• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.161-171
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• 2023

It is largely agreed that elderly drivers (over 64 years) are more likely to cause fatal crashes than other age groups. According to national road crash statistics 2021, the number of road fatalities per 10,000 drivers over 64 years old was 1.77, while that of drivers in their 30s was 0.55. This indicates a 2.67 times higher probability of causing crashes among the former than the latter. The current study estimates how rear-end crashes may be reduced by installing Automatic Emergency Braking Systems (AEBS), particularly for elderly drivers. We analyzed data from Samsung Fire & Marine Insurance. The results show that the Odds Ratio of rear-end crash occurrence between vehicles with AEBS and without AEBS is 0.75, implying there were lesser rear-end crashes in the vehicles installed with AEBS. The Odds Ratio of male drivers was determined to be 0.78, which was lesser than the 0.81 Odds Ratio obtained for female drivers. Elderly drivers who had installed AEBS in their vehicles showed an Odds Ratio of 0.76, implying crash reduction. In particular, the Odds Ratio of male elderly drivers was found to be the lowest at 0.49. We believe incentivizing by giving discounted insurance premiums to the elderly who drive vehicles installed with AEBS will help reduce rear-end crashes.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.25-31
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• 2021

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.213-222
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• 2018

This study was conducted to derive the traffic accident risk index through the recognition of the elderly driver's driving pattern to reduce the traffic accident rate of elderly drivers and to reflect them in the renewal and return policy of driver's license accordingly. First, the traffic accident risk index is defined by analyzing the behavioral characteristics of older drivers to derive the major factors that lead to traffic accidents. Second, we present a method to measure the traffic accident risk index from the driving pattern of the elderly through the smart-phone, the camera and the distance sensor attached to the car. Finally, we derive three thresholds by computer simulation and determine the accident risk from the measured traffic accident risk index as four steps and suggest ways to ensure safe driving of older drivers. It is required to objectively assess the driving ability of an aged driver in accordance with the proposed method, and to induce the driver to reset the driver's license renewal cycle and voluntarily return the driver's license to minimize social costs due to increased traffic accidents.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2017.11a
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• pp.247-248
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• 2017

교통사고는 인적피해, 물적피해, 사회적피해와 거시적으로는 심리적 피해까지 일으키며 많은 비용을 발생시킨다. 이처럼 교통사고로 인해 발생하는 다양한 피해요소를 비용으로 환산하여 추정하는 것은 교통안전사업의 절감편익을 산정하거나 기타 교통관련사업의 효과를 적용하기 위하여 활용할 수 있다. 하지만 현재까지 이루어진 사고비용 산출에 대한 연구론은 1당사자와 2당사자 구분없이 사고 전체를 아우르는 피해비용을 산출하는 방식의 접근 방법과, 사상자 1인 기준으로 발생되는 피해비용을 산출하는 방식의 접근 방법으로 나뉘며, 두 접근방법 모두 비용분석의 대상을 단일 객체로 간주하고 분석하는 한계를 지니고 있다. 이에 본 연구에서는 특정한 상호작용효과가 있을 것으로 예상되는 고연령 구간을 확인한 뒤 고연령 구간과 전체 연령 구간 사이에서 발생된 사고를 비교분석하여 1당사자와 2당사자가 모두 고연령 구간일 때 추가로 발생되는 사고비용을 산출 및 검정함으로서 고령자 사고감소효과 및 절감편익에 활용될 수 있는 상호작용 효과에 따른 추가 절감편익을 산출하였다.

• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.474-481
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• 2019

Korea entered the aged society in 2018 with the elderly population accounting for 14.4% of the total population, and it is expected to enter the super-aged society in 2026. In particular, it is predicted that by 2050, the elderly population will be 38% of the total population, making it one of the countries with the highest number of elderly people in the world. The increase in the elderly population is naturally leading to an increase in the number of traffic accidents among elderly drivers, in 2017, there were 26,713 elderly driver accidents over 65 years of age, with 848 people dying and 38,627 injured. Compared with 2011, the number of accidents and injuries has doubled and the number of deaths has increased 1.4 times. This study determined that the main factors of the increase in traffic accidents were the characteristics of elderly drivers, such as a decrease in visual/hearing ability, cognitive and information processing ability, and muscle strength. Therefore, it raised the necessity of autonomous vehicle(level 2) for elderly driver who can minimize the burden of driving and aimed to study the function of autonomous vehicle for elderly driver who is not familiar with new technology. Based on this, four functions of autonomous vehicles for elderly drivers were derived, such as providing clear information according to the road environment, considering physical characteristics of drivers, simplifying interface, and reinforcing in-vehicle safety devices.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.134-145
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• 2024

The purpose of this study was to propose a framework for developing test scenarios for issuance of conditional driver's licenses. The framework was composed of five stages. Initially, we reviewed the literature on traffic crash characteristics in terms of accident frequency and severity regarding the main factors of crashes caused by older drivers. In the second stage, the characteristics of crashes attributed to non-elderly, early elderly, and late elderly drivers were analyzed using data obtained from the Traffic Accident Analysis System (TAAS), and crash types for elderly drivers were derived. In the third stage, black box videos of high-risk crash types were analyzed to derive crash stories that described the circumstances in which crashes occurred. In the fourth step, crash situations were classified by rating the types of crash stories derived to develop various scenarios. Step 5 involved creating a scenario by applying the PEGASUS 5-Layer format, which has recently been used to develop test scenarios for autonomous vehicles. The results of this study are expected to be used as a basis for developing driving ability evaluation scenarios for the issuance of conditional driver's licenses.

• International Journal of Highway Engineering
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• v.11 no.4
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• pp.33-40
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• 2009

This study deals with the traffic accidents of elderly drivers. The objectives are to comparatively analyze the characteristics of accident between the elderly and other drivers, and to develop the models of traffic accidents. In pursuing the above, this paper gives particular attentions to testing the differences between the above two groups, and developing the models(Poisson and negative binomial regression) using the data of Cheongju 4-legged signalized intersections. The main results analyzed are as follows. First, the differences between the elderly and other drivers' accidents were clearly defined by the time of day, accident type, etc. Second, 3 accident models which were all statistically significant were developed. Finally, the differences between elderly and other drivers' models were comparatively analyzed using the common and specific variables.

• Transportation Technology and Policy
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• v.7 no.1
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• pp.13-20
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• 2010

• Transportation Technology and Policy
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• v.10 no.5
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• pp.11-25
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• 2013

• Journal of Korean Society of Transportation
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• v.30 no.6
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• pp.37-46
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• 2012

The number of traffic accidents caused by elderly drivers over the age of 65 has surged over the past ten years from 37,000 to 274,000 cases. The proportion of elderly drivers' accidents has jumped 3.1 times from 1.2% to 3.7% out of all traffic accidents, and traffic safety organizations are pursuing diverse measures to address the situation. Above all, connecting safety measures with an in-depth research on behavioral and physical characteristics of elderly drivers will prove vital. This study conducted an empirical research linking the driving characteristics and traffic accidents by elderly drivers based on the Driving Aptitude Test items and traffic accident data, which enabled the measurement of behavioral characteristics of elderly drivers. In developing the Influence Model, we applied the zero-inflated Poisson (ZIP) regression model and selected an accident prediction model based on the Bayesian Influence in regards to the ZIP regression model and the zero-inflated negative binomial (ZINB) regression model. According to the results of the AAE analysis, the ZIP regression model was more appropriate and it was found that three variables? prediction of velocity, diversion, and cognitive ability? had a relation of influence with traffic accidents caused by elderly drivers.