• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.55-63
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• 2004

기존의 설계시간순위 결정은 "순위곡선의 기울기가 완만해지는 지점"이라는 정성적인 기준을 사용하였다. 따라서, 분석자마다 서로 다른 결과를 도출하고 도로 설계시 고려해야하는 두요소(교통혼잡, 경제성)를 전혀 고려하지 못하는 문제점이 있다. 또한 현재의 도로 설계시 대상으로 삼는 시간교통량은 국내 도로의 교통특성이 설, 추석 등의 연휴에 집중적으로 몰리는 등 외국과 그 특성이 상이함에도 불구하고 미국과 동일한 상위 30순위 교통량을 사용한다. 상위 30순위 교통량을 설계시간순위로 하는 경우, 상위 30순위교통량 중 휴일교통량의 비율이 74.1%(설, 추석 연휴 39.7%)로 휴일 집중 교통량의 영향을 크게 받으며, 연중 최대교통량이 용량의 85.2% 에 불과해 도로가 과다 설계된다. 본 연구에서는 목표년도의 연중 최대시간교통량이 용량에 도달하는 순위를 설계시간순위로 하였으며, 분석결과 상위 150순위가 교통혼잡과 도로의 경제성을 모두 고려할 수 있는 설계시간순위로 산정되었다. 설계시간순위를 150순위로 할 경우 현행 설계순위인 30순위에 비해 휴일비율 13.8% 감소, 최대시간교통량의 용량비율($V_1/C_a$) 16.0% 증가의 효과가 있을 것으로 분석되었다. - 현행 설계시간순위(30순위) : 휴일비율 74.1%(설, 추석 비율 39.7%), $V_1/C_a$ 85.2% - 제안 설계시간순위(150순위) : 휴일비율 60.3%(설, 추석비율 23.0%), $V_1/C_a$ 101.2%

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.2
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• pp.52-62
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• 2013

Design Hourly Factor(DHF) is defined as the ratio of design hourly volume(DHV) to Average Annual Daily Traffic(AADT). Generally DHV used the 30th rank hourly volume. But this case DHV is affected by holiday volumes so the road is at risk for overdesigning. Computing K factor is available for counting 8,760 hour traffic volume, but it is impossible except permanent traffic counts. This study applied three method to make DHF, using 30th rank hourly volume to make DHF(method 1), using peak hour volume to make DHF(method 2). Another way to make DHF, rank hourly volumes ordered descending connect a curve smoothly to find the point which changes drastic(method 3). That point is design hour, thus design hourly factor is able to be computed. In addition road classified 3 type for national highway using factor analysis and cluster analysis, so we can analyze the characteristic of DHF by road type. DHF which was used method 1 is the largest at any other method. There is no difference in DHF by road type at method 2. This result shows for this reason because peak hour is hard to describe the characteristic of hourly volume change. DHF which was used method 3 is similar to HCM except recreation road but 118th rank hourly volume is appropriate.

• The Journal of Bigdata
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• v.3 no.2
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• pp.101-112
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• 2018

The purpose of the study is to predict the number of police calls using neural network which is one of the machine learning and negative binomial regression, by using the data of 112 police calls received from Chungnam Provincial Police Agency from June 2016 to May 2017. The variables which may affect the police calls have been selected for developing the prediction model : time, holiday, the day before holiday, season, temperature, precipitation, wind speed, jurisdictional area, population, the number of foreigners, single house rate and other house rate. Some variables show positive correlation, and others negative one. The comparison of the methods can be summarized as follows. Neural network has correlation coefficient of 0.7702 between predicted and actual values with RMSE 2.557. Negative binomial regression on the other hand shows correlation coefficient of 0.7158 with RMSE 2.831. Neural network has low interpretability, but an excellent predictability compared with the negative binomial regression. Based on the prediction model, the police agency can do the optimal manpower allocation for given values in the selected variables.

• Journal of Korean Society of Transportation
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• v.30 no.6
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• pp.3-12
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• 2012

Additive willingness-to-pay (WTP) for leisure-travel time saving focused on the Iksan-Pohang Expressway and the Donghae Expressway was surveyed during the summer months to estimate the value of travel-time savings (VTTS) for non-business leisure trips. Travelers traveling between 2 and 3 hours on Iksan-Pohang Expressway had WTP of 723 won per 10 minutes of leisure-travel time savings and those traveling between 3 and 4 hours on Donghae Expressway had WTP of 854 won per the same. Based on this survey, we learned that WTP in leisure travel time savings increased with the total travel time. 300 effective samples for each expressway were collected, and estimation was separately conducted on the basis of Cox test.

• Korean Journal of Environment and Ecology
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• v.34 no.5
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• pp.446-453
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• 2020

This study built a model to predict the daily number of visitors to 18 trails in the Taebaeksan National Park using the auto-counter system data to analyze the factors affecting the daily number of visitors to each trail and classified the trails by visitors' behaviors. Results of the multiple regression models with the daily number of visitors of the 18 trails indicated that the events, such as the National Foundation Day celebration of Snow Festival, affected the number of visitors of all of the 18 trails and were the most critical factor that determined the daily number of visitors to the Taebaeksan National Park. The long-holidays of three days or longer and other national holidays also affected the daily number of visitors to the trails. Precipitation had a negative impact on the number of visitors of trails where the intention of most visitors was for sightseeing or camping instead of hiking, whereas had no significant impacts on the number of visitors of trails where many visitors intended for hiking. It indicated that visitors who intended for hiking went ahead hiking even if the weather was poor. The effects of temperature had a positive effect on the number of visitors who intended for hiking but a negative effect on the number of visitor to the trails near Danggol Plaza where the Snow Festival was held in each winter, suggesting that the impact of the Snow Festival was the deterministic factor for trail management. Results of K-mean clustering showed that the 18 trails of the Taekbaeksan National Park could be classified into three types: those affected by the Snow Festival (type 1), those that have sightseeing points and so were visited mostly by non-hikers (type 2), and those visited mostly by hikers (type 3). Since visitor behaviors and illegal actions differ according to the trail type, this study's results can be used to prepare a trail management plan based on the trail characteristics.