Work

A method for filling traffic data based on feature-based combination prediction model

Year: 2025

Type: article

Abstract: Data imputation is a critical step in data processing, directly influencing the accuracy of subsequent research. However, due to the temporal nature of ride-hailing trajectory data, traditional imputation methods often struggle to adequately consider spatiotemporal characteristics, leading to limitations in both convergence speed and accuracy. To address this issue, this study employs a prediction-based approach to enhance imputation accuracy. Given the limited feature parameters in trajectory data, traditional prediction models often fail to comprehensively capture data characteristics. Therefore, this study proposes a feature generation model based on LightGBM-GRU, combined with a SARIMA-GRU prediction model, to more thoroughly capture and enrich the data characteristics. This approach effectively imputes missing data, thereby laying a solid foundation for subsequent research.

Source: Scientific Reports

DOI: 10.1038/s41598-025-92547-y

Authors: Haicheng Xiao, Xueyan Shen, Jianglin Li, Xiujian Yang

Institution: Kunming University of Science and Technology

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,690