Agent Based Model in SAS Environment for Rail Transit System Alignment Determination - I Made Indradjaja Brunner (Jurnal Wilayah dan Lingkungan, 2018)
Transit system had been proposed for the urban area of Honolulu. One consideration to be determined is the alignment of the transit system. Decision to set the transit alignment will have influences on which areas will be served, who will be benefiting, as well as who will be impacted. Inputs for the decision usually conducted through public meetings, where community members are shown numbers of maps with pre-set routes. That approach could lead to a rather subjective decision by the community members. This paper attempts to discuss the utilization of grid map in determining the best alignment for rail transit system in Honolulu, Hawaii. It tries to use a more objective approach using various data derived from thematic maps. Overlaid maps are aggregated into a uniform 0.1-square mile vector based grid map system in GIS environment. The large dataset in the GIS environment is analyzed and manipulated using SAS software. The SAS procedure is applied to select the location of the alignment using a rational and deterministic approach. Grid cells that are superior compared to the others are selected based on several predefined criteria. Location of the dominant cells indicates possible transit alignment. The SAS procedure is designed to allow a transient vector called the GUIDE (Grid Unit with Intelligent Directional Expertise) agent to analyze several cells at its vicinity and to move towards a cell with the highest value. Each time the agent landed on a cell, it left a mark. The chain of those marks shows location for the transit alignment. This study shows that the combination of ArcGIS and SAS allows a robust analysis of spatial data and manipulation of its datasets, which can be used to run a simulation mimicking the Agent-Based Modelling. This study also opens up further study possibilities by increasing number of factors analyzed by the agent, as well as creating a composite value of multi-factors.