日本地球惑星科学連合2016年大会

講演情報

口頭発表

セッション記号 S (固体地球科学) » S-TT 計測技術・研究手法

[S-TT55] ハイパフォーマンスコンピューティングが拓く固体地球科学の未来

2016年5月24日(火) 15:30 〜 17:00 A02 (アパホテル&リゾート 東京ベイ幕張)

コンビーナ:*堀 高峰(独立行政法人海洋研究開発機構・地震津波海域観測研究開発センター)、市村 強(東京大学地震研究所)、日野 亮太(東北大学大学院理学研究科)、有川 太郎(独立行政法人港湾空港技術研究所)、井料 隆雅(神戸大学)、座長:堀 高峰(独立行政法人海洋研究開発機構・地震津波海域観測研究開発センター)

16:00 〜 16:15

[STT55-03] 交通障害マルチスケールシミュレーション:開発の展望と課題

*井料 隆雅1 (1.神戸大学)

キーワード:交通シミュレーション

This presentation proposes an outline of the multi-scale simulation of damaged transport systems and discusses its prospects and tasks of the development. Damages on buildings and infrastructure by a major disaster will influence both performances and demand patterns of transport systems in a damaged area. The combination of degraded performances and a demand pattern that is significantly different from a normal demand can cause severe congestion, creating a major impact on the social systems of a damaged area. The multi-scale simulation of damaged transport systems aims to reproduce such situations using a traffic flow simulator and a transport demand model for a disaster situation combined with an estimated physical damages on buildings and infrastructure. Two issues should be mentioned to develop the simulation system. First, in a major disaster, an affected area should be substantially large and hence the scale of the problem (i.e. the number of links of a network and agents moving in it) must be very large. Second, the demand pattern after a disaster should be completely different from that of normal days and cannot be precisely estimated beforehand, implying that a huge number of demand patterns needs to be evaluated in the simulation. These two issues certainly arise computation burden that is very huge compared to typical problems that have been dealt with in past transport studies. The high-performance computing is useful to overcome these issues. In this presentation, the following technical topics will be introduced with a few tentative result: (1) How a traffic flow simulator is to be parallelised, (2) How travellers’ behaviour in a post-disaster network can be mathematically modelled, (3) How numerous patterns of the demands are to be sampled so that practically important cases are effectively evaluated.