Gonçalo Caiado, Rosário Macário, & Carlos Sousa Oliveira. (2011). A new paradigm in urban road network seismic vulnerability: From a link-by-link structural approach to an integrated functional assessment. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Other than the direct exposure of a seismic event, the interruption of the transportation network causes an indirect exposure of the population living in stricken areas. In spite of such evidences, current planning practices rarely address road network seismic risk concerns beyond the typical structural link-by-link approach. The underlying hypothesis of the current research work is that, when facing a major earthquake, the impacts on road networks performance for emergency response functions can be minimized namely by the introduction of measures, not only in terms of infra-structural reinforcement but also in terms of network connectivity and activities location. Potential applications of this work include urban planning micro and macro scale solutions to be included in specific instruments (such as urban master plans or emergency plans). Additionally, the proposed method may be integrated in loss estimation models, which still do not include earthquake losses due to inaccessibility.
|
Toshihiro Osaragi. (2015). Spatiotemporal Distribution of Automobile Users: Estimation Method and Applications to Disaster Mitigation Planning. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: When discussing human casualties from a severe earthquake with regard to urban disaster mitigation planning, it is important to clarify the characteristics of the spatiotemporal distribution of people. In this paper, we construct a model that estimates the spatiotemporal distribution of automobile users using data from the Person Trip Survey and the Road Traffic Census. We use this model to estimate the spatiotemporal distribution of automobile users in Tokyo and demonstrate several ways to apply this data to urban disaster mitigation planning.
|