Abstract
Location models have traditionally played an important role in suggesting sites for the placement of facilities, so that efficient service delivery is ensured. A common formulation of several location models is associated with the p-median problem, which aims to minimize the travel distance between support facilities and demand in a region. However, the influence of external conditions, such as traffic, on travel time is largely ignored. In this paper, we present a time-varying approach to the classical p-median problem, which accounts for fluctuations in travel cost distance at different time intervals. Using Google Traffic and Foursquare data to respectively retrieve traffic information and estimate demand in a region, and by employing an adaptive genetic algorithm in a planning problem application in the Netherlands, we show that our proposed model outperforms the classical p-median formulation, in providing more travel efficient service of demand nodes. Moreover, we achieve better placement of support facilities across major street arteries. The paper concludes with a discussion of associated uncertainties that are important to be recognized prior to viewing the modeling results as suggestions for implementation in planning and policy making.
Original language | English |
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Title of host publication | 21st Conference on Geo-Information Science (AGILE 2018) |
Place of Publication | Lund, Sweden |
Pages | 1-5 |
Number of pages | 5 |
Publication status | Published - 2018 |
Event | AGILE 2018: 21st AGILE Conference on Geographic Information Science - Lund, Sweden Duration: 12 Jun 2018 → 15 Jun 2018 |
Conference
Conference | AGILE 2018: 21st AGILE Conference on Geographic Information Science |
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Country/Territory | Sweden |
City | Lund |
Period | 12/06/18 → 15/06/18 |
Bibliographical note
Accepted Author ManuscriptKeywords
- location-allocation
- p-median
- genetic algorithm
- time-varying location model
- social data
- traffic