Research Article | A transformer-based multi-feature fusion method for detecting traffic events using Twitter data

A recent study published in Big Earth Data demonstrates how social media data, combined with deep learning, can significantly improve the detection of traffic events, offering transportation agencies a faster and more accurate tool for managing roads and reducing congestion. 

Citation
Qutaishat, D., & Li, S. (2025). A transformer-based multi-feature fusion method for detecting traffic events using Twitter data. Big Earth Data, 1–33. https://doi.org/10.1080/20964471.2025.2564525

Abstract

Early traffic event detection is essential for transportation networks’ quick response and accurate performance recovery. Social media data (e.g. Twitter data) can be a valuable source for detecting and describing traffic events, such as accidents, congestion, and road closures. Integrating spatial and temporal features inherent in Twitter can enhance models by revealing non-trivial information, patterns, and knowledge, resulting in improved performance. Recent research in Deep Learning (DL) has revealed the strength of learning features directly from data to extract potential hidden features that efficiently infer human activities and interactions and detect the relationships to generate fine-grained information. This research explores the efficiency of integrating Twitter data’s spatial, temporal, and semantic features for traffic event detection using DL. A novel framework employing a transformer-based multi-feature fusion approach is proposed, designed to detect traffic incidents comprehensively via Twitter data. The framework classifies tweets based on multiple dimensions: (1) semantic content is numerically represented and categorized traffic incidents, non-traffic, or traffic conditions and information; (2) spatial characteristics are analyzed through hot-spot analysis techniques, classifying locations into hot or cold spots; and (3) temporal attributes (date and time) are visualized and analyzed through heat maps reflecting incident densities. The performance of the models was then evaluated based on various fusion scenarios combining spatial, temporal, and semantic data using performance metrics such as F-score and accuracy. The results showed that the scenario of transformer-based multi-feature fusion of spatial, temporal, and semantic data for traffic event detection yielded better results. The model achieved a 7.93% accuracy improvement when distinguishing between the two classes, “Traffic Incident” and “Non-Traffic”, and a 6.09% increase in accuracy when classifying across three categories: “Traffic Incident,” “Non-Traffic,” and “Traffic Conditions and information.” These findings highlight the effectiveness of using a multifeatured Twitter dataset for improved detection accuracy.

Keywords

Deep learning, transformer, Twitter, DistilBERT, semantic feature, temporal feature, spatial feature

Big Earth Data is an interdisciplinary Open Access journal which aims to provide an efficient and high-quality platform for promoting the sharing, processing and analyses of Earth-related big data, thereby revolutionizing the cognition of the Earth’s systems. The journal publishes a wide range of content, including Research Articles, Review Articles, Data Notes, Technical Notes, and Perspectives. It is now included in ESCI (IF=3.8, Q1), Scopus (CiteScore=9.0, Q1), Ei Compendex, GEOBASE, and Inspec. Starting from 2023, Big Earth Data has announced a new award series for authors: Best and Outstanding Paper Awards.