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A time-varying p-median model for location-allocation analysis. / Sharifi Noorian, Shahin; Psyllidis, Achilleas; Bozzon, Alessandro.

21st Conference on Geo-Information Science (AGILE 2018). Lund, Sweden, 2018. p. 1-5.

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Sharifi Noorian, S, Psyllidis, A & Bozzon, A 2018, A time-varying p-median model for location-allocation analysis. in 21st Conference on Geo-Information Science (AGILE 2018). Lund, Sweden, pp. 1-5, AGILE 2018: 21st AGILE Conference on Geographic Information Science, Lund, Sweden, 12/06/18.

APA

Sharifi Noorian, S., Psyllidis, A., & Bozzon, A. (2018). A time-varying p-median model for location-allocation analysis. In 21st Conference on Geo-Information Science (AGILE 2018) (pp. 1-5). Lund, Sweden.

Vancouver

Sharifi Noorian S, Psyllidis A, Bozzon A. A time-varying p-median model for location-allocation analysis. In 21st Conference on Geo-Information Science (AGILE 2018). Lund, Sweden. 2018. p. 1-5

Author

Sharifi Noorian, Shahin ; Psyllidis, Achilleas ; Bozzon, Alessandro. / A time-varying p-median model for location-allocation analysis. 21st Conference on Geo-Information Science (AGILE 2018). Lund, Sweden, 2018. pp. 1-5

BibTeX

@inproceedings{66170ae39fdb440a89b7e498e3396497,
title = "A time-varying p-median model for location-allocation analysis",
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.",
keywords = "location-allocation, p-median, genetic algorithm, time-varying location model, social data, traffic",
author = "{Sharifi Noorian}, Shahin and Achilleas Psyllidis and Alessandro Bozzon",
note = "Accepted Author Manuscript",
year = "2018",
language = "English",
pages = "1--5",
booktitle = "21st Conference on Geo-Information Science (AGILE 2018)",

}

RIS

TY - GEN

T1 - A time-varying p-median model for location-allocation analysis

AU - Sharifi Noorian, Shahin

AU - Psyllidis, Achilleas

AU - Bozzon, Alessandro

N1 - Accepted Author Manuscript

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - location-allocation

KW - p-median

KW - genetic algorithm

KW - time-varying location model

KW - social data

KW - traffic

M3 - Conference contribution

SP - 1

EP - 5

BT - 21st Conference on Geo-Information Science (AGILE 2018)

CY - Lund, Sweden

ER -

ID: 45480491