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 languageEnglish
Title of host publication21st Conference on Geo-Information Science (AGILE 2018)
Place of PublicationLund, Sweden
Number of pages5
Publication statusPublished - 2018
EventAGILE 2018: 21st AGILE Conference on Geographic Information Science - Lund, Sweden
Duration: 12 Jun 201815 Jun 2018

Conference

ConferenceAGILE 2018: 21st AGILE Conference on Geographic Information Science
CountrySweden
CityLund
Period12/06/1815/06/18

    Research areas

  • location-allocation, p-median, genetic algorithm, time-varying location model, social data, traffic

ID: 45480491