Collision prediction models using multivariate Poisson-lognormal regression

El-Basyouny, Karim; Sayed, Tarek

doi:10.1016/j.aap.2009.04.005

Cited by 200 publications

(84 citation statements)

References 48 publications

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“…A second common approach is to use a mixing structure, in which one or more (typically) normally distributed random terms are introduced in the parameterization of the expected value of the discrete distribution (so that the expected value is not only a function of exogenous variables, but also includes one or more additive random terms within the exponentiation). If the same error term enters in the means of multiple count variables, this generates correlation (see Chib and Winkelmann, 2001, Lee et al, 2006, Park and Lord, 2007, Aguero-Valverde and Jovanis, 2009, and El-Basyouny and Sayed, 2009 for examples of such an approach). A similar, but slightly different mixing approach, has been used recently by Chiou and Fu (2012), who developed a multinomial-generalized Poisson model for the joint analysis of crash frequency and injury severity.…”

Section: Modeling Count Data By Typementioning

confidence: 99%

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

Narayanamoorthy

Paleti

Bhat

2013

Transportation Research Part B: Methodological

126

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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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Section: Modeling Count Data By Typementioning

confidence: 99%

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

Narayanamoorthy

Paleti

Bhat

2013

Transportation Research Part B: Methodological

126

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“…Several multivariate models have been employed such as multivariate spatial models (Song, 2004;Song et al, 2006), multivariate Poisson (MVP) models (Ma and Kockelman, 2006), and multivariate Poisson-lognormal (MVPLN) models (Park and Lord, 2007;Ma et al, 2008;Aguero-Valverde and Jovanis, 2009;El-Basyouny and Sayed, 2009). Compared to the univariate modelling approach, the multivariate models (i.e.…”

Section: Introductionmentioning

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

119

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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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“…Leaving out important explanatory variables can result in biased parameter estimates and incorrect inferences, especially if the omitted variable is correlated with variables included in the model, which is often the case [43] [44] [45] [46].…”

Section: Common Problems With Crash Datamentioning

confidence: 99%

Road Traffic Crash Data: An Overview on Sources, Problems, and Collection Methods

Abdulhafedh¹

2017

JTTs

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Road traffic crash data are useful tools to support the development, implementation, and assessment of highway safety programs that tend to reduce road traffic crashes. Collecting road traffic crash data aims at gaining a better understanding of road traffic operational problems, locating hazardous road sections, identifying risk factors, developing accurate diagnosis and remedial measures, and evaluating the effectiveness of road safety programs. Furthermore, they can be used by many agencies and businesses such as: law enforcements to identify persons at fault in road traffic crashes; insurers seeking facts about traffic crash claims; road safety researchers to access traffic crash reliable database; decision makers to develop long-term, statewide strategic plans for traffic and highway safety; and highway safety administrators to help educate the public. Given the practical importance of vehicle crash data, this paper presents an overview of the sources, trends and problems associated with road traffic crash data.

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Collision prediction models using multivariate Poisson-lognormal regression

Cited by 200 publications

References 48 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

Road Traffic Crash Data: An Overview on Sources, Problems, and Collection Methods

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