Crowd Forecasting at Venues with Microblog Posts Referring to Future Events
| Autor: | Haosen Zhan, Shonosuke Ishiwatari, Koji Zettsu, Haichuan Shang, Ryotaro Tsukada, Masashi Toyoda, Kazutoshi Umemoto |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
Event (computing) Microblogging business.industry ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Big data Attendance 02 engineering and technology 010501 environmental sciences 01 natural sciences Data modeling Transport engineering Long short term memory 020204 information systems 0202 electrical engineering electronic engineering information engineering Social media Traffic network Baseline (configuration management) business 0105 earth and related environmental sciences |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata50022.2020.9377925 |
| Popis: | Large events with many attendees cause congestion in the traffic network around the venue. To avoid accidents or delays due to this kind of unexpected congestion, it is important to predict the level of congestion in advance of the event. This study aimed to forecast congestion triggered by large events. However, historical congestion information alone is insufficient to forecast congestion at large venues when non-recurrent events are held there. To address this problem, we utilize microblog posts that refer to future events as an indicator of event attendance. We propose a regression model that is trained with microblog posts and historical congestion information to accurately forecast congestion at large venues. Experiments on next 24-hour congestion forecasting using real-world traffic and Twitter data demonstrate that our model reduces the prediction errors over those of the baseline models (autoregressive and long short term memory) by 20% – 50%. |
| Databáze: | OpenAIRE |
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