Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2019

Abstract

Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantity where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this paper, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. CellTradeMap extracts robust location information from the irregularly sampled, noisy MFRs, adapts the generic trade area analysis framework to incorporate cellular data, and enhances the original trade area model with cellular-based features. We evaluate CellTradeMap on a large-scale cellular network dataset covering 3.5 million mobile phone users in a metropolis in China. Experimental results show that the trade areas extracted by CellTradeMap are aligned with domain knowledge and CellTradeMap can model trade areas with a high predictive accuracy.

Discipline

OS and Networks | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 2019 IEEE International Conference on Computer Communications, Paris, France, April 29 - May 2

First Page

937

Last Page

945

Identifier

10.1109/INFOCOM.2019.8737564

Publisher

IEEE

City or Country

Paris, France

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