Passive Mobile Crowdsensing for Determining the Volume of Passengers in Public Transport

This conference paper presents a comprehensive exploration of the applicability of passive mobile crowdsensing for measuring pedestrian and passenger movement in public transit systems. The research endeavors to establish a robust method for gathering and processing sensor data, with a focus on data matching algorithms. It also involves practical sensor testing to evaluate the feasibility of Wi-Fi technology for these purposes. The study underscores the relevance of deploying specialized sensors and a dedicated server infrastructure to enhance data collection and analysis within public transport contexts. By developing custom sensors and server capabilities, the authors aim to elevate the accuracy and efficiency of device detection. This approach seeks to provide insights into rush hour dynamics across diverse scenarios, ultimately contributing to more efficient public transportation services and informed decision-making. The paper also serves as an introduction to the concept of crowdsensing, making it accessible to a broader audience.