| id |
caadria2019_638 |
| authors |
Willemse, Elias Jakobus, Tuncer, Bige and Bouffanais, Roland |
| year |
2019 |
| title |
Identifying Highly Dense Areas from Raw Location Data |
| doi |
https://doi.org/10.52842/conf.caadria.2019.2.805
|
| source |
M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 805-814 |
| summary |
In this paper we show how very high-volumes of raw WiFi-based location data of individuals can be used to identify dense activity locations within a neighbourhood. Key to our methods is the inference of the size of the area directly from the data, without having to use additional geographical information. To extract the density information, data-mining and machine learning techniques form activity-based transportation modelling are applied. These techniques are demonstrated on data from a large-scale experiment conducted in Singapore in which tens of thousands of school children carried a multi-sensor device for five consecutive days. By applying the techniques we were able to identify expected high-density areas of school pupils, specifically their school locations, using only the raw data, demonstrating the general applicability of the methods. |
| keywords |
; Machine Learning, Big-data, Location-analysis |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (588,687 bytes) |
| references |
Content-type: text/plain
|
Chen, N, Xie, J, Tinn, P, Nagakura, T and Larson, K (2017)
 Data Mining Tourism Patterns - Call Detail Records asComplementary Tools for Urban Decision Making
, Protocols, Flows, and Glitches: Proceedings of the22nd International Conference on Computer-AidedArchitectural Design Research in Asia (CAADRIA 2017), pp. 685-694
|
|
|
|
|
Ester, M, Kriegel, HPa and Xu, X (1996)
A density-based algorithm for discovering clusters inlarge spatial databases with noise
, Proceedings of the Second International Conference onKnowledge Discovery and Data Mining (KDD-96), pp. 226-231
|
|
|
|
|
Fakhreddine, A and Tippenhauer, NOa (2018)
Design and Large-Scale Evaluation of WiFi ProximityMetrics
, European Wireless 2018; 24th European WirelessConference, pp. 1-6
|
|
|
|
|
Gonz\'alez, MC and Hidalgo, CAa (2008)
Understanding individual human mobility patterns
, Nature, 453(7196), p. 779
|
|
|
|
|
Happle, G, Wilhelm, E and Schlueter, A (2017)
Determining air-conditioning usage patterns in Singapore from distributed, portable sensors
, Energy Procedia, 122, pp. 313-318
|
|
|
|
|
Huang, W, Lin, Y and Wu, M (2017)
Spatial-Temporal Behavior Analysis Using Big DataAcquired by Wi-Fi Indoor Positioning System
, Protocols, Flows, and Glitches: Proceedings of the22nd International Conference on Computer-AidedArchitectural Design Research in Asia (CAADRIA 2017), pp. 745-754
|
|
|
|
|
Jiang, S and Ferreira, Ja (2017)
Activity-based human mobility patterns inferred from mobile phone data: A case study of Singapore
, IEEE Transactions on Big Data, 3(2), pp. 208-219
|
|
|
|
|
Joubert, JW and Meintjes, S (2015)
Repeatability & reproducibility: Implications ofusing GPS data for freight activity chains
, Transportation Research Part B: Methodological, 76, pp. 81-92
|
|
|
|
|
Liao, L, Patterson, DJ and Fox, Da (2007)
Learning and inferring transportation routines
, Artificial Intelligence, 171(5-6), pp. 311-331
|
|
|
|
|
Monnot, B, Wilhelm, Ea, Zhou, Ya, Lu, HY and Jin, W (2016)
Inferring Activities and Optimal Trips: Lessons FromSingapore's National Science Experiment
, Complex Systems Design & Management Asia, Cham, pp. 247-264
|
|
|
|
|
Montini, L, Rieser-Schüssler, Na and Axhausen, KW (2014)
Trip purpose identification from GPS tracks
, Transportation Research Record, 2405(1), pp. 16-23
|
|
|
|
|
Pedregosa, F, Varoquaux, G, Gramfort, Aa, Thirion, B, Grisel, Oa, Prettenhofer, P, Weiss, Ra, Vanderplas, J, Passos, Aa, Brucher, M and Perrot, Ma (2011)
Scikit-learn: Machine Learning in Python
, Journal of Machine Learning Research, 12, pp. 2825-2830
|
|
|
|
|
Pinjari, AR and Bhat, CR (2011)
Activity-based travel demand analysis
, Andr\'e de Palma, RL (eds), A Handbook of Transport Economics, Edward Elgar Publishing, pp. 1-36
|
|
|
|
|
Schuessler, N and Axhausen, K (2009)
Processing raw data from global positioning systemswithout additional information
, Transportation Research Record: Journal of theTransportation Research Board, 2105, pp. 28-36
|
|
|
|
|
Tan, SB, Zegras, PCa and Arcaya, MC (2018)
Evaluating the effects of active morning commutes onstudents' overall daily walking activity inSingapore: Do walkers walk more?
, Journal of Transport & Health, 8, pp. 220-243
|
|
|
|
|
Tuncer, B, Benita, F and Tay, H (2017)
Identification of building floors in a 3D citymodel
, Smart Cities: Improving Quality of Life Using ICT &IoT (HONET-ICT), 2017 14th International Conferenceon, pp. 16-20
|
|
|
|
|
Vieira, MR, Frias-Martinez, Va and Frias-Martinez, E (2010)
Characterizing dense urban areas from mobilephone-call data: Discovery and social dynamics
, 2010 IEEE Second International Conference on SocialComputing (SocialCom), pp. 241-248
|
|
|
|
|
Wilhelm, E, MacKenzie, D, Zhou, Ya and Tippenhauer, NO (2017)
Evaluation of transport mode using wearable sensordata from thousands of students
, Proceedings of Annual Meeting of the TransportationResearch Board (TRB), pp. 1-18
|
|
|
|
|
Wilhelm, E, Siby, S, Zhou, Ya, Jayasuriya, Ma, Kee, Ja and Tippenhauer, NO (2016)
Wearable environmental sensors and infrastructure formobile large-scale urban deployment
, IEEE Sensors Journal, 16(22), pp. 8111-8123
|
|
|
|
|
Zheng, Y (2015)
Trajectory data mining: an overview
, ACM Transactions on Intelligent Systems andTechnology (TIST), 6(3), p. 29
|
|
|
|
| last changed |
2022/06/07 07:56 |
|
