College of Information Science and Engineering
Department of Information Science and Engineering
09/2015 Ritsumeikan University Graduate School of Information Science and Engineering Computer Science Master's course Completed
03/2019 Ritsumeikan University Graduate School of Information Science and Engineering Advanced Information Science and Engineering Doctoral course Completed
Doctor of Engineering (03/2019 Ritsumeikan University)
Master of Engineering (Ritsumeikan University)
04/01/2019- Ritsumeikan University Assistant Professor
■Subject of research
Estimate the number of passenger in the bus transportation without requiring passengers to cooperate
Propose a method for monitoring the activities of people by using low cost and practical small devices
Estimating the number of Passengers in Public Transportation by Analysing Mobile Device Wi-Fi Activity
Travel by public transportation is a daily activity for billions of people around the world. Quality of service is one of the important factors for passengers to consider, when deciding to use the public transportation system. City bus transportation is the main target of this research among several types of public transportation systems.
Increasing the quality of service can be done in several ways, such as improving on-time arrival, reducing waiting time, and providing seat availability information. If the number of passengers in a bus transportation system can be estimated, passengers can make a decision to wait for a bus or change their plan, leading to the increased quality of service. In addition, bus company can provide better satisfaction to passengers by preparing for additional busses and avoiding the congestion.
This research describes a method to estimate the number of passengers in public transportation by passively monitoring and analysing Wi-Fi signal activity from their mobile devices, and classifying them as originating from passenger or non-passenger devices using a filtering mechanism. The method uses a Wi-Fi frame sniffer to capture the Wi-Fi signal activity from mobile devices, and filters out the captured Wi-Fi frames based on three main parameters, received signal strength, the number of received packets and detected time, to distinguish passenger's devices from environmental ones. For estimating the number of passengers on the bus in real-time, the idle time is used as an additional parameter representing the time during which the mobile device has not been detected.
The experimental results reveal that the estimated number of devices follows the observed number of passengers, increasing when passengers get on the bus and decreasing when they get off or when a bus leaves the bus stop. This result is useful in providing the congestion information to passengers in the bus transportation system.
Mobile Network, Intelligence Transportation System, Monitoring Wi-Fi
■Research activities (Even top three results are displayed. In View details, all results for public presentation are displayed.)
Classifying Passenger and Non-passenger Signals in Public Transportation by Analysing Mobile Device Wi-Fi Activity Thongtat Oransirikul, Ian Piumarta and Hideyuki Takada Journal of Information Processing 27, 25-32 01/2019 1882-6652
Feasibility of Analyzing Wi-Fi Activity to Estimate Transit Passenger Population T. Oransirikul, R. Nishide, I. Piumarta and H. Takada 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA) 362-369 03/2016 1550-445X
■Research keywords(on a multiple-choice system)