Abstract
Automatic Passenger Counting (APC) solutions imply different ways of counting passengers and the most common division of APC is on independent and integrated systems. This chapter analyzes independent passenger counting solutions based on Wi-Fi technology. In this chapter, the researched problem is reflected in the review of different approaches to ridership via Wi-Fi technology in Public Urban Passenger Transport (PPT), and the most common possible division is on active and passive scanning of mobile user devices for probe request frames. Passenger counting using Wi-Fi-based APC in urban traffic can be broken down (classified) according to several observation factors, such as the location of the counting (inside and outside the vehicle), the observed means of transport (bus, train/metro, etc.), and the purpose of the collected data, estimates, actual numerical values, and methods/methodology of passenger counting. This chapter can serve as a contribution to defining the taxonomy of the way passengers are counted using Wi-Fi technology in different subsystems of public urban passenger transport as a part of future development or ongoing integration into a Smart City concept.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Statista (2020) Passenger mileage in EU-28 in 2018, by mode (in billion passenger kilometers) [Internet]. https://www.statista.com/statistics/280519/passenger-mileage-in-eu-27-by-mode/. Last accessed 23 Dec 2020
Statista (2020) Share of passenger mileage in EU-28 in 2018, by mode [Internet]. https://www.statista.com/statistics/280520/share-of-passenger-mileage-in-eu-27-by-mode/. Last accessed 27 Dec 2020
Statista (2014) Percentage of passengers using transport in the EU in September 2014, by mode of transport [Internet]. https://www.statista.com/statistics/429068/modes-of-transport-used-by-eu-citizens/. Last accessed 28 Dec 2020
Grgurević I, Juršić K, Rajič I (2020) Review of automatic passenger counting Systems in Public Urban Transport. In: Proceedings of 5th EAI international conference on Management of Manufacturing Systems, EAI MMS 2020, cyberspace
Statista (2019) Number of smartphone users worldwide from 2016 to 2021(in billions) [Internet]. https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/. Last accessed 29 Dec 2020
Seunghan R, Byungkyu BP, El-Tawab S (2020) WiFi sensing system for monitoring public transportation ridership: a case study. KSCE J Civ Eng 24:3092–3104
Håkegård J, Myrvoll TA, Skoglund T (2018) Statistical modelling for estimation of OD matrices for public transport using Wi-fi and APC data. In: 2018 21st international conference on intelligent transportation systems (ITSC), pp 1005–1010
Hidayat A, Terabe S, Yaginuma H (2020) Estimating bus passenger volume based on a Wi-fi scanner survey. Transportation Research Interdisciplinary Perspectives 6:100142
Ribeiro M, Galvão B, Prandi C, Nunes N (2020) Passive Wi-fi monitoring in public transport: a case study in the Madeira Island. In: Proceedings of TRA2020, the 8th transport research arena: rethinking transport towards clean and inclusive mobility, Helsinki, Finland
Ooi Y, Wai KZ, Tan I, Sheng OB (2016) Measuring the accuracy of crowd counting using Wi-fi probe-request-frame counting technique. J Telecommun Electron Comput Eng 8:79–81
Das S, Chatterjee S, Chakraborty S, Mitra B (2018) An Unsupervised Model for Detecting Passively Encountering Groups from WiFi Signals, 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, pp. 1–7
Yoshida, T., Yoshiaki, T.: Estimating the number of people using existing WiFi access point in indoor environment. Adv Comput Sci, pp. 46–53 (2015)
Le TV, Song B, Wynter L (2017) Real-time prediction of length of stay using passive Wi-fi sensing. In: 2017 IEEE international conference on communications (ICC), Paris, pp 1–6
Brandon S (2015) Estimating passenger flow & occupancy on board public transport buses through mobile participatory and opportunistic sensing. University of Dublin, Trinity College, Dissertation
Oransirikul T, Nishide R, Piumarta I, Takada H (2014) Measuring bus passenger load by monitoring Wi-fi transmissions from Mobile devices. Proc Technol 18:120–125
Oliveira L, Schneider D, De Souza J, Shen W (2019) Mobile device detection through WiFi probe request analysis. IEEE Access 7:98579–98588
Oliveira L, Henrique J, Schneider D, de Souza J, Rodriques S, Sherr W (2018) Sherlock: capturing probe requests for automatic presence detection. In: IEEE 22nd international conference on computer supported cooperative work in design (CSCWD), Nanjing, pp 848–853
Boyle DK (1998) Passenger counting technologies and procedures. Transit cooperative research program (TCRP) synthesis 29, published by. Transportation Research Board, Washington
Statista (2019) Number of internet of things (IoT) connected devices worldwide in 2018, 2025 and 2030 (in billions) [Internet]. https://www.statista.com/statistics/802690/worldwide-connected-devices-by-access-technology/ last accessed 2020/12/30
Gast MS (2005) 802.11 wireless networks: the definitive guide, 2nd edn. O’Reilly Media, Sebastopol, p 672
Cisco Press Homepage, https://www.ciscopress.com/articles/article.asp?p=1271797&seqNum=2 last accessed 2020/12/30
Freudiger J (2015): How talkative is your mobile device?: an experimental study of Wi-Fi probe requests. WiSec ‘15: Proceedings of the 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks, WiSec’15: 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks New York New York, pp 1–6
Nitti M, Pinna F, Pintor L, Pilloni V, Barabino B (2020) iABACUS: a Wi-fi based automatic bus passenger counting system. Energies 13(6):1–21
Mehmood U, Moser I, Jayaraman PP, Banerjee A (2019) Occupancy estimation using Wi-fi: a case study for counting passengers on busses. In: IEEE 5th world forum on internet of things, pp 165–170
Hidayat A, Terabe S, Yaginuma H (2018) Determine non-passenger data from WiFi scanner data (MAC address), a case study: Romango bus, Obuse, Nagano prefecture, Japan. Int Community Spatial Planning Sustain Dev 6(3):154–167
Prandi C, Nunes N, Ribeiro M, Nisi V (2017) Enhancing sustainable mobility awareness by exploiting multi-sourced data: the case study of the Madeira Islands. In: Sustainable internet and ICT for sustainability (SustainIT), Funchal, pp 1–5
Song B, Wynter L (2017) Real-time public transport service-level monitoring using passive WiFi: a spectral clustering approach for train timetable estimation. CoRR abs/1703.00759
Myrvoll TA, Håkegård JE, Matsui T, Septier F (2017) Counting public transport passenger using WiFi signatures of mobile devices. In: IEEE 20th international conference on intelligent transportation systems (ITSC), Yokohama, Japan, pp 1–6
Kang L, Qi B, Banerjee S (2016) A wireless-based approach for transit analytics. In: HotMobile ‘16: proceedings of the 17th international workshop on Mobile computing systems and applications, pp 75–80
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Grgurević, I., Juršić, K., Rajič, V. (2022). Overview of Wi-Fi-Based Automatic Passenger Counting Solutions in Public Urban Transport. In: Knapcikova, L., Peraković, D., Perisa, M., Balog, M. (eds) Sustainable Management of Manufacturing Systems in Industry 4.0. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-90462-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-90462-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90461-6
Online ISBN: 978-3-030-90462-3
eBook Packages: EngineeringEngineering (R0)