• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.453-459
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• 2010

In the study of traffic safety, it is utmost important to obtain more reliable estimates of the expected crashes for a site (or a segment). The observed crashes have been mainly used as the estimate of the expected crashes in Korea, while the empirical Bayes (EB) estimates based on the Poisson-gamma mixture model have been used in the USA and several European countries. Although numerous studies have used the EB method for estimating the expected crashes and/or the effectiveness of the safety countermeasures, no past studies examine the difference in the estimation errors between the two estimates. Thus, this study compares the estimation errors of the two estimates using a Monte Carlo simulation study. By analyzing the crash dataset at 3,000,000 simulated sites, this study reveals that the estimation errors of the EB estimates are always less than those of the observed crashes. Hence, it is imperative to incorporate the EB method into the traffic safety research guideline in Korea. However, the results show that the differences in the estimation errors between the two estimates decrease as the uncertainty of the prior distribution increases. Consequently, it is recommended that the EB method be used with reliable hyper-parameter estimates after conducting a comprehensive examination on the estimated negative binomial model.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.131-143
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• 1994

Under the asymmetric losses (entropy loss and Stein loss), we find the classes of Bayes and empiricla Bayes estimates for estimating the Poisson means when the distributin of means are believed a priori. Following the idea of Efron and Morris (1973), we have a computer simulation to compute a relative savings loss of proposed estimates as compared to the classical estimates.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.103-111
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• 2011

To evaluate highway safety countermeasures or identify high risk sites, the expected crashes for a site (or segment) have been estimated using the panel crash data. Past studies show that two different methods can be employed to estimate the expected crashes: observed crash based method and empirical Bayes (EB) method. This study conducts a simulation study to analyze how the estimation errors of the two estimates are affected by the different structures of the panel crash data and the presence of the change in safety over time. The results disclose that the estimation errors of the observed crash based estimates (i.e. the mean observed crash and comparative parallel estimate) are always greater than those of the EB estimates regardless of the structure of the panel crash data and the presence of the change in safety over time. Thus, it is highly recommended that the EB method be used in the study of traffic safety to obtain more reliable estimates for the expected crashes. In addition, this study corroborates that the estimation errors of the two estimates decrease as the analysis periods increase if safety does not change over time. Hence, it is also recommended that the 1-year analysis period used for identifying high risk sites in Korea be extended to produce more efficient estimates of the time-constant expected crashes.

• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.160-168
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• 2017

In the past 10 years, the accidents caused by drowsy driving have occupied about 23% of all traffic accidents in Korea expressway network and this rate is the highest one among all accident causes. Unlike other types of accidents caused by speeding and distraction to the road, the accidents by drowsy driving should be managed differently because the drowsiness might not be controlled by human's will. To reduce the number of accidents caused by drowsy driving, researchers previously focused on the spot based analysis. However, what we actually need is a segment (link) and occurring time based analysis, rather than spot based analysis. Hence, this research performs initial effort by adapting link concept in terms of drowsy driving on highway. First of all, we analyze the accidents caused by drowsy in historical accident data along with their road environments. Then, links associate with driving time are analyzed using digital tachograph (DTG) data. To carry this out, negative binomial regression models, which are broadly used in the field, including highway safety manual, are used to define the relationship between the number of traffic accidents on expressway and drivers' behavior derived from DTG. From the results, empirical Bayes (EB) and potential for safety improvement (PSI) analysis are performed for potential risk segments of accident caused by drowsy driving on the future. As the result of traffic accidents caused by drowsy driving, the number of the traffic accidents increases with increase in annual average daily traffic (AADT), the proportion of trucks, the amount of DTG data, the average proportion of speeding over 20km/h, the average proportion of deceleration, and the average proportion of sudden lane-changing.