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  • Contrasting case-wise deletion with multiple imputation and latent variable approaches to dealing with missing observations in count regression models
    Anal. Methods Accid. Res. (IF 9.333) Pub Date : 2019-08-17
    Amir Pooyan Afghari, Simon Washington, Carlo Prato, Md Mazharul Haque

    Missing 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.

    更新日期:2019-12-11
  • Modeling unobserved heterogeneity for zonal crash frequencies: A Bayesian multivariate random-parameters model with mixture components for spatially correlated data
    Anal. Methods Accid. Res. (IF 9.333) Pub Date : 2019-09-11
    Helai Huang, Fangrong Chang, Hanchu Zhou, Jaeyoung Lee

    This 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.

    更新日期:2019-12-11
  • Bayesian hierarchical modeling of traffic conflict extremes for crash estimation: A non-stationary peak over threshold approach
    Anal. Methods Accid. Res. (IF 9.333) Pub Date : 2019-09-30
    Lai Zheng, Tarek Sayed

    This study presents a Bayesian hierarchical model to estimate crashes from traffic conflict extremes in a non-stationary context. The model combines a peak over threshold approach with non-stationary thresholds in terms of regression quantiles and covariate-dependent parameters of the generalized Pareto distribution. The developed model was applied to estimate rear-end crashes from traffic conflicts of the same type collected from four signalized intersections. The conflicts were measured by the modified time to collision (MTTC) and traffic volume, shock wave area, average shock wave speed, and platoon ratio of each signal cycle were employed as covariates. Thresholds corresponding to quantiles ranging from 80% to 95% were tested and the threshold stability plot indicated the 90% quantile was reasonable. Threshold excesses were then declustered at the signal cycle level, and the remained ones were used to develop the Bayesian hierarchical generalized Pareto distribution models (BHM_GPD). The model estimation results show that accounting for non-stationarity significantly improves the model fit. As well, the best fitted model generated accurate crash estimates with relatively narrow confidence intervals. The developed BHM_GPD model was also compared to the Bayesian hierarchical generalized extreme value model (BHM_GEV). The results show that the two models generate comparable crash estimates in terms of accuracy, but the crash estimates from the BHM_GPD model are generally more precise than those of BHM_GEV model. It is also found that although the peak over threshold approach combined with declustering reduces the number of extreme samples, it ensures the use of actual extremes. Moreover, the limited sample size issue is overcome by the proposed Bayesian hierarchical framework, which allows sharing information from different sites and accounting for unobserved heterogeneity. The findings also imply that the BHM_GEV model is preferred when traffic conflicts are relatively evenly distributed over blocks; otherwise the BHM_GPD model should be a better choice.

    更新日期:2019-12-11
  • Do we need multivariate modeling approaches to model crash frequency by crash types? A panel mixed approach to modeling crash frequency by crash types
    Anal. Methods Accid. Res. (IF 9.333) Pub Date : 2019-10-31
    Tanmoy Bhowmik, Shamsunnahar Yasmin, Naveen Eluru

    In safety literature, simulation-based multivariate framework is the most commonly employed approach for analyzing multiple crash frequency dependent variables. The current research effort contributes to literature on crash frequency analysis by suggesting an alternative and mathematically simpler approach for analyzing multiple crash frequency variables for the same study unit. The proposed recasts a multivariate distributional problem as a repeated measure univariate problem. Specifically, we employed a simpler panel random parameter based univariate model framework to analyze zonal level crash counts for different crash types. The empirical analysis is based on the traffic analysis zone (TAZ) level crash count data for both motorized and non-motorized crashes from Central Florida for the year 2016. The performance of the proposed framework is compared with the performance of the random parameter multivariate negative binomial model (RPMNB) using a host of metrics for estimation sample and hold-out sample. The resulting goodness of fit and predictive measures clearly highlight the comparable performance offered by the proposed framework relative to the commonly used RPMNB model with substantially fewer parameters. The comparison exercise is augmented by computing aggregate level elasticity effects for both PMNB and RPMNB models. The results clearly highlight the comparable performance offered by the proposed PMNB model relative to the traditional RPMNB model. In summary, the proposed framework allows for a parsimonious specification without compromising the model explanatory power and provides similar performance as the most traditional multivariate NB model for analyzing different crash dimensions.

    更新日期:2019-12-11
  • A latent class approach for driver injury severity analysis in highway single vehicle crash considering unobserved heterogeneity and temporal influence
    Anal. Methods Accid. Res. (IF 9.333) Pub Date : 2019-11-09
    Hao Yu, Zhenning Li, Guohui Zhang, Pan Liu

    Temporal variation has been recognized as one of the major sources of unobserved heterogeneity in traffic safety research that has not been completely addressed. Overlooking temporal variation may result to biased estimates of effects of impact factors. This paper develops a latent class mixed logit model with temporal indicators to investigate highway single-vehicle crashes and the effects of significant contributing factors to driver injury severity. Crash data from 2010 to 2016 in Washington State is collected with a total of 31,115 single-vehicle crashes. The developed model is able to interpret both within- and across- class unobserved heterogeneity and temporal variation. After a careful comparison, a two-class model is selected as the final model. Estimation results show that: two temporal indicators show significant influence on latent class probability functions; urban indicator and principle type are found to be random parameters and have significant heterogeneity in the mean as a function of male indicator and driver’s age indicators. Variables with fixed effects, including animal collision, overturn collision, off-road collision, winter, minor arterial, interstate, wet, snow, ice, speed limit, vehicle age, turning movement, out control movement, lane-change movement, no airbag, deployed airbag, partial and total ejection, seatbelt, and no liability, show significant impacts on different levels of injury severity outcomes in each class. This study provided an insightful understanding of the time-varying effects of the significant factors on driver injury severity using marginal effect analysis, and the temporal indicators in the proposed model were found to enhance the model capability of temporal variation identification.

    更新日期:2019-12-11
  • Hourly associations between weather factors and traffic crashes: Non-linear and lag effects
    Anal. Methods Accid. Res. (IF 9.333) Pub Date : 2019-11-09
    Fen Xing, Helai Huang, ZhiYing Zhan, Xiaoqi Zhai, Chunquan Ou, N.N. Sze, K.K. Hon

    Weather is well recognized as a significant environmental factor contributing to higher risk of road crashes. In the conventional road safety studies, weather effects had been set out either based on the instant weather conditions recorded by the police officer attained or the average of meteorological observations over a relatively long time period, such as daily, weekly or even monthly, etc. To the best of our knowledge, it is rare that the lag effect of weather in the preceding period on the crash risk in the current period was attempted. With the use of high-resolution meteorological data in very short time interval, it is possible to evaluate the role of lagged weather effect on safety. In this study, we propose a novel distributed lag non-linear model (DLNM), integrated with case-crossover design, to evaluate the lag effect of weather on crash incidence. The proposed modelling framework could describe the non-linear relationship between weather and crash and the lag effects. Also, the possible over-dispersion and autocorrelation of the time-series weather and crash data can be controlled for. The model was estimated using an integrated meteorological, traffic and crash dataset in Hong Kong. For instances, high resolution data on temperature, humidity, rain intensity and wind speed in 1-hour interval was available. The bi-dimensional exposure-lag-response surfaces are established to visualize the varying effects of possible weather factors on crash risk, with respect to the lag size. Such relationship between effect size and lag size is often overlooked in the literatures. Results indicate that model with 4 degrees of freedom for both weather condition (knots at equal spaces) and lag time (knots at equal intervals) best fit with the observations, in accordance to Quasi-likelihood Akaike information criterion (Q-AIC). Then, stratified analyses are conducted to evaluate the difference in the association among different clusters. Findings should shed light on the modelling of non-linear exposure-response relationship and lag effects in traffic safety time series analysis.

    更新日期:2019-12-11
  • Assessing Risk-Taking in a Driving Simulator Study: Modeling Longitudinal Semi-Continuous Driving Data Using a Two-Part Regression Model with Correlated Random Effects.
    Anal. Methods Accid. Res. (IF 9.333) Pub Date : 2016-02-20
    Van Tran,Danping Liu,Anuj K Pradhan,Kaigang Li,C Raymond Bingham,Bruce G Simons-Morton,Paul S Albert

    Signalized intersection management is a common measure of risky driving in simulator studies. In a recent randomized trial, investigators were interested in whether teenage males exposed to a risk-accepting passenger took more intersection risks in a driving simulator compared with those exposed to a risk-averse peer passenger. Analyses in this trial are complicated by the longitudinal or repeated measures that are semi-continuous with clumping at zero. Specifically, the dependent variable in a randomized trial looking at the effect of risk-accepting versus risk-averse peer passengers on teenage simulator driving is comprised of two components. The discrete component measures whether the teen driver stops for a yellow light, and the continuous component measures the time the teen driver, who does not stop, spends in the intersection during a red light. To convey both components of this measure, we apply a two-part regression with correlated random effects model (CREM), consisting of a logistic regression to model whether the driver stops for a yellow light and a linear regression to model the time spent in the intersection during a red light. These two components are related through the correlation of their random effects. Using this novel analysis, we found that those exposed to a risk-averse passenger have a higher proportion of stopping at yellow lights and a longer mean time in the intersection during a red light when they did not stop at the light compared to those exposed to a risk-accepting passenger, consistent with the study hypotheses and previous analyses. Examining the statistical properties of the CREM approach through simulations, we found that in most situations, the CREM achieves greater power than competing approaches. We also examined whether the treatment effect changes across the length of the drive and provided a sample size recommendation for detecting such phenomenon in subsequent trials. Our findings suggest that CREM provides an efficient method for analyzing the complex longitudinal data encountered in driving simulation studies.

    更新日期:2019-11-01
Contents have been reproduced by permission of the publishers.
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