Seeing the Forest for the Trees: Road-Level Insights Assisted Lane-Level Traffic Prediction
Open Access
- 21 October 2024
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 1266-1275
Abstract
Lane-level traffic prediction is crucial for refined smart city applications, yet the scarcity and quality issues of datasets hinder its development. To overcome these challenges, this study introduces a novel M ulti- c hannel g raph-structured V ariational A uto E ncoder model, McgVAE. This model integrates road-level information to provide a global perspective for lane prediction and performs integrated tasks through three interconnected channels: the road-level channel ensures accurate prediction of road traffic states and communicates closely with the data quality channel to share historical and predicted road information; the data quality channel leverages road-level information to identify and correct missing and noisy data; and finally, the lane channel uses the aforementioned information for lane-level traffic prediction. After extensive experimental comparisons with multiple baseline models across three datasets, the McgVAE model demonstrated outstanding predictive performance and the ability to handle data missingness and noiseKeywords
Funding Information
- National Natural Science Foundation of China (No. 62072125)
- Hong Kong Research Grants Council (No. 16202722, T43-513/23-N-TRS)
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