"Spatiotemporal AI for Transportation Safety in the Digital Era: Responsible Use of New Data Forms"

Call for Papers-"Spatiotemporal AI for Transportation Safety in the Digital Era: Responsible Use of New Data Forms"

Journal: Accident Analysis & Prevention

Publisher: Elsevier

Submission Deadline: 31 Dec 2026

Key Requirements:

• Spatiotemporal AI focus mandatory.

• Responsible use: interpretability, calibration, robustness, privacy, and bias mitigation.

• New data forms: trajectories, video, telemetry, driver/crew monitoring, incident logs.

Overview

Digital transportation systems now generate rich spatiotemporal data from sensors, telemetry, video, and driver monitoring, creating new opportunities to detect near misses, conflicts, anomalies, and other safety precursors. This special issue brings together theories, methods, algorithms, metrics, datasets, simulations, and field implementations that use these data forms to improve road safety. It also emphasizes responsible deployment, requiring models to be interpretable, calibrated, robust, and evaluated for decision relevance across vehicles, operators, and the public.

Key Research Themes

• Methodological frameworks for responsible Spatiotemporal AI in safety.

• Multimodal data fusion with uncertainty and data governance.

• Dense trajectory analytics for risk, exposure, near-miss, and conflict inference.

• Video and image-based safety for event precursor detection and robust tracking.

• Driver/crew monitoring linking attention, workload, and fatigue to safety outcomes.

• Text mining of incident reports and logs.

• Interpretability, uncertainty, and OOD detection for operational use.

• Fairness, privacy-preserving learning, and bias assessment for sensitive spatial-temporal data.

• Simulation, digital twins, and sim-to-field validation for safety prediction and control.

Submission Instructions

• Submit by 31 Dec 2026 through Editorial Manager.

• Select "VSI: Spatiotemporal AI for Transportation Safety" as the article type.

• Contact Dr. James Haworth at j.haworth@ucl.ac.uk for topic suitability questions.

• Manuscripts will undergo peer review by at least two independent reviewers.

• Accepted papers will appear in the latest regular issue and on the special issue webpage simultaneously.

Guest Editors

• Dr. James Haworth, University College London, United Kingdom.

• Dr. Xiaowei Gao, Imperial College London, United Kingdom.

• Prof. Adam Sobey, The Alan Turing Institute / University of Southampton, United Kingdom.

• Prof. Hongguang Lyu, Dalian Maritime University, China.

• Prof. Nico Van de Weghe, Ghent University, Belgium.

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