Development of Test Cases for Automated Vehicle Driving Safety Assessment Using Driving Trajectories.

For consumers to have confidence in the safety of automated vehicles (AVs), AVs must be assessed using systematically developed scenarios to verify driving safety and reliability. In particular, verification using scenarios has been widely performed for the assessment and certification of AVs. This study aims to develop test cases based on driving trajectories to assess the driving safety of AVs.

Development of a Framework for Generating Driving Safety Assessment Scenarios for Automated Vehicles.

Despite the technological advances in automated driving systems, traffic accidents involving automated vehicles (AVs) continue to occur, raising concerns over the safety and reliability of automated driving. For the smooth commercialization of AVs, it is necessary to systematically assess the driving safety of AVs under the various situations that they potentially face. In this context, these various situations are mostly implemented by using systematically developed scenarios.

Risk of COVID-19 Infection in Public Transportation: The Development of a Model.

South Korea's social distancing policies on public transportation only involve mandatory wearing of masks and prohibition of food intake, similar to policies on other indoor spaces. This is not because public transportation is safe from coronavirus disease 2019 (COVID-19), but because no suitable policies based on accurate data have been implemented. To relieve fears regarding contracting COVID-19 infection through public transportation, the government should provide accurate information and take appropriate measures to lower the risk of COVID-19.

Passenger, airline, and policy responses to the COVID-19 crisis: The case of South Korea.

In recent years, domestic and international air passenger markets have expanded steadily around the world with the rapid growth of low cost carriers and aggressive route expansion; however, the unprecedented crisis caused by the coronavirus disease 2019 (COVID-19) pandemic resulted in greatly decreased air travel and an uncertain future for the aviation industry. The present study examined South Korean passengers, airlines, and government policy responses to the COVID-19 pandemic, and it suggests policy directions for the pandemic and post-pandemic periods. Air passengers respond to internal and external factors, and their demand for travel will increase with the reduction in global COVID cases and vaccine distribution.

Safety Analysis of Motorcycle Crashes in Seoul Metropolitan Area, South Korea: An Application of Nonlinear Optimal Scaling Methods.

This study identifies the critical factors that affect motorcycle crash severity based on Korean motorcycle crash data in 2009. Motorcyclists, the environment, roadways, other vehicles involved in the crashes, and traffic flow characteristics were used as variables for identifying critical factors. Multivariable statistical methods were used to analyze the data, including categorical principal components analysis (CatPCA) and nonlinear canonical correlation analysis (NLCCA).

Evaluation of countermeasures for red light running by traffic simulator-based surrogate safety measures.

Objective: The conflicts among motorists entering a signalized intersection with the red light indication have become a national safety issue. Because of its sensitivity, efforts have been made to investigate the possible causes and effectiveness of countermeasures using comparison sites and/or before-and-after studies. Nevertheless, these approaches are ineffective when comparison sites cannot be found, or crash data sets are not readily available or not reliable for statistical analysis.

An estimation of vehicle kilometer traveled and on-road emissions using the traffic volume and travel speed on road links in Incheon City.

The objective of this study is to estimate the vehicle kilometer traveled (VKT) and on-road emissions using the traffic volume in urban. We estimated two VKT; one is based on registered vehicles and the other is based on traffic volumes. VKT for registered vehicles was 2.

Distribution-dependent robust linear optimization with applications to inventory control.

This paper tackles linear programming problems with data uncertainty and applies it to an important inventory control problem. Each element of the constraint matrix is subject to uncertainty and is modeled as a random variable with a bounded support. The classical robust optimization approach to this problem yields a solution with guaranteed feasibility.

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots.

Hot spot identification (HSID) aims to identify potential sites-roadway segments, intersections, crosswalks, interchanges, ramps, etc.-with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice.

Injury severity in delivery-motorcycle to vehicle crashes in the Seoul metropolitan area.

More than 56% of motorcycles in Korea are used for the purpose of delivering parcels and food. Since such delivery requires quick service, most motorcyclists commit traffic violations while delivering, such as crossing the centerline, speeding, running a red light, and driving in the opposite direction down one-way streets. In addition, the fatality rate for motorcycle crashes is about 12% of the fatality rate for road traffic crashes, which is considered to be high, although motorcycle crashes account for only 5% of road traffic crashes in South Korea.

Development of an accident duration prediction model on the Korean Freeway Systems.

Since duration prediction is one of the most important steps in an accident management process, there have been several approaches developed for modeling accident duration. This paper presents a model for the purpose of accident duration prediction based on accurately recorded and large accident dataset from the Korean Freeway Systems. To develop the duration prediction model, this study utilizes the log-logistic accelerated failure time (AFT) metric model and a 2-year accident duration dataset from 2006 to 2007.

Accident prediction model for railway-highway interfaces.

Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs.