ArticleScientific reports2025
Advancements in accident-aware traffic management: a comprehensive review of V2X-based route optimization.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Deep learning-based high-speed railway communication systems.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As urban populations grow and vehicle numbers surge, traffic congestion and road accidents continue to challenge modern transportation systems. Conventional traffic management approaches, relying on static rules and centralized control, struggle to adapt to unpredictable road conditions, leading to longer commute times, fuel wastage, and increased safety risks. Vehicle-to-Everything (V2X) communication has emerged as a transformative solution, creating a real-time, data-driven traffic ecosystem where vehicles, infrastructure, and pedestrians seamlessly interact. By enabling instantaneous information exchange, V2X enhances situational awareness, allowing traffic systems to respond proactively to accidents and congestion. A critical application of V2X technology is accident-aware traffic management, which integrates real-time accident reports, road congestion data, and predictive analytics to dynamically reroute vehicles, reducing traffic bottlenecks and improving emergency response efficiency. Advanced computational algorithms, including heuristic methods, machine learning models, and AI-driven optimization techniques, play a vital role in enhancing routing decisions within V2X networks. By leveraging these algorithms, modern traffic systems can transition from reactive congestion management to proactive traffic optimization, significantly improving urban mobility. Despite its potential, the large-scale deployment of V2X-enabled traffic management systems faces several challenges, including network reliability, data privacy, cybersecurity risks, and interoperability issues. Additionally, concerns related to algorithmic transparency, ethical decision-making, and standardization of V2X communication protocols must be addressed to ensure seamless integration into existing infrastructure. Unlike existing surveys that broadly examine V2X communication or intelligent transportation systems (ITS), this review uniquely focuses on accident-aware traffic management and route optimization. It synthesizes state-of-the-art accident detection methods, routing strategies, and optimization algorithms, while identifying research gaps and proposing future directions for integrating V2X technologies into safer, adaptive, and intelligent transportation systems. By providing these targeted insights, the study contributes to the development of smarter, safer, and more efficient road networks, offering valuable guidance for researchers, policymakers, and industry professionals working to shape the future of urban mobility.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.