Estimation of Hourly Link Population and Flow Directions from Mobile CDR

Ko Lwin,Wataru Takeuchi,Yoshihide Sekimoto

doi:10.3390/ijgi7110449

Ko Lwin, Wataru Takeuchi + Show 1 more

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https://doi.org/10.3390/ijgi7110449

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Abstract

The rise in big data applications in urban planning and transport management is now widening and becoming a part of local government decision-making processes. Understanding people flow inside the city helps urban and transport planners build a healthy and lively city. Many flow maps are based on origin-and-destination points with crossing lines, which reduce the map’s readability and overall appearance. Today, with the emergence of geolocation-enabled handheld devices with wireless communication and networking capabilities, human mobility and the resulting events can be captured and stored as text-based geospatial big data. In this paper, we used one-week mobile-call-detail records (CDR) and a GIS road network model to estimate hourly link population and flow directions, based on mobile-call activities of origin–destination pairs with a shortest-path analysis for the whole city. Moreover, to gain the actual population size from the number of mobile-call users, we introduced a home-based magnification factor (h-MF) by integrating with the national census. Therefore, the final output link data have both magnitude (actual population) and flow direction at one-hour intervals between 06:00 and 21:00. The hourly link population and flow direction dataset are intended to optimize bus routes, solve traffic congestion problems, and enhance disaster and emergency preparedness.

Highlights

IntroductionInformation on human mobility (both magnitude and direction) inside the city is important in urban and transport planning, such as bus route planning and optimization [1], trip frequency scheduling [2], transportation modes prediction [3], traffic congestion management [4], public facility management, and disaster and emergency preparedness [5], in order to build a healthy and lively city
Information on human mobility inside the city is important in urban and transport planning, such as bus route planning and optimization [1], trip frequency scheduling [2], transportation modes prediction [3], traffic congestion management [4], public facility management, and disaster and emergency preparedness [5], in order to build a healthy and lively city
call-detail records (CDR) has some limitation on data acquisition because of privacy protection, CDR is one means to identify the mass movement of people inside a city or across the country inexpensively and time-effectively

Summary

Introduction

Information on human mobility (both magnitude and direction) inside the city is important in urban and transport planning, such as bus route planning and optimization [1], trip frequency scheduling [2], transportation modes prediction [3], traffic congestion management [4], public facility management, and disaster and emergency preparedness [5], in order to build a healthy and lively city. Traditional ways of acquiring people flow inside the city are paper-based travel surveys or other transport statistics, which are expensive and labor intensive. Human mobility and activities can be tracked using mobile phone call activities (call-detail record, CDR), internet usage, and other social interacting events through online social networks (OSN) and other wireless sensor networks (WSNs). CDR data have been used by many researchers for origin–destination trip generation [6,7,8,9,10,11], travel behavior analysis [12,13,14], social interaction [15,16], urban analysis [17], and population estimation [18,19]

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