Spatial and temporal aspects of prior and likelihood data choices for Bayesian models in road traffic safety analyses

Abstract

Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl IntroductionTraffic road safety, as an element of a human-vehicle-road system, has been the subject of scientific and research works for many years. There are many researchers and specialists in a wide range of fields or disciplines who are involved in the process of recognizing and understanding mechanisms related to a road crash. Many theories and models have been elaborated in order to evaluate the level of road traffic threats, as well as to identify circumstances, and cause and effect relationships of road accidents. The research area is extensive and covers: simulation and behavioural research (e.g. [8,9]), elaboration of entropy models (e.g. [1,12]), investigations of road polygons including road surroundings, and traffic and weather conditions (speed in particular) (e.g. [3,10]), as well as exploration and mining of real road accident data (e.g. [15,19]).Statistical methods belong to the most important research techniques utilised in analysing real data. There are two approaches in such an analysis. The first one is a frequentist (also known as classical) approach, in which a random event's probability is assumed to be represented by the frequency of the event occurrence in a very large number of identical samples. The other one is a Bayesian (also known as non-classical) approach, according to which a prior (unconditional) probability of a random event is a measure of a rational belief that the event will occur. Then, the belief is modified using data from experiments or from observations of circumstances connected with the event. Prior knowledge is transformed into posterior NowAkowskA M. spatial and temporal aspects of prior and likelihood data choices for Bayesian models in road traffic safety analyses. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2017; 19 (1): 68-75, http://dx.doi.org/10.17531/ein.2017 knowledge, which is a resultant probability and a measure of a rational expectation of the event occurrence after getting information from the data. Bayesian thinking, supported by the development of numerical sampling techniques, has created modern statistics fundamentals, which enables formulating and solving problems not available in classical statistics. Bayesian regression modelling is a non-classical methodology which becomes widespread in road traffic safety analyses, mainly because it allows eliminating various weaknesses of classical models. Bayesian regression models are difficult from both conceptual and computational points of view. Nevertheless, they bring a new quality to the development of scientific research methods, and they enable a flexible, though non-standard, approach to modelling issues. The models are used in order to develop safety performance functions (e.g. [6,7,13,16]), including a before-after analysis (e.g. [17]), and also to classify descriptive road accident features, such as driver's behaviour, accident type, or accid...

Application of the logistic regression for determining transition probability matrix of operating states in the transport systems

“…In the literature, logistic regression is used in medicine [5,44], in computed tomography [46], to identify technical systems [25], in the area of corporate finance [39,54], banking [1,34] broadly understood investments [12,29] and is used to assess the level of risk [2,8,45], in the social and demographic research [4,41] and others [25,40]. Regarding transport systems, logistic regression models are proposed primarily for assessing the road dangers demonstrated by the road accidents [18,37], making the choices of routs in the transport network [32,49] or analyzing the impact of selected factors on the implementation of transport processes [38,48].…”