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Measuring Bus Passenger Load by Monitoring Wi-Fi Transmissions from Mobile Devices
4 references·16 citing·45 second hop
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References

  • 2005Reality mining: sensing complex social systems2,771 cited
  • 2006Mobility Detection Using Everyday GSM Traces277 cited
  • 2009Location-Aware Tools for Improving Public Transit Usability76 cited
  • 2013Participatory bluetooth sensing: A method for acquiring spatio-temporal data about participant mobility and interactions at large scale events50 cited

Cited by

  • 2021Multi-stage deep learning approaches to predict boarding behaviour of bus passengers38 cited
  • 2018WiFi Scanner Technologies for Obtaining Travel Data about Circulator Bus Passengers: Case Study in Obuse, Nagano Prefecture, Japan35 cited
  • 2021A Field Study of Internet of Things-Based Solutions for Automatic Passenger Counting34 cited
  • 2020Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation Modes33 cited
  • 2017Data analysis of transit systems using low-cost IoT technology32 cited
  • 2019ViFi-MobiScanner: Observe Human Mobility via Vehicular Internet Service21 cited
  • 2023Online Public Transit Ridership Monitoring Through Passive WiFi Sensing18 cited
  • 2016Feasibility of Analyzing Wi-Fi Activity to Estimate Transit Passenger Population17 cited
  • 2019Classifying Passenger and Non-passenger Signals in Public Transportation by Analysing Mobile Device Wi-Fi Activity17 cited
  • 2021

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Review of Automatic Passenger Counting Systems in Public Urban Transport
17 cited
  • 2024A low-cost automatic people-counting system at bus stops using Wi-Fi probe requests and deep learning13 cited
  • 2018Determine Non-Passenger Data from WiFi Scanner Data (MAC Address), A Case Study: Romango Bus, Obuse, Nagano Prefecture, Japan11 cited
  • 2022Overview of Wi-Fi-Based Automatic Passenger Counting Solutions in Public Urban Transport10 cited
  • 2017Lessons learned using wi-fi and Bluetooth as means to monitor public service usage9 cited
  • 2024TransitCrowd: Estimating Subway Stations Demand with Mobile Crowdsensing Data8 cited
  • 2020A Framework for Transit Monitoring System Using IoT Technology: Two Case Studies7 cited