Spatial-Temporal Similarity-Based Data Fusion for Large-Scale Trajectories in Metro System


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    As the metro system becoming more and more important. How to utilize big data technology to support operational and management tasks is a hot topic in academia and industry communities. These tasks include metro network developing, service scheduling, risk response management, and public services. To address these issues, we propose a data fusion-based approach on two sources to rebuild a passenger’s full trip. The key idea is that we leverage the WiFi signal data and the smart card data together. We first calculate the spatiotemporal similarity between of smart card’s trajectories and mobile device’s trajectories. Then, we associate a passenger’s smart card and the corresponding mobile device via their similarity. Finally, we combine the instation trajectory hidden in the WiFi signal record and the coarse-grained trip presented by smart card record. We validate our approach on an extremely large dataset in the Chinese city Shenzhen. We calculate the similarity of trajectories generated by 7.28 million of smart cards and trajectories generated by 40.1 million of mobile devices in a Spark cluster. Experimental results show that this approach can rebuild 203 000 passenger trajectories. These results are enough to support many important applications in metro system.

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XIONG Wen, ZHOU Qianmei, YANG Kun, DAI Hao, SUN Li. Spatial-Temporal Similarity-Based Data Fusion for Large-Scale Trajectories in Metro System[J]. Journal of Integration Technology,2019,8(5):26-33

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  • Online: October 09,2019
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