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Related Experiment Video

Updated: May 23, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Published on: January 20, 2023

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

Haicheng Xiao¹, Xueyan Shen¹, Jianglin Li²

¹Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, China.

Scientific Reports

|March 12, 2025

Summary

This study introduces an advanced data imputation method for ride-hailing trajectories, improving accuracy and speed by leveraging spatiotemporal features. The novel approach enhances data characteristics for more reliable research outcomes.

Keywords:

Data filling techniques LG-SG model Ride-hailing trajectory data Spatiotemporal modeling

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Area of Science:

Data Science
Artificial Intelligence
Transportation Science

Background:

Data imputation is crucial for research accuracy, but traditional methods fail with complex spatiotemporal ride-hailing data.
Existing techniques lack speed and accuracy due to inadequate handling of temporal data characteristics.

Purpose of the Study:

To enhance data imputation accuracy and efficiency for ride-hailing trajectory data.
To overcome limitations of traditional imputation methods in capturing spatiotemporal features.

Main Methods:

A prediction-based imputation approach was developed.
A feature generation model using LightGBM-GRU was combined with a SARIMA-GRU prediction model.
This hybrid model enriches data characteristics for improved imputation.

Main Results:

The proposed method effectively imputes missing data in ride-hailing trajectories.
Enhanced capture and enrichment of data characteristics were achieved.
Improved imputation accuracy and convergence speed are demonstrated.

Conclusions:

The LightGBM-GRU and SARIMA-GRU integrated model offers a robust solution for spatiotemporal data imputation.
This approach provides a solid foundation for subsequent analyses in ride-hailing research.
The study highlights the importance of advanced feature engineering for accurate data imputation.