Estimating Congestion in a Fixed-Route Bus by Using BLE Signals

被引:6
作者
Kanamitsu, Yuji [1 ,2 ]
Taya, Eigo [1 ,2 ]
Tachibana, Koki [1 ]
Nakamura, Yugo [3 ,4 ,5 ]
Matsuda, Yuki [1 ,2 ,5 ]
Suwa, Hirohiko [1 ,2 ]
Yasumoto, Keiichi [1 ,2 ]
机构
[1] Nara Inst Sci & Technol, Grad Sch Sci & Technol, Nara 6300192, Japan
[2] RIKEN Ctr Adv Intelligence Project AIP, Tokyo 1030027, Japan
[3] Kyushu Univ, Grad Sch, Fukuoka 8190395, Japan
[4] Kyushu Univ, Fac Informat Sci & Elect Engn, Fukuoka 8190395, Japan
[5] JST PRESTO, Tokyo 1020076, Japan
基金
日本学术振兴会;
关键词
people counting; crowd density; BLE; route bus; machine learning; CROWD DENSITY-ESTIMATION; SYSTEM;
D O I
10.3390/s22030881
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Information on congestion of buses, which are one of the major public transportation modes, can be very useful in light of the current COVID-19 pandemic. Because it is unrealistic to manually monitor the number of riders on all buses in operation, a system that can automatically monitor congestion is necessary. The main goal of this paper's work is to automatically estimate the congestion level on a bus route with acceptable performance. For practical operation, it is necessary to design a system that does not infringe on the privacy of passengers and ensures the safety of passengers and the installation sites. In this paper, we propose a congestion estimation system that protects passengers' privacy and reduces the installation cost by using Bluetooth low-energy (BLE) signals as sensing data. The proposed system consists of (1) a sensing mechanism that acquires BLE signals emitted from passengers' mobile terminals in the bus and (2) a mechanism that estimates the degree of congestion in the bus from the data obtained by the sensing mechanism. To evaluate the effectiveness of the proposed system, we conducted a data collection experiment on an actual bus route in cooperation with Nara Kotsu Co., Ltd. The results showed that the proposed system could estimate the number of passengers with a mean absolute error of 2.49 passengers (error rate of 38.8%).
引用
收藏
页数:15
相关论文
共 34 条
  • [1] Abedi N., 2013, P IEEE POW EN SOC GE, P1
  • [2] [Anonymous], 2014, P 2014 ACM INT S WEA
  • [3] A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers
    Aronson, Myla F. J.
    La Sorte, Frank A.
    Nilon, Charles H.
    Katti, Madhusudan
    Goddard, Mark A.
    Lepczyk, Christopher A.
    Warren, Paige S.
    Williams, Nicholas S. G.
    Cilliers, Sarel
    Clarkson, Bruce
    Dobbs, Cynnamon
    Dolan, Rebecca
    Hedblom, Marcus
    Klotz, Stefan
    Kooijmans, Jip Louwe
    Kuehn, Ingolf
    MacGregor-Fors, Ian
    McDonnell, Mark
    Mortberg, Ulla
    Pysek, Petr
    Siebert, Stefan
    Sushinsky, Jessica
    Werner, Peter
    Winter, Marten
    [J]. PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 2014, 281 (1780)
  • [4] Simple Sensors Used for Measuring Service Times and Counting Pedestrians Strengths and Weaknesses
    Bauer, Dietmar
    Ray, Markus
    Seer, Stefan
    [J]. TRANSPORTATION RESEARCH RECORD, 2011, (2214) : 77 - 84
  • [5] Privacy preserving crowd monitoring: Counting people without people models or tracking
    Chan, Antoni B.
    Liang, Zhang-Sheng John
    Vasconcelos, Nuno
    [J]. 2008 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-12, 2008, : 1766 - 1772
  • [6] Del P. L., 2015, P 2015 IEEE INT C MU, P1
  • [7] A Survey of Techniques for Automatically Sensing the Behavior of a Crowd
    Draghici, Adriana
    Van Steen, Maarten
    [J]. ACM COMPUTING SURVEYS, 2018, 51 (01)
  • [8] Fast crowd density estimation with convolutional neural networks
    Fu, Min
    Xu, Pei
    Li, Xudong
    Liu, Qihe
    Ye, Mao
    Zhu, Ce
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2015, 43 : 81 - 88
  • [9] Green RJ, 2008, ENERG J, P1
  • [10] An Internet-of-Things Enabled Connected Navigation System for Urban Bus Riders
    Handte, Marcus
    Foell, Stefan
    Wagner, Stephan
    Kortuem, Gerd
    Marron, Pedro Jose
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2016, 3 (05): : 735 - 744