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Purpose – Time-series regression models are applied to analyse transport safety data for three purposes: (1) to develop a relationship between transport accidents (or incidents) and various time-varying factors, with the aim of identifying the most important factors; (2) to develop a time-series accident model in forecasting future accidents for the given values of future time-varying factors and (3) to evaluate the impact of a system-wide policy, education or engineering intervention on accident counts. Regression models for analysing transport safety data are well established, especially in analysing cross-sectional and panel datasets. There is, however, a dearth of research relating to time-series regression models in the transport safety literature. The purpose of this chapter is to examine existing literature with the aim of identifying time-series regression models that have been employed in safety analysis in relation to wider applications. The aim is to identify time-series regression models that are applicable in analysing disaggregated accident counts.

Methodology/Approach – There are two main issues in modelling time-series accident counts: (1) a flexible approach in addressing serial autocorrelation inherent in time-series processes of accident counts and (2) the fact that the conditional distribution (conditioned on past observations and covariates) of accident counts follow a Poisson-type distribution. Various time-series regression models are explored to identify the models most suitable for analysing disaggregated time-series accident datasets. A recently developed time-series regression model – the generalised linear autoregressive and moving average (GLARMA) – has been identified as the best model to analyse safety data.

Findings – The GLARMA model was applied to a time-series dataset of airproxes (aircraft proximity) that indicate airspace safety in the United Kingdom. The aim was to evaluate the impact of an airspace intervention (i.e., the introduction of reduced vertical separation minima, RVSM) on airspace safety while controlling for other factors, such as air transport movements (ATMs) and seasonality. The results indicate that the GLARMA model is more appropriate than a generalised linear model (e.g., Poisson or Poisson-Gamma), and it has been found that the introduction of RVSM has reduced the airprox events by 15%. In addition, it was found that a 1% increase in ATMs within UK airspace would lead to a 1.83% increase in monthly airproxes in UK airspace.

Practical applications – The methodology developed in this chapter is applicable to many time-series processes of accident counts. The models recommended in this chapter could be used to identify different time-varying factors and to evaluate the effectiveness of various policy and engineering interventions on transport safety or similar data (e.g., crimes).

Originality/value of paper – The GLARMA model has not been properly explored in modelling time-series safety data. This new class of model has been applied to a dataset in evaluating the effectiveness of an intervention. The model recommended in this chapter would greatly benefit researchers and analysts working with time-series data.

Some aircraft runway collisions are described first to illustrate some common features of a runway incursion (RI) incident. Then, using the FAA definition of runway incursion, some data relating to RI incidents are presented to show that both RIs and incursion rates are growing steadily in the USA. Data for UK airport operations also indicate that the incursion rates in Britain are of the same order. Hence there is a need for some form of RI prevention. Most of the technological prevention systems available, or being developed, are briefly described before the paper concludes with the suggestion that effective prevention may be more swiftly obtained by improving airside signage and training for airport workers who use runways and taxiways in their vehicles.

Europe has adopted Flight Path 2050 (FP 2050) challenge with an objective of 90 per cent of the travelers being able to reach door-to-door European destinations within 4 hours by 2050. The aim can be achieved by reliable, well-organized small aircraft transport (SAT). Analysis of the currently operating small aircraft operational reliability data will support the development of future aircraft designs as well as reliability and safety requirements necessary for commercial operations.

The paper provides results of a statistical analysis of small aircraft current operations based on the reported events contained in the Database named European Coordination Centre for Aviation Incident Reporting Systems database. It presents identified safety indicators and focuses particularly on those related to the aviation technology.

It has been found that certain airframe and powerplant systems have the biggest influence on flight safety.

Multidisciplinary analysis of the operational and aircraft components reliability data will help in a proper preparation of the SAT supporting facilities, a design process of new aircraft and improvements of the existing airframe and powerplant systems.

Presented results are valuable for further developments of the statistical tools facilitating new product introduction.