Journal of Korean Society of Transportation (대한교통학회지)

Korean Society of Transportation (대한교통학회)
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Artificial Intelligence Estimation of Network Flows for Seismic Risk Analysis

지진 위험도 분석에서 인공지능모형을 이용한 네트워크 교통량의 예측

  • Published : 1999.08.01
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Abstract

Earthquakes damage roadway bridges and structures, resulting in significant impacts on transportation system Performance and regional economy. Seismic risk analysis (SRA) procedures establish retrofit priorities for vulnerable highway bridges. SRA procedures use average daily traffic volumes to determine the relative importance of a bridge. This research develops a cost-effective transportation network analysis (TAN) procedure for evaluating numerous traffic flow analyses in terms of the additional system cost due to failure. An important feature of the TNA Procedure is the use of an associative memory (AM) approach in the artificial intelligence held. A simple seven-zone network is developed and used to evaluate the TNA procedure. A subset of link failure system states is randomly selected to simulate synthetic post-earthquake network flows. The performance of different AM model is evaluated. Results from numerous link-failure scenarios demonstrate the applicability of the AM models to traffic flow estimation.

지진은 교량과 도로 구조물들에 피해를 입혀서 교통시스템 운행전반과 지역경제에 심각한 피해를 초래하도록 한다. 지진위험도 분석방법은 안전도에 문제가 있는 고속도로 교량들에 대하여 구조물 보강공사를 실시하기 위한 우선순위 결정에 사용되어 진다. 지진위험도 분석방법들은 한 교량의 상대적 중요도를 결정하기 위하여 일일 평균교통량을 사용하고 있다. 본 연구는 도로붕괴시 교통시스템에 추가로 부과되는 시스템비용의 관점에서 수많은 교통량분석을 실행하는데 비용-효과적인 교통망 분석방법을 개발하는데 있다. 본 연구에서 개발된 교통망 분석방법의 핵심은 인공지능분야에서 개발된 연상기억모형의 사용이다. 본 연구에서 개발된 교통망 분석방법을 평가하기 위하여 7개의 교통죤으로 구성된 교통망이 구축되었다. 다양한 교통링크 붕괴 시나리오들이 지진으로 붕괴된 교통망에서의 교통량들을 추정하기 위하여 무작위로 선정되었다. 이러한 교통링크 붕괴 시나리오에 대한 교통량의 변화는 여러 연상기억모형들을 이용하여 예측하였고, 그 예측능력을 평가하였다. 다양한 시나리오로부터의 예측결과는 교통량 예측분야에서 연상기억 모형들의 적용 가능성을 보여주고 있다.

Keywords

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