A comparative Full Bayesian before-and-after analysis and application to urban road safety countermeasures in New Jersey (original) (raw)
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Journal of Safety Research, 2013
This paper addresses two key issues when applying hierarchical Bayes methods for traffic safety analysis, namely, (a) how to incorporate available information from previous studies (past experiences) for the specification of informative hyper-priors in accident data modeling; and, (b) what are the practical implications of the choice of hyper-priors on the results of a road safety analysis. In this paper, we first illustrate how to incorporate an integrated summary of the available evidences from previous studies for the development of informative hyper-priors in the dispersion parameter. The performance of different hyper-priors was then investigated, including Gamma and Uniform probability distributions. For this purpose, different simulation scenarios were defined and tested using crash data collected at 3-legged rural intersections in California and crash data collected for rural 4-lane highway segments in Texas. By doing so, we show how the accuracy of parameter estimates are considerably improved, in particular when working with limited accident data, i.e., crash datasets with low mean and small sample size. The results however show that the past knowledge incorporated in the hyper-priors can only slightly improve the accuracy of the hotspot identification methods. In addition, when the number of roadway elements or the time period (e.g., number of years of crash data) is relatively large, the choice of the hyper-prior distribution does not have a significant impact on the final results of a traffic safety analysis.
Statistical Road Safety Modeling
Transportation Research Record, 2004
The hope is that statistical models fitted to historical data can be used to estimate the effect of road design elements on safety. Whether this can be done is not clear. One sign of trouble is that models based on diverse data sets tend not to yield similar results. In this paper some suggestions are made on how to increase the chance of success in this quest. Emphasis is on three questions: 1.Which variables should serve in the model; 2.What mathematical function should represent their influence; and 3.How to check whether the representation of the influence of a variable is appropriate.
2010
Recent research suggests that traditional safety evaluation methods may be inadequate in accurately determining the effectiveness of roadway safety measures. In recent years, advanced statistical methods are being utilized in traffic safety studies to more accurately determine the effectiveness of roadway safety measures. These methods, particularly Bayesian statistical techniques, have the capabilities to account for the shortcomings of traditional methods. Hierarchical Bayesian modeling is a powerful tool that more fully identifies a given problem than a simpler model could. This report explains the process wherein a hierarchical Bayesian model is developed as a tool to analyze the effectiveness of two types of road safety measures: raised medians and cable barrier. Several sites where these safety measures have been implemented in the last 10 years were evaluated using available crash data. The results of this study show that the installation of a raised median is an effective te...
Overdispersion in modelling accidents on road sections and in Empirical Bayes estimation
Accident Analysis & Prevention, 2001
In multivariate statistical models of road safety one usually finds that the accident counts are 'overdispersed'. The extent of the overdispersion is itself subject to estimation. It is shown that the assumption one makes about the nature of overdispersion will affect the maximum likelihood estimates of model parameters. If one assumes that the same overdispersion parameter applies to all road sections in the data base, then, the maximum likelihood estimate of parameters will be unduly influenced by very short road sections and insufficiently influenced by long road sections. The same assumption about the overdispersion parameter also leads to an inconsistency when one estimates the safety of a road section by the Empirical Bayes method. A way to avoid both problems is to estimate an overdispersion parameter () that applies to a unit length of road, and to set the overdispersion parameter for a road section of length L to L. How this would change the estimates of regression parameters for road section models now in use requires examination. Safety estimation by the Empirical Bayes method is altered substantially.
2014
Using the Bayesian approach as the model selection criteria, the main purpose in this study is to establish a practical road accident model that can provide a better interpretation and prediction performance. For this purpose we are using a structural explanatory model with autoregressive error term. The model estimation is carried out through Bayesian inference and the best model is selected based on the goodness of fit measures. To cross validate the model estimation further prediction analysis were done. As the road safety measures the number of fatal accidents in Spain, during 2000-2011 were employed. The results of the variable selection process show that the factors explaining fatal road accidents are mainly exposure, economic factors, and surveillance and legislative measures. The model selection shows that the impact of economic factors on fatal accidents during the period under study has been higher compared to surveillance and legislative measures.
Accident Analysis & Prevention, 2008
Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to potential biases in sample databases. This paper offers a multivariate Poisson-lognormal (MVPLN) specification that simultaneously models injuries by severity. The MVPLN specification allows for a more general correlation structure as well as overdispersion. This approach addresses some questions that are difficult to answer by estimating them separately. With recent advancements in crash modeling and Bayesian statistics, the parameter estimation is done within the Bayesian paradigm, using a Gibbs Sampler and the Metropolis-Hastings (M-H) algorithms for crashes on Washington State rural two-lane highways. The estimation results from the MVPLN approach did show statistically significant correlations between crash counts at different levels of injury severity. The non-zero diagonal elements suggested overdispersion in crash counts at all levels of severity. The results lend themselves to several recommendations for highway safety treatments and design policies. For example, wide lanes and shoulders are key for reducing crash frequencies, as are longer vertical curves.
Journal of Transportation Safety & Security, 2020
Improving traffic safety is a priority of most transportation agencies around the world. As part of traffic safety management strategies, efforts have focused on developing more accurate crash-frequency models and on identifying contributing factors in order to implement better countermeasures to improve traffic safety. Over time, models have increased in complexity and computational time. Bayesian models using the MCMC method have been commonly used in traffic safety analyses because of their ability to deal with complex models. Recently, the INLA approach has appeared as an alternative to the MCMC method by significantly reducing the computing time. In this study, an INLA-CAR model is developed to assess crashes by severity at the segment level on a highway section in Banda Aceh, Indonesia and is compared with a Bayesian non-spatial model. Results of the DIC show the importance of including spatial correlation in the models. The coefficient estimates show that AADT is the most influential in both models and across all severity types; however, the coefficient estimates for land use and horizontal alignment vary across severity types. Finally, in order to assess some limitations of the DIC, three other goodness-of-fit measures are used to crossvalidate the results of the DIC.
Accident; analysis and prevention, 2018
The safety and operational improvements of corridors have been the focus of many studies since they carry most traffic on the road network. Estimating a crash prediction model for total crash counts identifies the crash risk factors that are associated with crash counts at a specific type of road entity. However, this may not reveal useful information to detect the road problems and implement effective countermeasures. Therefore, investigating the contributing factors for crash counts by different types is of great importance. This study aims to provide a good understanding of the contributing factors to crash counts by different types at intersections and roadway segments along corridors. Data from 255 signalized intersections and 220 roadway segments along 20 corridors have been used for this study. The investigated crash types include same direction, angle and turning, opposite direction, non-motorized, single vehicle, and other multi-vehicle crashes. Two models have been estimat...
Analytic Methods in Accident Research, 2019
Recent literature on highway safety research has focused on methodological advances to minimize misspecifications and the potential for erroneous estimates and invalid statistical inferences. To further these efforts, this study carries out an empirical assessment of uncorrelated and correlated random-parameters count models for analyzing road crash frequencies on multilane highways considering two crash severities; injury and no-injury. The empirical results indicate that the relative statistical performance of these models is comparable; however, the correlated randomparameters approach accounts for both the heterogeneous effects of explanatory factors across the road segments and the cross-correlations among the random parameter estimates. As noted in the results, statistically significant correlation effects among the random parameters confirm the adequacy of this approach. The safety models for multilane roadways presented in this study can be useful in (i) the detection of critical risk factors on these road types, (ii) the assessment of crash reduction due to improvements in pavement condition and retrofitting of roadway geometric features and, (iii) the prediction of crash frequency while comparing different design alternatives. As such, the outcomes of this study may assist design engineers and highway agencies in designing new or calibrating existing multilane roadways from a safety standpoint.
Applying Bayesian hierarchical models to examine motorcycle crashes at signalized intersections
Accident Analysis & Prevention, 2010
Motorcycles are overrepresented in road traffic crashes and particularly vulnerable at signalized intersections. The objective of this study is to identify causal factors affecting the motorcycle crashes at both four-legged and T signalized intersections. Treating the data in time-series cross-section panels, this study explores different Hierarchical Poisson models and found that the model allowing autoregressive lag 1 dependent specification in the error term is the most suitable. Results show that the number of lanes at the four-legged signalized intersections significantly increases motorcycle crashes largely because of the higher exposure resulting from higher motorcycle accumulation at the stop line. Furthermore, the presence of a wide median and an uncontrolled left-turn lane at major roadways of four-legged intersections exacerbate this potential hazard. For T signalized intersections, the presence of exclusive right-turn lane at both major and minor roadways and an uncontrolled leftturn lane at major roadways of T intersections increases motorcycle crashes. Motorcycle crashes increase on high-speed roadways because they are more vulnerable and less likely to react in time during conflicts. The presence of red light cameras reduces motorcycle crashes significantly for both four-legged and T intersections. With the red-light camera, motorcycles are less exposed to conflicts because it is observed that they are more disciplined in queuing at the stop line and less likely to jump start at the start of green.