Aggregate Human Mobility Using Mobile Network Big Data

DOI

10.2991/978-94-6463-126-5_7How to use a DOI?

Keywords

Aggregate Data; Human Mobility; Big Data

Abstract

Processing big data into human mobility analysis requires more in-depth stages and calculations with each consideration or assumption at the beginning of the step. Data from the Ministry of Transportation Research and Research Section with City Data for March 2022 on 15–16, Sunday and Monday as a representative of weekends and weekdays. The data received is translated into a UTM 48 S coordinate map because it is located in DKI Jakarta. The method used is processing the Python and Kepler programming languages as data visualization. The data cleansing required at the beginning removes data that is not moving and eliminates data that moves too far at speeds above 60 km/h. Data aggregation is indicated by spatial join with the zoning area. It eliminates the need to do an internal movement. Furthermore, set a dwelling time of 30 min and a travel time of 50 km/h. The movement taken is the movement of more than one activity. In one zone, the area can be represented from one sub-district area or a particular zoning area, then translated into one midpoint simultaneously. It becomes interesting when the analysis of human mobility is seen from the internal movement so that the internal movement is eliminated. By looking at the data aggregation, it can be calculated for preparing the Origin-Destination Matrix table. The condition of DKI Jakarta with calculations using the MNBD is undoubtedly a new finding because this is the initial condition of the Covid-19 pandemic coming to Indonesia. So that human mobility can be seen more clearly due to less movement due to travel restrictions. After experiencing the data aggregation process, approximately only 2.17% of the data can be used. It can be seen that the five highest zones are Gambir, Karawaci, South Bekasi, Sunter Agung, and Pondok Aren.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Okkie Putriani
AU  - Sigit Priyanto
AU  - Imam Muthohar
AU  - Mukhammad Rizka Fahmi Amrozi
PY  - 2023
DA  - 2023/03/31
TI  - Aggregate Human Mobility Using Mobile Network Big Data
BT  - Proceedings of the International Conference on Railway and Transportation (ICORT 2022)
PB  - Atlantis Press
SP  - 58
EP  - 66
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-126-5_7
DO  - 10.2991/978-94-6463-126-5_7
ID  - Putriani2023
ER  -