Multi-criteria spatial decision support system for identifying strategic roads in disaster situations

Abstract

A road transportation network has an important role in the management of disaster situations. Despite its' vulnerability to disasters, it helps to provide emergency responses to disaster management practices. Therefore it is required to identify the most important roads in the network in order to support decision-makers to make appropriate decisions about the roads.This paper introduces an integrated methodology to evaluate the transportation network performance (TNP) in disaster situations by developing a multi-criteria spatial decision support system (MC-SDSS). The developed MC-SDSS is a fully integrated system of Geospatial Information System (GIS) and Multi-Criteria Decision Making (MCDM) methods. On the one hand, GIS functionalities are used for storing the data, performing the analyses in order to produce the required criteria and displaying the results. On the other hand, AHP as a well-known MCDM method is used to receive priorities and preferences of decision-makers about the criteria.Based on the decision-making model (intelligent, design, and choice), four criteria are selected as indicators for evaluating the TNP in disaster situations: capacity, accessibility, vulnerability, and importance criteria. In this regard, criteria maps are generated by GIS tools, the experts' preferences about the criteria are acquired by AHP comparison matrix, and a ranking of the roads are prepared and visualized on the MC-SDSS. Finally, by utilizing the One-At-Time approach as the sensitivity analysis method, MC-SDSS tries to determine the robustness of the results due to the variation or uncertainty resulting from changing the important scales of the criteria in the AHP pairwise comparison matrix.The results show that about 9, 33, 20, and 38% of the roads are very high, high, moderate, and low strategic in the case study (Mazandaran province, Iran) respectively. It also shows that capacity/accessibility pairwise comparison is the most sensitive comparison in the AHP comparison matrix.