Classifying Passenger and Non-passenger Signals in Public Transportation by Analysing Mobile Device Wi-Fi Activity
Author:
Affiliation:
1. Graduate School of Information Science and Engineering, Ritsumeikan University
2. College of Information Science and Engineering, Ritsumeikan University
Publisher
Information Processing Society of Japan
Subject
General Computer Science
Link
https://www.jstage.jst.go.jp/article/ipsjjip/27/0/27_25/_pdf
Reference19 articles.
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2. [2] Oransirikul, T., Nishide, R., Piumarta, I. and Takada, H.: Feasibility of Analyzing Wi-Fi Activity to Estimate Transit Passenger Population, 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), pp.362-369 (online), DOI: 10.1109/AINA.2016.68 (2016).
3. [3] Lim, W.L.H., Lum, J.T.W., Yeo, I.J.W. and Keoh, S.L.: A crowd-assisted real-time public transport information service: No more endless wait, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp.1-6 (online), DOI: 10.1109/PERCOMW.2016.7457088 (2016).
4. [4] Chen, P., Chen, H., Zhang, C. and Tang, Z.: Real-time bus passenger flow statistics scheme based on light-sensitive wireless sensor network, 2016 35th Chinese Control Conference (CCC), pp.8490-8494 (online), DOI: 10.1109/ChiCC.2016.7554712 (2016).
5. [5] Chen, C.H., Chang, Y.C., Chen, T.Y. and Wang, D.J.: People Counting System for Getting In/Out of a Bus Based on Video Processing, 2008 8th International Conference on Intelligent Systems Design and Applications, Vol.3, pp.565-569 (online), DOI: 10.1109/ISDA.2008.335 (2008).
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