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[2602.16739] Real-time Secondary Crash Likelihood Prediction Excluding Post Primary Crash Features
Summary
This article presents a novel framework for predicting secondary crash likelihood in real-time, focusing on traffic conditions without relying on post-crash data.
Why It Matters
The ability to predict secondary crashes in real-time is crucial for traffic management and safety. This framework addresses limitations of existing methods by utilizing real-time data, potentially reducing congestion and improving road safety.
Key Takeaways
- Introduces a hybrid framework for predicting secondary crashes without post-crash features.
- Utilizes a dynamic spatiotemporal window to gather real-time traffic data.
- Achieves a 91% correct identification rate for secondary crashes.
- Implements ensemble learning to enhance predictive performance.
- Outperforms previous models with a significant improvement in ROC AUC score.
Computer Science > Machine Learning arXiv:2602.16739 (cs) [Submitted on 17 Feb 2026] Title:Real-time Secondary Crash Likelihood Prediction Excluding Post Primary Crash Features Authors:Lei Han, Mohamed Abdel-Aty, Zubayer Islam, Chenzhu Wang View a PDF of the paper titled Real-time Secondary Crash Likelihood Prediction Excluding Post Primary Crash Features, by Lei Han and 3 other authors View PDF Abstract:Secondary crash likelihood prediction is a critical component of an active traffic management system to mitigate congestion and adverse impacts caused by secondary crashes. However, existing approaches mainly rely on post-crash features (e.g., crash type and severity) that are rarely available in real time, limiting their practical applicability. To address this limitation, we propose a hybrid secondary crash likelihood prediction framework that does not depend on post-crash features. A dynamic spatiotemporal window is designed to extract real-time traffic flow and environmental features from primary crash locations and their upstream segments. The framework includes three models: a primary crash model to estimate the likelihood of secondary crash occurrence, and two secondary crash models to evaluate traffic conditions at crash and upstream segments under different comparative scenarios. An ensemble learning strategy integrating six machine learning algorithms is developed to enhance predictive performance, and a voting-based mechanism combines the outputs of the three mo...