• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.13-22
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• 2017

The present study proposes a new methodology for predicting the demand for truck rest areas beside national highways. Previous demand forecasting methods were reviewed first in order to complement them with additional items. The results of the study are as follows. In the demand forecasting process, the primary task is to divide parking demands of trucks into short-term parking and long-term parking. Since short-term and long-term parking vary in utilization, congestion, and turnover rate, different influence factors should be considered according to parking time. Furthermore, the demand characteristics of rest and convenience facilities need to be reflected as well, because they in turn affect the demand for truck rest areas. In sum, the demand forecasting process for destination-type truck rest areas on national highways requires more attention than that for truck rest areas on expressways, and possible influences of various factors should be examined in this process.

• Korean Architects
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• no.11 s.367
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• pp.34-35
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• 1999

• The tire
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• s.228
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• pp.27-29
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• 2006

우리협회에서는 고속도로상에서 타이어 정비불량으로 인한 교통사고를 예방하고 올바른 사용방법을 홍보하고자 고속도로 휴게소에서 업계와 합동으로 타이어 안전사용검사 및 서비스 행사를 매년 실시하고 있다 본고에서는 금년 7월 검사까지의 추이를 중심으로, 일본의 타이어 안전사용 실태와 비교할 수 있도록 일본 자동차 타이어 공업협회(Japan Automobile Tire Manufacturers Association)의 05년도 안전검사 결과 수치를 삽입하였다. 참고로 JATMA의 안전검사는 고속도로뿐만 아니라 일반도로에서도 이루어지며 검사대상에서도 화물차를 포함하지만 ,본고에서는 고속도로에서의 승용차 대상의 결과치 만을 발췌하였다. 아울러, 올해 상반기에 실시한 운전자의 타이어 안전관리에 관한 설문조사 결과를 요약, 정리하였다.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.83-106
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• 2021

Interests in clean fuels have been soaring because of environmental problems such as air pollution and global warming. Unlike fossil fuels, hydrogen obtains public attention as a eco-friendly energy source because it releases only water when burned. Various policy efforts have been made to establish a hydrogen based transportation network. The station that supplies hydrogen to hydrogen-powered trucks is essential for building the hydrogen based logistics system. Thus, determining the optimal location of refueling stations is an important topic in the network. Although previous studies have mostly applied optimization based methodologies, this paper adopts machine learning to review spatial attributes of candidate locations in selecting the optimal position of the refueling stations. Machine learning shows outstanding performance in various fields. However, it has not yet applied to an optimal location selection problem of hydrogen refueling stations. Therefore, several machine learning models are applied and compared in performance by setting variables relevant to the location of highway rest areas and random points on a highway. The results show that Random Forest model is superior in terms of F1-score. We believe that this work can be a starting point to utilize machine learning based methods as the preliminary review for the optimal sites of the stations before the optimization applies.