Bayesian ranking of sites for engineering safety improvements: Decision parameter, treatability concept, statistical criterion, and spatial dependence

Miaou, Shaw-Pin; Song, Joon Jin

doi:10.1016/j.aap.2005.03.012

Cited by 203 publications

(133 citation statements)

References 17 publications

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“…Overall, earlier efforts in the safety literature that incorporate spatial dependence focus almost exclusively on total counts (Miaou and Song, 2005 is the only exception).…”

Section: Spatial Dependency Effectsmentioning

confidence: 99%

“…The resulting model is estimated using Bayesian hierarchical methods. Examples of such efforts include Miaou and Song, 2005, Aguero-Valverde and Jovanis, 2006, 2010, Mitra, 2009, Wang et al, 2011, Siddiqui et al, 2012. Unfortunately, this approach (which is essentially a mixing approach of the type discussed in the previous section, except with the mixing undertaken over space) can be difficult as the number of spatial units increases, and extending the approach to modeling crashes by type is extremely challenging (if not impractical).…”

Section: Spatial Dependency Effectsmentioning

confidence: 99%

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On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level

Narayanamoorthy

Paleti

Bhat

2013

Transportation Research Part B: Methodological

129

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In loving memory of my grandmother N. N. Rugmani AcknowledgmentsFirst and foremost, I must express appreciation to Dr. Chandra Bhat, with whom it has been a pleasure and a privilege to work. I thank him for his insights and advice as also for the stimulus and motivation to dig deep into research. I am grateful to him for showing me how to make sense of mathematical models and for instilling in me the notion that writing should be simple, direct, logical, and tight. His invaluable ideas and the high standards that he sets for himself and his students form the backbone of

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“…Overall, earlier efforts in the safety literature that incorporate spatial dependence focus almost exclusively on total counts (Miaou and Song, 2005 is the only exception).…”

Section: Spatial Dependency Effectsmentioning

confidence: 99%

Section: Spatial Dependency Effectsmentioning

confidence: 99%

On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level

Narayanamoorthy

Paleti

Bhat

2013

Transportation Research Part B: Methodological

129

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“…After obtaining the expected number of accidents at each severity level, road segments can then be ranked by an appropriate decision parameter (Θ) for further engineering examination and treatment. The choice of decision parameter (Θ) depends on the context under which the rank is to be used, especially the range of safety treatments to be implemented (Miaou and Song, 2005). Therefore, inputs from decision makers can be useful, for their interests can be taken into consideration for ranking.…”

Section: Predicting Accident Frequency At Different Severity Levels Amentioning

confidence: 99%

“…The full Bayesian method has also been used in site ranking (e.g. Miaou and Song, 2005) and it has been shown to outperform the EB method (Huang et al, 2009). This paper adopts full Bayesian spatial models that controls for spatial correlation.…”

Section: Accident Frequency Modelmentioning

confidence: 99%

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model

Wang

Quddus

Ison

2011

Accident Analysis & Prevention

121

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Citation: WANG, C., QUDDUS, M.A. and ISON, S.G., Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model. Accident Analysis and Prevention, 43(6), pp.1979-1990 Metadata AbstractAccident prediction models (APMs) have been extensively used in site ranking with the objective of identifying accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poissonlognormal) for modelling the number of accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate accident frequency at different severity levels, namely the two-stage mixed multivariate model which combines both accident frequency and severity models. The accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for accident frequency and severity analysis respectively, and the results combined to produce estimation of the number of accidents at different severity levels. Based on the results from the two-stage model, the accident hotspots on the M25 and surround have been identified. The ranking result using the twostage model has also been compared with other ranking methods, such as the naïve ranking method, multivariate Poisson-lognormal and fixed proportion method. Compared to the traditional frequency based analysis, the two-stage model has the advantage in that it utilises more detailed individual accident level data and is able to predict low frequency accidents (such as fatal accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting accident frequency according to their severity levels and site ranking.

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“…If required, the severity of the accident can be accounted for (e.g. [6], [7], [25] and [31]). Consequently, one tries to find an explanation why some locations are hot spots and others not (i.e.…”

Section: Introductionmentioning

confidence: 99%

Hot Spot Analysis: Improving a Local Indicator of Spatial Association for Application in Traffic Safety

Moons

Brijs

Wets

Computational Science and Its Applications – ICCSA 2008

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Abstract. As in most European countries, traffic safety has become top priority in the National Safety Plan in Belgium. The first phase in every safety analysis concerns the identification of the hazardous locations. In this respect, a local indicator of spatial association (Moran's I) is improved and applied to determine hot spots locations on highways in Limburg, a province in Belgium. However, the analysis is complicated by the fact that accident data have a very specific nature: they form a Poisson random process rather than a Gaussian random process and they are prone to sparseness. Therefore, the well-established indicator needs some adaptations and simulations are required to determine the underlying distribution of Moran's I. This paper emphasizes the importance of using a correct distribution and indicates what could go wrong otherwise e.g. at policy level.

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Bayesian ranking of sites for engineering safety improvements: Decision parameter, treatability concept, statistical criterion, and spatial dependence

Cited by 203 publications

References 17 publications

On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level

On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model

Hot Spot Analysis: Improving a Local Indicator of Spatial Association for Application in Traffic Safety

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