Cyclist-vehicle crash modeling with measurement error in traffic exposure

Accident; Analysis and Prevention
Mohamed Bayoumi Kamel, Tarek Sayed

Abstract

Exposure measures are always among the explanatory variables of any crash model. Regardless of the technique used to model crash, the mean crash frequency will increase with an increase in exposure since more crashes are likely to occur at higher exposure. For cyclist-vehicle crash models, bike and vehicle exposure measures are essential for an accurate and reliable estimate of the cyclist crash risk. However, traffic exposure measures are an example of variables that are measured with error. Generally, measurement error in regression estimates has three effects: 1) produce bias in parameter estimation for statistical models, 2) lead to a loss of explanation power, 3) mask important features of the data. This study proposes a full Bayesian Poisson Lognormal crash models that account for measurement error in traffic exposure measures (i.e., Vehicle Kilometers Travelled and Bike Kilometers Travelled). The underlying approach is to adjust the traffic exposure measures for measurement error to improve the accuracy of the crash model and crash model estimates. The full Bayesian models are developed using data for 134 traffic analysis zones (TAZs) in the city of Vancouver, Canada. The results show that Poisson Lognormal models that a...Continue Reading

Citations

Apr 30, 2021·Accident; Analysis and Prevention·Shamsunnahar YasminNaveen Eluru
Jun 29, 2021·Accident; Analysis and Prevention·Mohamed Bayoumi Kamel, Tarek Sayed

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