1,721,063 research outputs found
Developing safety performance functions for railway grade crossings: A case study of Canada
No full textAlthough accident frequencies at railway grade crossings have shown a decreasing trend over the last two decades (partly due to implemented safety improvements and technological advances), safety at grade crossings is still a major concern since crossing accidents are usually associated with devastating consequences. This paper investigates the effect of various site attributes on railway crossing safety outcomes using recent Canada wide data from a 6-year period (2008-2013). The new data sets allow adjusting previous accident models according to latest circumstances (e.g., vehicles' improved safety features) affecting safety dynamics at crossings. Employing Bayesian hierarchical models including the non-conventional Poisson-Weibull model, different safety performance functions were separately developed for crossings with the following major warning systems: (1) flashing light and bell (FLB), (2) flashing light, bell, and gate (FLBG), (3) standard reflectorized crossing sign (SRCS), and (4) standard reflectorized crossing sign and stop sign (SRCS & STOP). Among other findings, the results indicated that traffic exposure (product of train and vehicle), number of lanes, whistle prohibition, train speed, and road speed were the most important factors affecting accident frequencies at Canadian railway crossings. It should be also noted that safety performance functions vary, in terms of independent variables and their associated coefficients, between the aforementioned warning devices.</p
Is speeding more likely during weekend night hours? Evidence from sensor-collected data in Montreal
No full textA number of traffic safety studies have investigated temporal variations in road safety indicators such as crash frequency, confirming that such variations exist. This paper examined whether speeding is more likely on weekend nights relative to all other times of the day by directly comparing speeding during weekends and weekdays. To this end, we analysed a sample of local streets, in Montreal, for which speed data were collected automatically using traffic analyser sensors. We found that, interestingly, weekend speeding was less likely to occur during night hours, whereas it was more likely to occur during evening and midday hours. Among other findings, the results indicated that one-way streets and those having a speed limit of 50 km/h were slightly less prevalent in speeding on weekends. Our results can be useful in designing road safety interventions, including publicity campaigns and police enforcement, which aim at reducing speeding behaviours
Multilevel Dirichlet process mixture analysis of railway grade crossing crash data
No full textThis article introduces a flexible Bayesian semiparametric approach to analyzing crash data that are of hierarchical or multilevel nature. We extend the traditional varying intercept (random effects) multilevel model by relaxing its standard parametric distributional assumption. While accounting for unobserved cross-group heterogeneity in the data through intercept, the proposed method allows identifying latent subpopulations (and consequently outliers) in data based on a Dirichlet process mixture. It also allows estimating the number of latent subpopulations using an elegant mathematical structure instead of prespecifying this number arbitrarily as in conventional latent class or finite mixture models. In this paper, we evaluate our method on two recent railway grade crossing crash datasets, at province and municipality levels, from Canada for the years 2008-2013. We use cross-validation predictive densities and pseudo-Bayes factor for Bayesian model selection. While confirming the need for a multilevel modeling approach for both datasets, the results reveal the inadequacy of the standard parametric assumption in the varying intercept model for the municipality-level dataset. In fact, our proposed method is shown to improve model fitting significantly for the latter data. In a fully probabilistic framework, we also identify the expected number of latent clusters that share similar unidentified features among Canadian provinces and municipalities. It is possible thus to further investigate the reasons for such similarities and dissimilarities. This can have important policy implications for various safety management programs.</p
Bayesian methodology to estimate and update safety performance functions under limited data conditions: A sensitivity analysis
No full textIn road safety studies, decision makers must often cope with limited data conditions. In such circumstances, the maximum likelihood estimation (MLE), which relies on asymptotic theory, is unreliable and prone to bias. Moreover, it has been reported in the literature that (a) Bayesian estimates might be significantly biased when using non-informative prior distributions under limited data conditions, and that (b) the calibration of limited data is plausible when existing evidence in the form of proper priors is introduced into analyses. Although the Highway Safety Manual (2010) (HSM) and other research studies provide calibration and updating procedures, the data requirements can be very taxing. This paper presents a practical and sound Bayesian method to estimate and/or update safety performance function (SPF) parameters combining the information available from limited data with the SPF parameters reported in the HSM. The proposed Bayesian updating approach has the advantage of requiring fewer observations to get reliable estimates. This paper documents this procedure. The adopted technique is validated by conducting a sensitivity analysis through an extensive simulation study with 15 different models, which include various prior combinations. This sensitivity analysis contributes to our understanding of the comparative aspects of a large number of prior distributions. Furthermore, the proposed method contributes to unification of the Bayesian updating process for SPFs. The results demonstrate the accuracy of the developed methodology. Therefore, the suggested approach offers considerable promise as a methodological tool to estimate and/or update baseline SPFs and to evaluate the efficacy of road safety countermeasures under limited data conditions.</p
Benchmarking regions using a heteroskedastic grouped random parameters model with heterogeneity in mean and variance: applications to grade crossing safety analysis
No full textComparing regions while adjusting for differences in characteristics of sites located in those regions is valuable since it identifies possible inter-regional dissimilarities in crash risk propensities according to specific safety performance measures (e.g., crash frequency of a specific type). This paper describes a framework to benchmark different regions (neighborhoods, provinces, etc.) in terms of a selected safety performance measure. To avoid issues relating to aggregated (macro-level) data, we use disaggregate (micro-level) data to draw inferences at a macro/region-level, which is often needed for developing large-scale transportation safety and planning programs and policies. To overcome unobserved heterogeneity, we employ a multilevel Bayesian heteroskedastic Poisson lognormal model with grouped random parameters allowing heterogeneity in both mean and variance parameters. The proposed approach is illustrated through a comprehensive study of highway railway grade crossings across Canada. The results indicate that the proposed model addresses unobserved heterogeneity more efficiently and provides more insight compared to conventional random parameters models. For example, we found that as traffic exposure increases, grade crossing safety deteriorates at a higher rate in the Canadian Prairies than in the other regions. Our benchmarking framework is also affected by different model specifications. The results indicate the need for further in-depth investigations, which could help to identify possible reasons for inter-region differences in terms of specific safety indicators. This study provides valuable guidelines to Canadian transportation authorities, revealing important underlying crash mechanisms at highway railway grade crossings in Canada.</p
Using a flexible multivariate latent class approach to model correlated outcomes: A joint analysis of pedestrian and cyclist injuries
No full textSeveral recent transportation safety studies have indicated the importance of accounting for correlated outcomes, for example, among different crash types, including differing injury-severity levels. In this paper, we discuss inference for such data by introducing a flexible Bayesian multivariate model. In particular, we use a Dirichlet process mixture to keep the dependence structure unconstrained, relaxing the usual homogeneity assumptions. The resulting model collapses into a latent class multivariate model that is in the form of a flexible mixture of multivariate normal densities for which the number of mixtures (latent components) not only can be large but also can be inferred from the data as part of the analysis. Therefore, besides accounting for correlation among crash types through a heterogeneous correlation structure, the proposed model helps address unobserved heterogeneity through its latent class component. To our knowledge, this is the first study to propose and apply such a model in the transportation literature. We use the model to investigate the effects of various factors such as built environment characteristics on pedestrian and cyclist injury counts at signalized intersections in Montreal, modeling both outcomes simultaneously. We demonstrate that the homogeneity assumption of the standard multivariate model does not hold for the dataset used in this study. Consequently, we show how such a spurious assumption affects predictive performance of the model and the interpretation of the variables based on marginal effects. Our flexible model better captures the underlying complex structure of the correlated data, resulting in a more accurate model that contributes to a better understanding of safety correlates of non-motorist road users. This in turn helps decision-makers in selecting more appropriate countermeasures targeting vulnerable road users, promoting the mobility and safety of active modes of transportation.</p
Bayesian nonparametric modeling in transportation safety studies: Applications in univariate and multivariate settings
No full textIn transportation safety studies, it is often necessary to account for unobserved heterogeneity and multimodality in data. The commonly used standard or over-dispersed generalized linear models (e.g., negative binomial models) do not fully address unobserved heterogeneity, assuming that crash frequencies follow unimodal exponential families of distributions. This paper employs Bayesian nonparametric Dirichlet process mixture models demonstrating some of their major advantages in transportation safety studies. We examine the performance of the proposed approach using both simulated and real data. We compare the proposed model with other models commonly used in road safety literature including the Poisson-Gamma, random effects, and conventional latent class models. We use pseudo Bayes factors as the goodness-of-fit measure, and also examine the performance of the proposed model in terms of replicating datasets with high proportions of zero crashes. In a multivariate setting, we extend the standard multivariate Poisson-lognormal model to a more flexible Dirichlet process mixture multivariate model. We allow for interdependence between outcomes through a nonparametric random effects density. Finally, we demonstrate how the robustness to parametric distributional assumptions (usually the multivariate normal density) can be examined using a mixture of points model when different (multivariate) outcomes are modeled jointly.</p
Identifying areas of high risk for collisions: A Canda-wide study of grade crossing safety
No full textRanking sites and identifying high-crash risk locations based on various safety performance measures (e.g. expected crash frequency) are among the key tasks of the safety management program, enabling an effective allocation of funds for safety improvement projects. While several studies have discussed the issues relating to the hotspot identification process at a micro-level (e.g., intersections or highway segments), less attention is given to the macro-level hotspot identification issue: how to identify areas or regions with the highest risk of crashes. In this research, we introduce a Bayesian multilevel (hierarchical) model for estimating the regional differences while controlling for other important site attributes. The proposed method is illustrated using a case study on railway grade crossings in Canada. While accommodating the spatial dependencies of crash risk, our method allows a fair comparison of different regions by adjusting for the effect of covariates such as traffic exposure. In particular, we compute pairwise probabilities of crash risk for each province in Canada compared to all others. We are therefore able to draw inferences about regional safety performances under similar circumstances. Our findings indicate the need for further investigation to identify the possible reasons for inter-region variations in grade crossing safety across Canada. Our approach could be useful to guide safety policy development and resource allocation.</p
Bayesian road safety analysis: Incorporation of past evidence and effect of hyper-prior choice
No full textProblem This paper aims to address two related issues when applying hierarchical Bayesian models for road safety analysis, namely: (a) how to incorporate available information from previous studies or past experiences in the (hyper) prior distributions for model parameters and (b) what are the potential benefits of incorporating past evidence on the results of a road safety analysis when working with scarce accident data (i.e., when calibrating models with crash datasets characterized by a very low average number of accidents and a small number of sites). Method A simulation framework was developed to evaluate the performance of alternative hyper-priors including informative and non-informative Gamma, Pareto, as well as Uniform distributions. Based on this simulation framework, different data scenarios (i.e., number of observations and years of data) 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. Results This study shows how the accuracy of model parameter estimates (inverse dispersion parameter) is considerably improved when incorporating past evidence, in particular when working with the small number of observations and crash data with low mean. The results also illustrates that when the sample size (more than 100 sites) and the number of years of crash data is relatively large, neither the incorporation of past experience nor the choice of the hyper-prior distribution may affect the final results of a traffic safety analysis. Conclusions As a potential solution to the problem of low sample mean and small sample size, this paper suggests some practical guidance on how to incorporate past evidence into informative hyper-priors. By combining evidence from past studies and data available, the model parameter estimates can significantly be improved. The effect of prior choice seems to be less important on the hotspot identification. Impact on Industry The results show the benefits of incorporating prior information when working with limited crash data in road safety studies.</p
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