DSETA: Dual-Stage Continual Learning Framework for ETA Prediction
A recent paper published on arXiv (2608.00402) presents DSETA, a dual-stage framework aimed at continual learning for predicting travel times in ever-changing traffic conditions. This innovative approach tackles the issue of sustaining precise estimated time of arrival (ETA) predictions in large urban areas where traffic congestion is highly variable. Traditional methods often struggle to adapt to unexpected congestion or fail to separate long-term trends from short-term variations. DSETA consists of two stages: the intra-day stage, which utilizes real-time data to respond to immediate traffic changes due to events like accidents or holidays, and the inter-day stage, which focuses on long-term trends. The authors of this study, announced as a cross-type submission on arXiv, designed the framework specifically for ride-hailing services to enhance ETA accuracy in practical situations.
Key facts
- DSETA is a dual-stage continual learning framework for ETA prediction.
- It addresses dynamic traffic environments and sudden congestion.
- The framework has inter-day and intra-day learning stages.
- Intra-day stage uses real-time data for short-term adaptation.
- Inter-day stage handles long-term trends.
- The paper is on arXiv with ID 2608.00402.
- It is a cross-type announcement.
- The goal is to improve ETA prediction for ride-hailing platforms.
Entities
Institutions
- arXiv