Bayesian latent class safety performance function for identifying motor vehicle crash black spots
Afghari, Amir Pooyan, Haque, M. Mazharul, Washington, Simon, & Smyth, Tanya (2016) Bayesian latent class safety performance function for identifying motor vehicle crash black spots. Transportation Research Record, 2601, pp. 90-98.
The current state of the practice in Blackspot Identification (BSI) utilizes safety performance functions based on total crash counts to identify transport system sites with potentially high crash risk. This paper postulates that total crash count variation over a transport network is a result of multiple distinct crash generating processes including geometric characteristics of the road, spatial features of the surrounding environment, and driver behaviour factors. However, these multiple sources are ignored in current modelling methodologies in both trying to explain or predict crash frequencies across sites. Instead, current practice employs models that imply that a single underlying crash generating process exists. The model mis-specification may lead to correlating crashes with the incorrect sources of contributing factors (e.g. concluding a crash is predominately caused by a geometric feature when it is a behavioural issue), which may ultimately lead to inefficient use of public funds and misidentification of true blackspots.
This study aims to propose a latent class model consistent with a multiple crash process theory, and to investigate the influence this model has on correctly identifying crash blackspots. We first present the theoretical and corresponding methodological approach in which a Bayesian Latent Class (BLC) model is estimated assuming that crashes arise from two distinct risk generating processes including engineering and unobserved spatial factors. The Bayesian model is used to incorporate prior information about the contribution of each underlying process to the total crash count. The methodology is applied to the state-controlled roads in Queensland, Australia and the results are compared to an Empirical Bayesian Negative Binomial (EB-NB) model. A comparison of goodness of fit measures illustrates significantly improved performance of the proposed model compared to the NB model. The detection of blackspots was also improved when compared to the EB-NB model. In addition, modelling crashes as the result of two fundamentally separate underlying processes reveals more detailed information about unobserved crash causes.
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|Item Type:||Journal Article|
|Keywords:||Safety Performance Function (SPF), Blackspot Identification, Bayesian Latent Class, Crash Causation Mechanisms, Transportation Safety, Multiple Risk Generating Processes|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Transport Engineering (090507)
|Divisions:||Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Faculty of Health
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2016 Transportation Research Board (US)|
|Deposited On:||09 Feb 2016 23:58|
|Last Modified:||08 Jan 2017 22:42|
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