Sensing Multi-modal Mobility Patterns: A Case Study of Helsinki using Bluetooth Beacons and a Mobile Application

Abstract

Large urban special events significantly contribute to a city's vibrancy and economic growth but concurrently impose challenges on transportation systems due to alterations in mobility patterns. This study aims to shed light on mobility patterns by utilizing a unique, comprehensive dataset collected from the Helsinki public transport mobile application and Bluetooth beacons. Earlier methods, relying on mobile phone records or focusing on single traffic modes, do not fully grasp the intricacies of travel behavior during such events. We focus on the Vappu festivities (May 1st) in the Helsinki Metropolitan Area, a national holiday characterized by mass gatherings and outdoor activities. We examine and compare multi-modal mobility patterns during the event with those during typical non-working days in May 2022. Through this case study, we find that people tend to favor public transport over private cars and are prepared to walk longer distances to participate in the event.The study underscores the value of using comprehensive multimodal data to better understand and manage transportation during large-scale events.

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

“…Jiang et al [9] proposed an incentive mechanism protecting user rights using uncertain and hidden bids but could not effectively safeguard task demander interests due to the absence of a reputation management module. Huang et al [10] researched potential combinations of traditional Automated Passenger Counters (APC) and a novel source capable of collecting detailed mobile demand data but did not include a reputation management module to prevent malicious data uploads. Tutsoy et al [25] proposed an AI based long-term policy making algorithm aiming to maximize the number of the students attending the schools while minimizing the number of the casualties.…”

“…Jiang et al [9] proposed an incentive mechanism protecting user rights using uncertain and hidden bids but could not effectively safeguard task demander interests due to the absence of a reputation management module. Huang et al [10] researched potential combinations of traditional Automated Passenger Counters (APC) and a novel source capable of collecting detailed mobile demand data but did not include a reputation management module to prevent malicious data uploads. However, these mechanisms face several challenges in traditional crowdsourcing systems: 1) platform security lacks robust guarantees and may be susceptible to attacks [11]; 2) there exists a potential for large-scale privacy breaches [12]; and 3) incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations, occasionally lacking a dedicated reputation update module.…”