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WiFi Sensing System for Monitoring Public Transportation Ridership: A Case Study

  • Transportation Engineering
  • Published:
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Abstract

Public transportation system as an essential mode of travel has been investigated by local governments and transportation agencies to capture passengers’ travel behaviors. Despite their efforts, agencies especially in small to medium sized cities could not afford to collect such behaviors data due to significant costs associated with the data collection system. In this study, we presented a WiFi sensing system which makes such data collection feasible with low-cost devices. We demonstrated the WiFi sensing system’s applicability in estimating passengers’ origin-destination (O/D) travel and passengers’ bus stop waiting times via video validation. In addition, WiFi signal strength was analyzed to further improve accuracy of the system. To this end, sliding window algorithm was adopted to mitigate the randomness of mobile devices’ signals. Our small-scale proof of concept experiment was conducted at four bus stops along the main transit corridor in Charlottesville, Virginia. Results indicated that the system was able to re-identify 91% of bus passengers and passengers bus stop waiting time error was as small as 7 seconds. It is expected that the system can be a viable low-cost Internet of Things (IoT) solution for monitoring public transit system performance.

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References

  • Abedi N, Bhaskar A, Chung E (2013) Bluetooth and Wi-Fi MAC address-based crowd data collection and monitoring: Benefits, challenges, and enhancement. Proceedings on Australasian transport research forum, October 2–4, Brisbane, Australia

  • Arai I, Horimi S, Nishio N (2010) Wi-Foto 2: Heterogeneous device controller using Wi-Fi positioning and template matching. Proceedings on Pervasive, May 10–20, Helsinki, Finland

  • Barcelo J, Montero L, Marqués L, Carmona C (2010) Travel time forecasting and dynamic origin-destination estimation for freeways based on bluetooth traffic monitoring. Transportation Research Record: Journal of the Transportation Research Board (2175):19–27, DOI: https://doi.org/10.3141/2175-03

    Google Scholar 

  • Becker JK, Li D, Starobinski D (2019) Tracking anonymized bluetooth devices. Proceedings on Privacy Enhancing Technologies 3:50–65, DOI: https://doi.org/10.2478/popets-2019-0036

    Article  Google Scholar 

  • Chandir S, Dharma VK, Siddiqi DA, Khan AJ (2017) Feasibility of using global system for mobile communication (GSM)-based tracking for vaccinators to improve oral poliomyelitis vaccine campaign coverage in rural Pakistan. Vaccine 35(37):5037–5042, DOI: https://doi.org/10.1016/j.vaccine.2017.07.026

    Article  Google Scholar 

  • Chon Y, Kim S, Lee S, Kim D, Kim Y, Cha H (2014) Sensing WiFi packets in the air: Practicality and implications in urban mobility monitoring. Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing, September 13–17, Seattle, WA, USA, 189–200, DOI: https://doi.org/10.1145/2632048.2636066

  • Danalet A, Farooq B, Bierlaire M (2014) A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures. Transportation Research Part C: Emerging Technologies 44:146–170, DOI: https://doi.org/10.1016/j.trc.2014.03.015

    Article  Google Scholar 

  • Ding X, Liu Z, Xu H (2019) The passenger flow status identification based on image and WiFi detection for urban rail transit stations. Journal of Visual Communication and Image Representation 58:119–129, DOI: https://doi.org/10.1016/j.jvcir.2018.11.033

    Article  Google Scholar 

  • Du Y, Yue J, Ji Y, Sun L (2017) An enhanced method to improve detection rate and precision of pedestrian flow under WiFi based system. Transportation research board 96th annual meeting, January 8–12, Washington DC, USA

  • Dunlap M, Li Z, Henrickson K, Wang Y (2016) Estimation of origin and destination information from bluetooth and Wi-Fi sensing for transit. Transportation Research Record: Journal of the Transportation Research Board 2595(1):11–17, DOI: https://doi.org/10.3141/2595-02

    Article  Google Scholar 

  • El-Tawab S, Yorio Z, Salman A, Oram R, Park BB (2019) Origin-destination tracking analysis of an intelligent transit bus system using Internet of Things. 2019 IEEE international conference on pervasive computing and communications workshops (PerCom Workshops), March 11–15, Kyoto, Japan, 139–144, DOI: https://doi.org/10.1109/PERCOMW.2019.8730746

  • Evers K, Oram R, El-Tawab W, Heydari MH, Park BB (2017) Security measurement on a cloud-based cyber-physical system used for intelligent transportation. 2017 IEEE international conference on vehicular electronics and safety (ICVES), June 27–28, Vienna, Austria, 97–102, DOI: https://doi.org/10.1109/ICVES.2017.7991908

  • Fuxjaeger P, Ruehrup S, Paulin T, Rainer B (2016) Towards privacy-preserving Wi-Fi monitoring for road traffic analysis. IEEE Intelligent Transportation Systems Magazine 8(3):63–74, DOI: https://doi.org/10.1109/MTS.2016.2573341

    Article  Google Scholar 

  • Gavilanes GB (2018) Persons counter through Wi-Fi’s passive sniffing for IoT. 2018 IEEE third Ecuador technical chapters meeting (ETCM), October 15–19, Cuenca, Ecuador, DOI: https://doi.org/10.1109/ETCM.2018.8580283

  • Goodall N (2017) Fundamental characteristics of Wi-Fi and wireless local area network re-identification for transportation. IET Intelligent Transport Systems 11(1):37, DOI: https://doi.org/10.1049/iet-its.2016.0087

    Article  Google Scholar 

  • Haleem SLA, Samsudeen SN (2016) Real time bus tracking and scheduling system using wireless sensor and mobile technology. Journal of Information Systems & Information Technology (JISIT) 1(1):18–23

    Google Scholar 

  • Haghani A, Hamedi M, Sadabadi K, Young S, Tarnoff P (2010) Data collection of freeway travel time ground truth with Bluetooth sensors. Transportation Research Record: Journal of the Transportation Research Board (2160):60–68, DOI: https://doi.org/10.3141/2160-07

  • Ji Y, Zhao J, Zhang Z, Du Y (2017) Estimating bus loads and OD flows using location-stamped farebox and Wi-Fi signal data. Journal of Advanced Transportation 2017:1–10, DOI: https://doi.org/10.1155/2017/6374858

    Google Scholar 

  • Kurkcu A, Ozbay K (2017) Estimating pedestrian densities, wait times, and flows with Wi-Fi and Bluetooth sensors. Transportation Research Record 2644(1):72–82, DOI: https://doi.org/10.3141/2644-09

    Article  Google Scholar 

  • Lesani A, Miranda-Moreno L (2019) Development and testing of a real-time WiFi-Bluetooth system for pedestrian network monitoring and data extrapolation. IEEE Transaction on Intelligent Transportation Systems 20(4):1484–1496, DOI: https://doi.org/10.1109/TITS.2018.2854895

    Article  Google Scholar 

  • Li A, Shahidehpour M (2017) Deployment of cybersecurity for managing traffic efficiency and safety in smart cities. The Electricity Journal 30:52–61, DOI: https://doi.org/10.1016/j.tej.2017.04.003

    Google Scholar 

  • Martin J, Mayberry T, Donahue C, Foppe L, Brown L, Riggins C, Rye EC, Brown D (2017) A study of MAC address randomization in mobile devices and when it fails. Proceedings on Privacy Enhancing Technologies 2017(4):365–383, DOI: https://doi.org/10.1515/popets-2017-0054

    Article  Google Scholar 

  • Mehmood U, Moser I, Prakash P, Banerjee A (2019) Occupancy estimation using WiFi: A case study for counting passengers on busses. 2019 IEEE 5th world forum on Internet of Things (WF-IoT), April 15–18, Limerick, Ireland DOI: https://doi.org/10.1109/WF-IoT.2019.8767350

  • Mishalani RG, McCord MR, Reinhold T (2016) Use of mobile device wireless signals to determine transit route-level passenger origin-destination flows: Methodology and empirical evaluation. Transportation Research Record 2544(1):123–130, DOI: https://doi.org/10.3141/2544-14

    Article  Google Scholar 

  • NelsonNygaard Consulting Associates Inc. (2013) Charlottesville transit study report. Charlottesville Area Transit, Charlottesville, VA, USA

    Google Scholar 

  • Nishide R, Yamamoto S, Takada H (2015) Position estimation for people waiting in line using Bluetooth communication. Proceedings of the 5th international conference on mobile services, resources, and users, June 21–26, Brussels, Belgium

  • Nuaimi EA, Neyadi HA, Mohamed N, Al-Jaroodi J (2015) Applications of big data to smart cities. Journal of Internet Services and Applications 6(1):25, DOI: https://doi.org/10.1186/s13174-015-0041-5

    Article  Google Scholar 

  • Oransirikul T, Nishide R, Piumarta I, Takada H (2014) Measuring bus passenger load by monitoring wi-fi transmissions from mobile devices. Procedia Technology 18:120–125, DOI: https://doi.org/10.1016/j.protcy.2014.11.023

    Article  Google Scholar 

  • Oransirikul T, Piumarta I, Takada H (2019) Classifying passenger and non-passenger signals in public transportation by analysing mobile device Wi-Fi activity. Journal of Information Processing 27:25–32, DOI: https://doi.org/10.2197/ipsjjip.27.25

    Article  Google Scholar 

  • Paradeda D, Kraus Jr W, Carlson R (2019) Bus passenger counts using Wi-Fi signals: some cautionary findings. Transportes 27:115–130, DOI: https://doi.org/10.14295/transportes.v27i3.2039

    Article  Google Scholar 

  • Poucin G, Farooq B, Patterson Z (2016) Pedestrian activity pattern mining in wifi-network connection data. Transportation research board 95th annual meeting, January 10–14, Washington DC, USA

  • Pu Z, Zhu M, Cui Z, Wang Y (2019) Mining public transit ridership flow and origin-destination information from Wi-Fi and Bluetooth sensing data. arXiv:1911.01282 (preprint)

  • Rathore MM, Ahmad A, Paul A, Rho S (2016) Urban planning and building smart cities based on the Internet of Things using big data analytics. Computer Networks 101:63–80, DOI: https://doi.org/10.1016/j.comnet.2015.12.023

    Article  Google Scholar 

  • Romancyshyn T, Lesani A, Miranda-Moreno L (2017) Monitoring signalized intersection performance with bluetooth and wifi duration data. Transportation research board 96th annual meeting, January 8–12, Washington DC, USA

  • Ryeng E, Haugen T, Gronlund H, Overa S (2016) Evaluating Bluetooth and Wi-Fi sensors as a tool for collecting bicycle speed at varying gradients. Transportation Research Procedia 14:2289–2296, DOI: https://doi.org/10.1016/j.trpro.2016.05.245

    Article  Google Scholar 

  • Salem A, Nadeem T, Cetin M, El-Tawab S (2015) Driveblue: Traffic incident prediction through single site bluetooth. 2015 IEEE 18th international conference on intelligent transportation systems, September 15–18, Las Palmas, Spain, 725–730, DOI: https://doi.org/10.1109/ITSC.2015.123

  • Salman A, Diehl W, Kaps JP (2017) A light-weight hardware/software co-design for pairing-based cryptography with low power and energy consumption. 2017 international conference on field programmable technology (ICFPT), December 11–13, Melbourne, VIC, Australia, 235–238, DOI: https://doi.org/10.1109/FPT.2017.8280149

  • Salman A, El-Tawab S, Yorio Z, Hilal A (2018) Indoor localization using 802.11 WiFi and IoT edge nodes. 2018 IEEE global conference on Internet of Things (GCIoT), December 5–7, Alexandria, Egypt, 1–5, DOI: https://doi.org/10.1109/GCIoT.2018.8620162

  • Schauer L, Werner M, Marcus P (2014) Estimating crowd densities and pedestrian flows using Wi-Fi and Bluetooth. 11th international conference on mobile and ubiquitous systems: Computing, networking and services, December 2–5, London, UK, 171–177, DOI: https://doi.org/10.4108/icst.mobiquitous.2014.257870

  • Shokry A, Torki M, Youssef M (2018) Deeploc: A ubiquitous accurate and low-overhead outdoor cellular localization system. Proceedings of the 26th ACM SIGSPATIAL international conference on advances in geographic information systems, November 6–9, Seattle, WA, USA, 339–348, DOI: https://doi.org/10.1145/3274895.3274909

  • US DOT FHWA (2018) San Diego regional fare system modernization - Volume 1: Technical application. Office of Operations, US Department of Transportation (US DOT) Federal Highway Administration (FHWA), Washington DC, USA

    Google Scholar 

  • Vanhoef M, Matte C, Cunche M, Cardoso L, Piessens F (2016) Why MAC address randomization is not enough: An analysis of Wi-Fi network discovery mechanisms. ASIA CCS’16: ACM Asia conference on computer and communications security, May 30–June 3, Xi’an, China, DOI: https://doi.org/10.1145/2897845.2897883

  • Waadt A, Wang S, Bruck GH, Jung P (2009) Traffic congestion estimation service exploiting mobile assisted positioning schemes in GSM networks. Procedia Earth and Planetary Science 1(1):1385–1392, DOI: https://doi.org/10.1016/j.proeps.2009.09.214

    Article  Google Scholar 

  • Wepulanon P, Sumalee A, Lam W (2019) Temporal signatures of passive Wi-Fi data for estimating bus passenger waiting time at a single bus stop. IEEE Transactions on Intelligent Transportation Systems 1–11, DOI: https://doi.org/10.1109/TITS.2019.2926577

  • Yorio Z, Oram R, El-Tawab S, Salman A, Heydari MH, Park BB (2018) Data analysis and information security of an Internet of Things (IoT) intelligent transit system. 2018 systems and information engineering design symposium (SIEDS), April 27, Charlottesville, VA, USA, 24–29, DOI: https://doi.org/10.1109/SIEDS.2018.8374744

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Acknowledgements

This research was supported by the 4-VA Initiative (http://4-va.org/) and GRL Program through the NRF funded by the Ministry of Science, ICT & Future Planning (2013K1A1A2A02078326).

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Correspondence to Seunghan Ryu.

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Ryu, S., Park, B.B. & El-Tawab, S. WiFi Sensing System for Monitoring Public Transportation Ridership: A Case Study. KSCE J Civ Eng 24, 3092–3104 (2020). https://doi.org/10.1007/s12205-020-0316-7

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  • DOI: https://doi.org/10.1007/s12205-020-0316-7

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