Aggregating Data for the Flow‐Intercepting Location Model: A Geographic Information System, Optimization, and Heuristic Framework. 截流选址模型的数据集计: 一个地理信息系统、优化和探索性框架

Abstract

Flow‐intercepting problems have received considerable interest, represented by about 40 academic publications, since the early 1990s. Point‐based demand aggregation also has received much research interest in both industry and academia. Systematic studies of flow data aggregation for flow‐intercepting problems have not, however, been reported to date. Our research highlights the importance of flow‐based demand aggregation and develops a framework for aggregating such demand. This framework represents the first systematic study of aggregation for flow‐intercepting location models (FILM). The standard FILM is the perfect model for our goals—its aggregation errors are easy to understand and its outputs are easy to measure and compare. Our research uses geographic information systems, optimization, and heuristic technologies to examine the special network flow structure of a real‐world transportation system and to develop a comprehensive method of aggregating data for the standard FILM. We apply our method to the 2001 afternoon peak traffic data for Edmonton, Alberta (the sixth largest Canadian city) and find this application to be extremely efficient. We discover that in the Edmonton traffic flow network, a large number of paths have very small flows; major flows are concentrated in a limited number of paths; and a large number of small‐flow paths and a large number of low‐flow nodes on local streets have negligible effects on facility locations for FILM. We speculate that most real‐world transportation systems may have similar characteristics.