• International Journal of Highway Engineering
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• v.18 no.5
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• pp.75-82
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• 2016

PURPOSES : This study was initiated to estimate the benefits from the campaign to prevent drowsy driving crashes on expressways. The study was conducted by the Korea Expressway Corporation using a contingent valuation method. METHODS : First, a questionnaire was designed for a preliminary survey. From the survey's results, the initial willingness to pay for the campaign was determined by averaging different amounts of payments chosen under virtual scenarios in the survey. The willingness to pay data was used to find a first bid price for the open-ended method used for the second survey. After that, a primary questionnaire was designed and conducted using a single dichotomous choice question (SDBCQ). Drivers at expressway resting areas were asked their willingness to pay for the campaign. Based on statistical analysis using data collected from the second survey, the mean willingness to pay was estimated using a probability utility function. Finally, the benefit from the campaign was calculated using the estimated willingness to pay and accident data on expressways. CONCLUSIONS : Based on the result from the contingent valuation method, the benefit from the campaign to prevent drowsy driving crashes was estimated to be 170.6 won per expressway trip. The benefit is to be paid as an additional toll. In addition, the traffic crash cost estimate is about 2,209,680,000 won less than the cost during the same period in 2014.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.2
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• pp.42-54
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• 2011

This study presented an algorithm to detect zigzag driving maneuver that is highly associated with vehicle crash occurrence. In general, the zigzag driving results from the driver's inattention including drowsy driving and driving while intoxicated. Therefore, the technology to detect such unsafe driving maneuver will provide us with a valuable opportunity to prevent crash in the road. The proposed detection algorithm used angular velocity data obtained from a gyro sensor. Performance evaluations of the algorithm presented promising results for the actual implementation in practice. The outcome of this study can be used as novel information contents under the ubiquitous transportation systems environment.

• Journal of Auto-vehicle Safety Association
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• v.15 no.4
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• pp.88-94
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• 2023

The seating postures of passengers in the automated driving vehicle are possible in atypical forms such as rear-facing and lying down. It is necessary to improve devices such as airbags and seat belts to protect occupants from injury in accidents of the automated driving vehicle, and collision safety evaluation tests must be newly developed. The purpose of this study is to define representative types of head-on collision accidents to develop collision standards for autonomous vehicles that take into account changes in driving behavior and occupants' postures. 150 frontal collision cases remained by filtering (accident videos, images, AIS 2+, passenger car, etc…) and random sampling from approximately 320,000 accidents claimed by a major insurance company over the past 5 years. The most frequent accident type is a head-on collision between a vehicle going straight and a vehicle turning left from the opposite side, accounting for 54.7% of all accidents, and most of these accidents occur in permissive left turns. The next most common frontal collision is the center-lane violation by drowsy driving and careless driving, accounting for 21.3% of the total. For the two types above, data such as vehicle speed, contact point/area, and PDOF at the moment of impact are obtained through accident reconstruction using PC-Crash. As a result, two types of autonomous vehicle crash safety test scenarios are proposed: (1) a frontal oblique collision test based on the accident types between a straight vehicle and a left-turning vehicle, and (2) a small overlap collision test based on the head-on accidents of center-lane violation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.3
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• pp.391-398
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• 2019

In general, truck-involved crashes increase severity in terms of both injury level and crash impact level. Recently, although the frequency and fatality of truck-involved crashes in Korea are rising, their associative studies are very limited. Therefore, the objective of this study is to identify critical factors influencing on injury severity of truck-involved crashes on Korean freeway system. To carry out this objective, this study uses an ordered probit model (OPM) based on a 6-year crash dataset from 2012 to 2017. From the analysis, eight variables were found to have a great effect on injury severity: older driver, crash speed, rear-end collision, number of vehicles involved, drowsy driving, nighttime (0:00 to 6:00) driving, overturn or rollover, and vehicle's fire after crash. However, injury severity was less severe in crashes under snowy condition and crashes to traffic facilities (i.e., crash alone).

• International Journal of Highway Engineering
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• v.17 no.6
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• pp.85-95
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• 2015

PURPOSES : The objective of this study is to analyze factors affecting traffic accident severity for determining countermeasures on freeway climbing lanes. METHODS : In this study, an ordered probit model, which is a widely used discrete choice model for categorizing crash severity, was employed. RESULTS : Results suggest that factors affecting traffic accident severity on climbing lanes include speed, drowsy driving, grade of uphill 3%, gender (male offender and male victim), and cloud weather. CONCLUSIONS : Several countermeasures are proposed for improving traffic safety on freeway climbing lanes based on the analysis of crash severity. More extensive analysis with a larger data set and various modeling techniques are required for generalizing the results.