IEEE Transactions on Intelligent Transportation Systems

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ISSN / EISSN : 15249050 / 15580016
Total articles ≅ 3,752
Google Scholar h5-index: 68
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Paulo H. L. Rettore, Bruno P. Santos, Roberto Rigolin F. Lopes, Guilherme Maia, Leandro Villas, Antonio A. F. Loureiro
IEEE Transactions on Intelligent Transportation Systems pp 1-16; doi:10.1109/tits.2020.2971111

Abstract:
In this work, we propose the Road Data Enrichment (RoDE), a framework that fuses data from heterogeneous data sources to enhance Inteligent Transportation System (ITS) services, such as vehicle routing and traffic event detection. We describe RoDE through two services: (i) Route service, and (ii) Event service. For the first service, we present the Twitter MAPS (T-MAPS), a low-cost spatiotemporal model to improve the description of traffic conditions through Location-Based Social Media (LBSM) data. As a case study, we explain how T-MAPS is able to enhance routing and trajectory descriptions by using tweets. Our experiments compare T-MAPS' routes against Google Maps' routes, showing up to 62% of route similarity, even though T-MAPS uses fewer and coarse-grained data. We then propose three applications, Route Sentiment (RS), Route Information (RI), and Area Tags (AT), to enrich T-MAPS' suggested routes. For the second service, we present the Twitter Incident (T-Incident), a low-cost learning-based road incident detection and enrichment approach built using heterogeneous data fusion. Our approach uses a learning-based model to identify patterns on social media data which is then used to describe a class of events, aiming to detect different types of events. Our model to detect events achieved scores above 90%, thus allowing incident detection and description as a R0DE application. As a result, the enriched event description allows ITS to better understand the LBSM user's viewpoint about traffic events (e.g., jams) and points of interest (e.g., restaurants, theaters, stadiums).
Guanglong Du, Tao Li, Chunquan Li, Peter X. Liu, Di Li
IEEE Transactions on Intelligent Transportation Systems pp 1-12; doi:10.1109/tits.2020.2979527

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Chuan Hu, Yimin Chen, Junmin Wang
IEEE Transactions on Intelligent Transportation Systems pp 1-11; doi:10.1109/tits.2020.2979431

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Chu-Tak Li, Wan-Chi Siu
IEEE Transactions on Intelligent Transportation Systems pp 1-18; doi:10.1109/tits.2020.2975710

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Yuanli Gu, Wenqi Lu, Xinyue Xu, Lingqiao Qin, Zhuangzhuang Shao, Hanyu Zhang
IEEE Transactions on Intelligent Transportation Systems, Volume 21, pp 1332-1342; doi:10.1109/tits.2019.2939290

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Jeonghyun Baek, Junhyuk Hyun, Euntai Kim
IEEE Transactions on Intelligent Transportation Systems, Volume 21, pp 1216-1228; doi:10.1109/tits.2019.2904836

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Wufei Wu, Renfa Li, Guoqi Xie, Jiyao An, Yang Bai, Jia Zhou, Keqin Li
IEEE Transactions on Intelligent Transportation Systems, Volume 21, pp 919-933; doi:10.1109/tits.2019.2908074

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Ashraf Abosekeen, Aboelmagd Noureldin, Michael J. Korenberg
IEEE Transactions on Intelligent Transportation Systems, Volume 21, pp 1250-1263; doi:10.1109/tits.2019.2905871

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Enhui Chen, Zhirui Ye, Chao Wang, Mingtao Xu
IEEE Transactions on Intelligent Transportation Systems, Volume 21, pp 1109-1120; doi:10.1109/tits.2019.2902405

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