Smart City Gnosys
Smart city article details
| Title | Traffic Event Detection Using Twitter Data Based On Association Rules |
|---|---|
| ID_Doc | 58562 |
| Authors | Xu S.; Li S.; Wen R.; Huang W. |
| Year | 2019 |
| Published | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 2/W5 |
| DOI | http://dx.doi.org/10.5194/isprs-annals-IV-2-W5-543-2019 |
| Abstract | Social media platforms allow millions of people worldwide to instantly share their thoughts online. Many people use social media to share traffic related experiences and events with online posts. A large amount of traffic related data can be obtained from these online posts – especially geosocial media data, where posts are tagged with geolocation information such as coordinates or place names. By extracting traffic events from geosocial media data, drivers can adapt to changing traffic conditions, while traffic management departments can propose timely and effective plans to improve traffic conditions. Most of the existing studies query traffic-related information based on a list of single keywords, which result in large amounts of noisy data – negative data containing one or more traffic-related keywords, but do not actually represent real-world traffic events. This paper aims to filter noisy data by mining association rules among words in positive data containing messages representing traffic events. Messages are more likely to be true traffic events if they follow the co-occurrence pattern of words mined from positive samples. A case study was conducted in Toronto, Canada using Twitter data. The tweets queried by the association rules were classified into non-traffic event, traffic accidents, roadwork, severe weather conditions, and special events with an 85% accuracy based on supervised machine learning methods. Compared with hourly average travel speed data, 81% of detected events were identified as real-world traffic events. This research sheds light on traffic condition monitoring in smart transportation platforms, which plays an important role for smart cities. © Authors 2019. |
| Author Keywords | Association rules; Event detection; Geosocial media data; Natural language processing; Traffic event; Twitter |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
46754  | 0.914 | Alomari E.; Mehmood R.; Katib I. | Road Traffic Event Detection Using Twitter Data, Machine Learning, And Apache Spark | Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019 (2019) |
| 49778 | 0.91 | Alomari E.A.; Mehmood R. | Smart Cities, Smarter Roads: A Review Of Leveraging Cutting-Edge Technologies For Intelligent Event Detection From Social Media | International Journal of Advanced Computer Science and Applications, 14, 11 (2023) |
| 27465 | 0.875 | Alkouz B.; Al Aghbari Z. | Fusion Of Multiple Arabic Social Media Streams For Traffic Events Detection | 2022 7th International Conference on Big Data Analytics, ICBDA 2022 (2022) |
| 42938 | 0.873 | Laynes Fiascunari V.; Rabelo L. | Preliminary Study For Impact Of Social Media Networks On Traffic Prediction | Communications in Computer and Information Science, 1643 CCIS (2022) |
| 11152 | 0.869 | Park D.-Y.; Ko I.-Y. | Auto-Labeling Of Sensor Data Using Social Media Messages: A Case Study For A Smart City | Proceedings of the ACM Symposium on Applied Computing (2021) |
| 38280 | 0.867 | AlDhanhani A.; Damiani E.; Mizouni R.; Wang D.; Al-Rubaie A. | Multi-Modal Traffic Event Detection Using Shapelets | Neural Computing and Applications, 35, 2 (2023) |
| 14304 | 0.862 | Teixeira E.B.; De Souza Moura P.N.; Campos C.A.V. | Classification Of Traffic Event Tweets In Portuguese Language Using Deep Learning | 2022 International Wireless Communications and Mobile Computing, IWCMC 2022 (2022) |
| 38279 | 0.861 | Chen Q.; Wang W. | Multi-Modal Neural Network For Traffic Event Detection | 2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019 (2019) |
| 47341 | 0.859 | Islam M.J.; Talusan J.P.; Bhattacharjee S.; Tiausas F.; Dubey A.; Yasumoto K.; Das S.K. | Scalable Pythagorean Mean-Based Incident Detection In Smart Transportation Systems | ACM Transactions on Cyber-Physical Systems, 8, 2 (2024) |
| 36035 | 0.855 | Bawaneh M.; Simon V. | Machine Learning-Based Anomaly Detection In Smart City Traffic: Performance Comparison And Insights | International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS - Proceedings (2025) |