PURPOSES : This study aims to analyze the causes of pedestrian traffic accidents on community roads. METHODS : This study collected variables affecting pedestrian traffic accidents on community roads based on field surveys and analyzed them using negative binomial regression and zero-inflated negative binomial regression models. RESULTS : Model analysis results showed that the negative binomial regression model is more suitable than the zero-inflation negative binomial regression model. Additionally, the segment length (m), pedestrian volume (persons/15 min), traffic volume (numbers/15 min.), the extent of illegal parking, pedestrian-vehicle conflict ratio, and one-way traffic (one: residential, two: commercial) were found to influence pedestrian traffic accidents on community roads. Model fitness indicators, comparing actual values with predicted values, showed an MPB of 1.54, MAD of 2.57, and RMSE of 7.03. CONCLUSIONS : This study quantified the factors contributing to pedestrian traffic accidents on community roads by considering both static and dynamic elements. Instead of uniformly implementing measures, such as pedestrian priority zones and facility improvements on community roads, developing diverse strategies that consider various dynamic factors should be considered.

PURPOSES :In this study, we analyzed the road crossing behavior of older pedestrians on a mid-block signalized crosswalk, and compared it to that of younger pedestrians. In addition, we analyzed the correlation between accidents involving older pedestrians while crossing roads and their behavioral characteristics. Finally, we confirmed the reasons for an increase in accidents involving older pedestrians.METHODS :First, 30 areas with the highest incidence of accidents involving older pedestrians while crossing roads were selected as target areas for analysis. Next, we measured the start-up delay (the time elapsed from the moment the signal turns green to the moment the pedestrian starts walking) and head movement (the number of head turns during crossing a road) of 900 (450 older and 450 younger) pedestrians. The next step was to conduct a survey and confirm the differences in judgment between older and younger pedestrians about approaching vehicles. Finally, we analyzed the correlation between the survey results and traffic accidents.RESULTS :The average start-up delay and head movement of the older pedestrians was 1.58 seconds and 3.15 times, respectively. A definite correlation was obtained between head movement and the frequency of pedestrian traffic accidents. The results of our survey indicate that 17.3% of the older pedestrians and 7.8% of the younger pedestrians have a high crash risk.CONCLUSIONS :Behavioral characteristics of older pedestrians were closely correlated with accidents involving older pedestrians while crossing roads in mid-block signalized crosswalks. Our study indicates that in order to reduce the number of accidents involving older pedestrians, it is necessary to develop an improvement plan including measures such as installation of safety facilities taking the behavioral characteristics of older pedestrians into consideration and their safety education.

PURPOSES: This study deals with the pedestrian accidents in case of Cheongju. The goals are to develop the pedestrian accident model. METHODS: To analyze the accident, count data models, truncated count data models and Tobit regression models are utilized in this study. The dependent variable is the number of accident. Independent variables are traffic volume, intersection geometric structure and the transportation facility. RESULTS : The main results are as follows. First, Tobit model was judged to be more appropriate model than other models. Also, these models were analyzed to be statistically significant. Second, such the main variables related to accidents as traffic volume, pedestrian volume, number of Entry/exit, number of crosswalk and bus stop were adopted in the above model. CONCLUSIONS: The optimal model for pedestrian accidents is evaluated to be Tobit model.

본 연구에서는 경찰청에서 제공한 2006년 전국 37,589개의 보행자 사고 자료에서 나타난 보행자 사고 심각도를 분석했다. 이를 위해 본 연구에서는 보행자사고 심각도에 영향을 미칠 것으로 예상되는 주요 요인을 미리 선정하여, 그 영향을 순서형 로짓 모형(Ordered Logit Model)을 통해 분석했다. 이모형은 사고 심각도와 같이 종속변수가 순서형으로 표시되는 경우 매우 유용한 결과를 제시하는 것으로 알려져 있어서 본 연구에서 사용했다. 본 연구는 다음과 같은 결과를 나타냈다. 첫째, 우리나라 보행자 사고 심각도는 운전자와 보행자로 나타나는 인적요인에서 성별, 나이, 음주여부에 영향을 많이 받는다. 둘째, 기타 요인에는 차량, 도로기하구조, 날씨, 시간대가 포함된다. 셋째, 보행자 사고 심각도는 계절요인과 무관하다. 끝으로, 보행자 사고 심각도 분석을 위해 순서형 로짓 모형을 적용하면 매우 적절한 분석결과를 얻을 수 있다. 결론적으로 본 연구는 우리나라 보행자 사고 심각도는 사람들이 전통적으로 생각하는 것과 같은 방식인 것으로 확인했다. 이 결론은 본 연구에서 사용한 사고자료에 국한한 것이며, 이 결론을 일반화시키려면 추후 연구가 필요하다.