MATEC Web Conf.
Volume 355, 20222021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
|Number of page(s)||10|
|Section||Mathematical Science and Application|
|Published online||12 January 2022|
Study on real-time prediction model of railway passenger flow based on big data technology
Institute of Computing Technologies, China Academy of Railway Sciences, Beijing
* Corresponding author: email@example.com
In order to solve the limitation of traditional offline forecasting application scenarios, the author uses a variety of big data open source frameworks and tools to combine with railway real-time data, and proposes a real-time prediction model of railway passenger flow. The model architecture is divided into four levels from bottom to top: data source layer, data transmission layer, prediction calculation layer and application layer. The main components of the model are data flow and prediction flow. Through message queue and ETL, the data process part realizes the synchronization of offline data and real-time data; through the big data technology frameworks such as Spark, Redis and Hive and the GBDT (Gradient Boosting Tree) algorithm, the prediction process partially realizes the real-time passenger flow of the train OD section prediction. The experimental results show that the model proposed by the author has certain practicability and accuracy both in performance and prediction accuracy.
Key words: Big data technology / Real-time prediction model / Spark / GBDT
© The Authors, published by EDP Sciences, 2022
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