Identifying shifts in multi-modal travel patterns during special events using mobile data: Celebrating Vappu in Helsinki

• URL: http://arxiv.org/abs/2305.17925v1
• Date: Mon, 29 May 2023 07:32:49 GMT
• Title: Identifying shifts in multi-modal travel patterns during special events using mobile data: Celebrating Vappu in Helsinki
• Authors: Zhiren Huang and Charalampos Sipetas and Alonso Espinosa Mireles de Villafranca and Tri Quach
• Abstract summary: 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. We focus on the Vappu festivities (May 1st) in the Helsinki Metropolitan Area, a national holiday characterized by mass gatherings and outdoor activities. We find that people tend to favor public transport over private cars and are prepared to walk longer distances to participate in the event.
• Score: 1.0323063834827415
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 multi-modal data to better understand and manage transportation during large-scale events.

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