Research Article
Traffic Volume Prediction Methods in a Multi-Source Data Environment Based on the "Four-Step Method"
@INPROCEEDINGS{10.4108/eai.15-3-2024.2346530, author={Jia Cao and Wenna Wang and Yiyi Cheng and Qian Yang}, title={Traffic Volume Prediction Methods in a Multi-Source Data Environment Based on the "Four-Step Method"}, proceedings={Proceedings of the 4th International Conference on Public Management and Intelligent Society, PMIS 2024, 15--17 March 2024, Changsha, China}, publisher={EAI}, proceedings_a={PMIS}, year={2024}, month={6}, keywords={traffic volume prediction; multi source data; model research; four-step method}, doi={10.4108/eai.15-3-2024.2346530} }- Jia Cao
Wenna Wang
Yiyi Cheng
Qian Yang
Year: 2024
Traffic Volume Prediction Methods in a Multi-Source Data Environment Based on the "Four-Step Method"
PMIS
EAI
DOI: 10.4108/eai.15-3-2024.2346530
Abstract
With the continuous updating of data collection methods in the field of highway transportation, the source of monitoring data is gradually expanding from a single static and intermittent dataset to a multi-source dataset that combines static, dynamic, and continuous data. Therefore, it is necessary to combine high-value social data and conduct research on traffic volume prediction methods based on continuous data environment, realizing the transformation of traffic volume prediction from intermittent and local analysis methods to continuous, multi-source, and gradually iterative optimization ideas, thereby improving the accuracy and reliability of traffic volume prediction.
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