id |
caadria2017_070 |
authors |
Chen, Nai Chun, Xie, Jenny, Tinn, Phil, Alonso, Luis, Nagakura, Takehiko and Larson, Kent |
year |
2017 |
title |
Data Mining Tourism Patterns - Call Detail Records as Complementary Tools for Urban Decision Making |
doi |
https://doi.org/10.52842/conf.caadria.2017.685
|
source |
P. Janssen, P. Loh, A. Raonic, M. A. Schnabel (eds.), Protocols, Flows, and Glitches - Proceedings of the 22nd CAADRIA Conference, Xi'an Jiaotong-Liverpool University, Suzhou, China, 5-8 April 2017, pp. 685-694 |
summary |
In this study we show how Call Detail Record (CDR) can be used to better understand the travel patterns of visitors. We show how Origin-Destination (OD) Interactive Maps can provide transportation information through CDR. We then use aggregation of CDR to show the differences between the travel patterns of visitors from different countries and of different lengths of stay. We also show that visitors move differently during event periods and non-event periods, reflecting the importance of real-time data available by CDR. From CDR, we can gain more detailed and complete information about how tourists move compared to traditional surveys, which can be used to aid smarter transportation systems and urban resource planning. |
keywords |
Machine Learning; Call Detail Record; Original-Destination Matrix; Urban Design Tool |
series |
CAADRIA |
email |
naichun@mit.edu |
full text |
file.pdf (2,413,719 bytes) |
references |
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|
Agung, M and Kistijantoro, I A (2016)
High Performance CDR Processing with MapReduce
, Journal of ICT Research & Applications Vol 10 Issue 2, pp 95-109
|
|
|
|
Balduini, M, Della Valle, E, Azzi, M, Larcher, R, Antonelli, F and Ciuccarelli, P (2015)
CitySensing: Fusing City Data for Visual Storytelling
, IEEE MultiMedia, 22:3, pp 44-53
|
|
|
|
Breiman, L (2001)
Random Forests
, Machine Learning, 45:1, pp 5-32
|
|
|
|
Calabrese, F, Diao, M, Lorenzo, G, Ferreira, J and Ratti, C (2013)
Understanding individual mobility patterns from urban sensing data: A mobile phone trace example
, Transportation Research Part C: Emerging Technologies, 26, pp 301-313
|
|
|
|
Chua, A and Servillo, L (2016)
Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy
, Tourism Management, 57, pp 295-310
|
|
|
|
Eagle, N and Pentland, A S (2009)
Eigenbehaviors: identifying structure in routine
, Behavioural Ecology and Sociobiology, 63(7), pp 1057-1066
|
|
|
|
Hoteit, S, Secci, S, Sobolevsky, S, Ratti, C and Pujolle, G (2014)
Estimating human trajectories and hotspots through mobile phone data
, Computer Networks, 64, pp 296-307
|
|
|
|
Iqbal, MS, Choudhury, C, Wang, P and Gonz?°lez, M (2014)
Development of origin-destination matrices using mobile phone cell data
, Transportation Research Part C: Emerging Technologies, 40, pp 63-74
|
|
|
|
Jiang, S, Ferreira, J and Gonz?°lez, M C (2016)
Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore
, IEEE Transactions on Big Data, 99, pp 1-1
|
|
|
|
Jones, GA and Warner, KJ (2016)
The 21st century population-energy-climate nexus
, Energy Policy, 93, pp 206-212
|
|
|
|
Liu, F, Janssens, D, Wets, G and Cools, M (2013)
Annotating mobile phone location data with activity purposes using machine learning algorithms
, Expert Systems with Applications: An International Journal, 40:9, pp 3299-3311
|
|
|
|
Monasterio, J, Salles, A, Lang, C, Weinberg, D, Minnoni, M, Travizano, M and Sarraute, C (2016)
Analyzing the spread of chagas disease with mobile phone data
, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
|
|
|
|
Montjoye, YA, Quoidbach, J, Robic, F and Pentland, A (2013)
Predicting Personality Using Novel Mobile Phone-Based Metrics
, Social Computing, Behavioral-Cultural Modeling and Prediction, 7812, pp 48-55
|
|
|
|
Semertzidis, T (2015)
Can Energy Systems Models Address the Resource Nexus?
, Energy Procedia, 83, pp 279-288
|
|
|
|
Toole, J, Colak, S, Strut, B, Alexander, L, Evsukoff, A and Gonz?°lez, MC (2015)
The path most traveled: Travel demand estimation using big data resources
, Transportation Research Part C: Emerging Technologies, 58, pp 162-177
|
|
|
|
White, J, Quick, J and Philippou, P (2004)
The use of mobile phone location data for traffic information
, IEE International Conference on Road Transport Information and Control
|
|
|
|
last changed |
2022/06/07 07:55 |
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