Work

Ensemble machine learning for travel time prediction: An Active learning-assisted approach

Year: 2025

Type: article

Abstract: Accurate travel time prediction is the fundamental challenge in modern transportation systems, affecting real-time navigation, congestion mitigation, and route optimization. However, achieving precise travel time predictions over large transportation networks is challenging due to the dynamic nature of traffic and the high cost of collecting large, labeled datasets. To mitigate these challenges, this study introduces and evaluates an error‑guided active learning strategy that prioritizes high‑error regions for labeling and expands queries to their local spatiotemporal neighborhoods. The framework is evaluated using three models: a Deep Neural Network, an Extreme Gradient Boosting regressor, and an ensemble combination of the two. A case study was conducted using a large-scale dataset of 6.72 million travel time data points from the Bangkok Road network, covering 100 customer locations at 15-minute intervals over a one-week span. Results show the active learning framework substantially reduces labeling without compromising accuracy. A benchmark ensemble model trained on 25 % of randomly selected data attained a Mean Absolute Percentage Error of 10.65 % (12.4 % to 8.9 %). Using the same amount of labeled data, the active learning-enhanced ensemble model achieved a Mean Absolute Percentage Error of 5.95 % (7.2 % to 4.7 %), a roughly 44 % decrease in error. Furthermore, the active learning-enhanced model matched the benchmark’s accuracy using only 15.62 % of the total data, a reduction of almost 40 % in labeling effort while ultimately providing better predictive performance. The proposed method offers a scalable and cost-effective solution for creating high-quality, dynamic travel time predictions that are essential for advancing intelligent transportation and logistics systems.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2025.108712

Authors: Syed Muhammad Tariq Shah, Mongkut Piantanakulchai

Institution: Thammasat University

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: $1,940