Abstract: Publication date: Available online 11 September 2019Source: Analytic Methods in Accident ResearchAuthor(s): Helai Huang, Fangrong Chang, Hanchu Zhou, Jaeyoung LeeAbstractThis study applies mixture components in a multivariate random parameters spatial model for zonal crash counts. Three different modeling formulations are employed to demonstrate the effects of mixture components and spatial heterogeneity in the goodness-of-fit in a multivariate random parameter model. The models are built for injury (i.e., possible, non-incapacitating, incapacitating, and fatal injury) and non-injury crashes using the data from 738 traffic analysis zones (TAZs) in Hillsborough County of Florida during a three-year period. The Deviance Information Criteria (DIC) is used to evaluate the performances of these models indicate the proposed model outperforms the rests. According to the estimated results, various traffic-related, demographics, and socioeconomic factors affect the occurrences of crashes for different severity levels. With regard to the effect of mixture components, it identifies two homogeneous sub-classes labeled as “stable pattern” and “unstable pattern” to better capture the heterogeneity. The standard deviation (SD) and correlation across injury and non-injury crashes are both very high in the “stable pattern” compared with its “unstable pattern” counterpart. On the other hand, the results of model comparison reveal that: (i) adding one more mixture component has no significant influences on the spatial heterogeneity and spatial correlation of different kinds of crash frequency and (ii) the consideration of spatial effects improves the accuracy of estimate results. Moreover, the multivariate random parameters spatial model with mixture components was compared with its univariate form to highlight the validity of applying multivariate structure.

Abstract: Publication date: Available online 17 August 2019Source: Analytic Methods in Accident ResearchAuthor(s): Amir Pooyan Afghari, Simon Washington, Carlo Prato, Md Mazharul HaqueAbstractMissing data can lead to biased and inefficient parameter estimates in statistical models, depending on the missing data mechanism. Count regression models are no exception, with missing data leading to incorrect inferences about the effects of explanatory variables. A convenient approach for dealing with missing data is to remove observations with incomplete records prior to the analysis - often referred to as case-wise deletion. Removing incomplete records, however, reduces the sample size, increases standard errors and, if data are not missing completely at random, produces biased parameter estimates. A more complex approach is multiple imputation, which provides an estimate of the modelling uncertainty created by the data ‘missing-ness’, as distinct from the natural variation in the data. However, multiple imputation produces biased parameter estimates if the probability of missing data is related to the observed data - or is endogenous. Latent variable modelling has recently been introduced as an alternative approach for dealing with missing data, but it comes at a high computational cost and complexity.Despite fairly extensive methodological advancements in statistical literature, case-wise deletion is commonly employed to deal with missing data in statistical models of transport, while the multiple imputation and latent variable approaches remain relatively unexplored. More importantly, the performance of these approaches has not been tested across different types of data missing-ness. To address these gaps, this study aims to contrast case-wise deletion with multiple imputation and latent variable approaches in dealing with missing data in count regression models. We compare the performance of these three approaches using crash count models estimated against empirical data obtained from state controlled roads in Queensland, Australia. A quasi-experimental evaluation of data missing-ness is then conducted by extracting three data subsets from the original dataset, each with a unique missing data mechanism (with terminology adopted from the statistical literature): missing completely at random, missing at random, and missing not at random. The three approaches are then applied to each data subset and the results are compared in terms of bias, precision of parameter estimates, and goodness-of-fit. The findings indicate that multiple imputation is the most effective approach when data are missing either completely at random or at random, whereas the latent variable approach is more effective when data are missing not at random. However, the effectiveness of the latent variable approach is dependent on the availability of suitable variables as instruments in the data.

Abstract: Publication date: Available online 12 July 2019Source: Analytic Methods in Accident ResearchAuthor(s): Ugur Eker, Sheikh Shahriar Ahmed, Grigorios Fountas, Panagiotis Ch. AnastasopoulosAbstractThis study aims at investigating public perceptions towards the safety and security implications that will arise after the future introduction of flying cars in the traffic fleet. In this context, we focus on individuals’ opinions about possible safety benefits and concerns as well as about policy measures that can potentially enhance the security of flying car. Due to the emergent nature and lack of public exposure of this technology, individuals’ perceptions and opinions regarding flying cars might be subject to several layers of unobserved heterogeneity, such as shared unobserved variations across interrelated perceptions, grouped effects, and interactive effects between various sources of unobserved heterogeneity. To explore individuals’ perceptions accounting, at the same time, for such heterogeneity patterns, grouped random parameters bivariate probit and correlated grouped random parameters binary probit models with heterogeneity in means are estimated. In this context, data collected from an online survey of 584 individuals from the United States are statistically analyzed. The estimation results revealed that a number of individual-specific socio-demographic, behavioral and driving attributes affect the perceptions towards the safety aspects of flying cars, along with the attitudes towards potential security interventions. Despite the exploratory nature of the analysis, the findings of this study can provide manufacturers, policy-makers and regulating agencies with valuable information regarding the integration and acceptance challenges that may arise with the introduction of flying cars.

Abstract: Publication date: Available online 22 June 2019Source: Analytic Methods in Accident ResearchAuthor(s): Tariq Usman Saeed, Thomas Hall, Hiba Baroud, Matthew J. VolovskiAbstractRecent 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 random-parameters 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.

Abstract: Publication date: Available online 14 June 2019Source: Analytic Methods in Accident ResearchAuthor(s): Lai Zheng, Tarek Sayed, Mohamed EssaAbstractA Bayesian hierarchical modeling (BHM) approach is used to model non-stationary traffic conflict extremes of different sites together for crash estimation. The hierarchical structure has three layers, a data layer that is modeled with a generalized extreme value (GEV) distribution, a latent Gaussian process layer that relates parameters of GEV to covariates and the unobserved heterogeneity, and a prior layer with prior distributions to characterize the latent process. The proposed approach was applied to traffic conflicts collected at the signal cycle level from four intersections in the city of Surrey, British Columbia. Traffic conflicts were measured by the modified time to collision (MTTC) indicator while traffic volume, shock wave area, average shock wave speed, and platoon ration of each cycle were employed as covariates. Four BHM models were developed, including a stationary model (i.e., BHM_GEV(0,0,0)) with no covariates and three non-stationary models (i.e., BHM_GEV(1,0,0), BHM_GEV(0,1,0), and BHM_GEV(1,1,0)) with covariates added to the location parameter, scale parameter, and both parameters of the GEV distribution, respectively. Traditional at-site GEV models were also developed for individual sites for comparison purposes. The results show that the BHM_GEV(1,1,0) is the best fitted model among the four models since considering covariates and unobserved heterogeneity significantly improves the model performance in terms of goodness of fit. The BHM_GEV(1,1,0) also yields relatively accurate and more precise crash estimates compared to the at-site models. This is attributed to the BHM_GEV(1,1,0) allowing borrowing strength from other sites. It is also found that the traffic volume, shock wave area, and platoon ratio have significant influence on the safety of signalized intersections.