Multimodal Data Fusion for Big Events

Abstract

Many of the transportation problems prevalent in urban areas culminate in large-scale events. Such events generate large multimodal flows that arrive and depart within short time intervals to constrained areas. Monitoring and managing big events pose a challenge for transport planners, operators, event organizers, and city officials. In this study, data concerning multimodal flows were collected and analyzed for a so-called triple event in Amsterdam, Netherlands, where more than 60,000 people visited the Amsterdam ArenA area. The collection and fusion of large and diverse data sets provided this study a unique opportunity to reconstruct, from incomplete data, the crowds’ arrival and departure times and estimate their modal-split patterns. Considerably different arrival and departure time patterns were observed for car and public transport users. Visitors using public transport arrived approximately 45 min before the start times of the events compared with 75 min for car users. The lag between the event end time and the departure time of public transport users was approximately 20 to 50 min, whereas a lag of 20 to 80 min was observed for departing cars. The factors that possibly underlie these differences are discussed as are the limitations in the analysis. The results of this study can support decisions about the allocation of parking lots and the scheduling of public transport services.

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