Bayesian methodology to estimate and update safety performance functions under limited data conditions: A sensitivity analysis

Heydari, Shahram; Miranda-Moreno, Luis F.; Lord, Dominique; Fu, Liping

doi:10.1016/j.aap.2013.11.001

Cited by 25 publications

(23 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lee (2011) made a 5 comprehensive comparison and concluded that the model of Leroux et al (1999) was most 6 appealing, as it performed consistently well in the presence of independence and strong 7 spatial correlation. 8 Although most safety analysts have made an effort to handle the spatially correlated 9 effects in model residuals, a limited number of studies have specifically focused on another 10 issue related to the location dimension of crash data, i.e., spatial heterogeneity or spatial 11 non-stationarity (Xu and Huang, 2015). Variables do not usually vary identically across space, 12 and the relationship between crashes and related risk factors may not necessarily be constant 13 or fixed across the study area.…”

Section: Introductionmentioning

confidence: 99%

“…In reality, spatial pooling with smoothly 6 varying coefficients over contiguous areas may be implausible, especially when clear 7 discontinuities exist (Congdon, 2014, p. 340). In this vein, a robust model with a mechanism to 8 accommodate the global and local smoothing collectively would be preferable. 9 This study intends to investigate the spatially varying relationships between crash 10 frequency and relevant risk factors using a fully Bayesian approach.…”

Section: Introductionmentioning

confidence: 99%

“…The model with 2 d R towards value of one fits better to 7 the data. 8 The MAD was adopted to provide a measure of model prediction performance:…”

mentioning

confidence: 99%

See 2 more Smart Citations

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

Huang

et al. 2017

Accident Analysis & Prevention

View full text Add to dashboard Cite

This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

Huang

et al. 2017

Accident Analysis & Prevention

View full text Add to dashboard Cite

show abstract

“…Therefore, research methods and analysis technologies are not generally applicable. Although some scholars used Decision Tree [26,32,33], Bayesian network [34][35][36], and other complex systems to research the correlation between accident causes, the theoretical systems and related supporting technologies have not been systematically established.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica

Zou

Yue

2017

Journal of Advanced Transportation

View full text Add to dashboard Cite

Based on an overall consideration of factors affecting road safety evaluations, the Bayesian network theory based on probability risk analysis was applied to the causation analysis of road accidents. By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian network structure was established by integrating K2 algorithm with experts' knowledge, and Expectation-Maximization algorithm that could process missing data was adopted to conduct the parameter learning in Netica, thereby establishing the Bayesian network model for the causation analysis of road accidents. Then Netica was used to carry out posterior probability reasoning, the most probable explanation, and inferential analysis. The results showed that the Bayesian network model could effectively explore the complex logical relation in road accidents and express the uncertain relation among related variables. The model not only can quantitatively predict the probability of an accident in certain road traffic condition but also can find the key reasons and the most unfavorable state combination which leads to the occurrence of an accident. The results of the study can provide theoretical support for urban road management authorities to thoroughly analyse the induction factors of road accidents and then establish basis in improving the safety performance of the urban road traffic system.

show abstract

“…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 accident severity (e.g.…”

mentioning

confidence: 99%

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

Nowakowska¹

2016

EiN

View full text Add to dashboard Cite

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...

show abstract

Bayesian methodology to estimate and update safety performance functions under limited data conditions: A sensitivity analysis

Cited by 25 publications

References 24 publications

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica

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

Contact Info

Product

Resources

About