Analysis of the duration of compliance between recidivism of drunk driving and reinstatement of license after suspension or revocation.

The objective of this study was to examine the specific deterrence effect of administrative license suspension and revocation regarding the duration of compliance. This study tried to capture the effort of the reinstated offenders to increase the duration of their compliance in spite of their subsequent conviction for DUI. Specifically, the difference between the recidivism rate and the duration of compliance was examined and compared in terms of drivers' characteristics, including gender, the type of license, and age. Data from all drivers who have been newly licensed for five years from 2009 to 2014 in Korea were collected for analysis. The proportional hazard regression and logistic regression models were estimated for the drivers with suspended or revoked licenses, respectively. The former was for exploring the duration of compliance, and the latter was for analyzing the recidivism rate. The results of the analysis showed that license sanctions significantly increased the duration of compliance. The results indicated that the violation-prone groups included male drivers, those with regular and moped licenses, and those between the ages of 30 and 39. License suspension was more effective than license revocation, but this finding did not hold for regular licensed drivers. Drivers' groups that showed different results between compliance duration and recidivism rate also were identified and appropriate treatments should be implemented to improve their willingness to comply.

Deterrent effects of demerit points and license sanctions on drivers' traffic law violations using a proportional hazard model.

Current traffic law enforcement places an emphasis on reducing accident risk from human factors such as drunk driving and speeding. Among the various strategies implemented, demerit points and license sanction systems have been widely used as punitive and educational measures. Limitations, however, exist in previous studies in terms of estimating the interaction effects of demerit points and license sanctions. To overcome such limitations, this work focused on identifying the interaction effects of demerit points and license sanctions on driver traffic violation behavior. The interaction deterrent effects were assessed by using a Cox's proportional hazard model to provide a more accurate and unbiased estimation. For this purpose, five years of driver conviction data was obtained from the Korea National Police Agency (KNPA). This data included personal characteristics, demerit point accumulation and license sanction status. The analysis showed that accumulated demerit points had specific deterrent effects. Additionally, license revocation showed consistent and significant deterrent effects, greater than those for suspension. Male drivers under their 30s holding a motorcycle license were identified as the most violation-prone driver group, suggesting that stricter testing for the acquisition of a motorcycle driver's license is needed.

Safety features of freeway weaving segments with a buffer-separated high-occupancy-vehicle (HOV) lane.

This paper investigates the crash-rate distribution of the freeway weaving segments with a buffer-separated high-occupancy-vehicle lane. For this purpose, crash, traffic and geometry data were extracted from various sources. These extracted data were either spatially or spatiotemporally matched with one another, and used for both descriptive and model-based analyses. The descriptive analysis revealed that crash-rate distributions of the weaving segments depend not only on the class of the subject highway but also on the presence of an access point. This finding was statistically confirmed by crash frequency models. Notably, both descriptive and model-based analyses showed that weaving segments with an access point tend to show lower crash-rates than the counterparts without one. One might attribute this counterintuitive phenomenon to a certain underlying tendency by which traffic engineers or planners generally follow in selecting locations of an access point along the freeway. Further research is required to resolve such a conjecture.

Google Earth elevation data extraction and accuracy assessment for transportation applications.

Roadway elevation data is critical for a variety of transportation analyses. However, it has been challenging to obtain such data and most roadway GIS databases do not have them. This paper intends to address this need by proposing a method to extract roadway elevation data from Google Earth (GE) for transportation applications. A comprehensive accuracy assessment of the GE-extracted elevation data is conducted for the area of conterminous USA. The GE elevation data was compared with the ground truth data from nationwide GPS benchmarks and roadway monuments from six states in the conterminous USA. This study also compares the GE elevation data with the elevation raster data from the U.S. Geological Survey National Elevation Dataset (USGS NED), which is a widely used data source for extracting roadway elevation. Mean absolute error (MAE) and root mean squared error (RMSE) are used to assess the accuracy and the test results show MAE, RMSE and standard deviation of GE roadway elevation error are 1.32 meters, 2.27 meters and 2.27 meters, respectively. Finally, the proposed extraction method was implemented and validated for the following three scenarios: (1) extracting roadway elevation differentiating by directions, (2) multi-layered roadway recognition in freeway segment and (3) slope segmentation and grade calculation in freeway segment. The methodology validation results indicate that the proposed extraction method can locate the extracting route accurately, recognize multi-layered roadway section, and segment the extracted route by grade automatically. Overall, it is found that the high accuracy elevation data available from GE provide a reliable data source for various transportation applications.

Finite mixture modeling approach for developing crash modification factors in highway safety analysis.

This study aimed to investigate the relative performance of two models (negative binomial (NB) model and two-component finite mixture of negative binomial models (FMNB-2)) in terms of developing crash modification factors (CMFs). Crash data on rural multilane divided highways in California and Texas were modeled with the two models, and crash modification functions (CMFunctions) were derived. The resultant CMFunction estimated from the FMNB-2 model showed several good properties over that from the NB model. First, the safety effect of a covariate was better reflected by the CMFunction developed using the FMNB-2 model, since the model takes into account the differential responsiveness of crash frequency to the covariate. Second, the CMFunction derived from the FMNB-2 model is able to capture nonlinear relationships between covariate and safety. Finally, following the same concept as those for NB models, the combined CMFs of multiple treatments were estimated using the FMNB-2 model. The results indicated that they are not the simple multiplicative of single ones (i.e., their safety effects are not independent under FMNB-2 models). Adjustment Factors (AFs) were then developed. It is revealed that current Highway Safety Manual's method could over- or under-estimate the combined CMFs under particular combination of covariates. Safety analysts are encouraged to consider using the FMNB-2 models for developing CMFs and AFs.

Finite mixture modeling for vehicle crash data with application to hotspot identification.

The application of finite mixture regression models has recently gained an interest from highway safety researchers because of its considerable potential for addressing unobserved heterogeneity. Finite mixture models assume that the observations of a sample arise from two or more unobserved components with unknown proportions. Both fixed and varying weight parameter models have been shown to be useful for explaining the heterogeneity and the nature of the dispersion in crash data. Given the superior performance of the finite mixture model, this study, using observed and simulated data, investigated the relative performance of the finite mixture model and the traditional negative binomial (NB) model in terms of hotspot identification. For the observed data, rural multilane segment crash data for divided highways in California and Texas were used. The results showed that the difference measured by the percentage deviation in ranking orders was relatively small for this dataset. Nevertheless, the ranking results from the finite mixture model were considered more reliable than the NB model because of the better model specification. This finding was also supported by the simulation study which produced a high number of false positives and negatives when a mis-specified model was used for hotspot identification. Regarding an optimal threshold value for identifying hotspots, another simulation analysis indicated that there is a discrepancy between false discovery (increasing) and false negative rates (decreasing). Since the costs associated with false positives and false negatives are different, it is suggested that the selected optimal threshold value should be decided by considering the trade-offs between these two costs so that unnecessary expenses are minimized.

Bias properties of Bayesian statistics in finite mixture of negative binomial regression models in crash data analysis.

Factors that cause heterogeneity in crash data are often unknown to researchers and failure to accommodate such heterogeneity in statistical models can undermine the validity of empirical results. A recently proposed finite mixture for the negative binomial regression model has shown a potential advantage in addressing the unobserved heterogeneity as well as providing useful information about features of the population under study. Despite its usefulness, however, no study has been found to examine the performance of this finite mixture under various conditions of sample sizes and sample-mean values that are common in crash data analysis. This study investigated the bias associated with the Bayesian summary statistics (posterior mean and median) of dispersion parameters in the two-component finite mixture of negative binomial regression models. A simulation study was conducted using various sample sizes under different sample-mean values. Two prior specifications (non-informative and weakly-informative) on the dispersion parameter were also compared. The results showed that the posterior mean using the non-informative prior exhibited a high bias for the dispersion parameter and should be avoided when the dataset contains less than 2,000 observations (even for high sample-mean values). The posterior median showed much better bias properties, particularly at small sample sizes and small sample means. However, as the sample size increases, the posterior median using the non-informative prior also began to exhibit an upward-bias trend. In such cases, the posterior mean or median with the weakly-informative prior provided smaller bias. Based on simulation results, guidelines about the choice of priors and the summary statistics to use are presented for different sample sizes and sample-mean values.

Application of finite mixture models for vehicle crash data analysis.

Developing sound or reliable statistical models for analyzing motor vehicle crashes is very important in highway safety studies. However, a significant difficulty associated with the model development is related to the fact that crash data often exhibit over-dispersion. Sources of dispersion can be varied and are usually unknown to the transportation analysts. These sources could potentially affect the development of negative binomial (NB) regression models, which are often the model of choice in highway safety. To help in this endeavor, this paper documents an alternative formulation that could be used for capturing heterogeneity in crash count models through the use of finite mixture regression models. The finite mixtures of Poisson or NB regression models are especially useful where count data were drawn from heterogeneous populations. These models can help determine sub-populations or groups in the data among others. To evaluate these models, Poisson and NB mixture models were estimated using data collected in Toronto, Ontario. These models were compared to standard NB regression model estimated using the same data. The results of this study show that the dataset seemed to be generated from two distinct sub-populations, each having different regression coefficients and degrees of over-dispersion. Although over-dispersion in crash data can be dealt with in a variety of ways, the mixture model can help provide the nature of the over-dispersion in the data. It is therefore recommended that transportation safety analysts use this type of model before the traditional NB model, especially when the data are suspected to belong to different groups.