AI on the Road – A Review of Technologies Enhancing Urban Traffic Safety and Efficiency
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Summary
This systematic literature review addresses the growing complexity of urban traffic management, driven by population growth, increased vehicle density, and shifting transit demands. The study investigates how the integration of Artificial Driving (AD) models and intelligent network connections can enhance traffic safety and efficiency, moving beyond the limitations of Traditional Traffic Management (TTM) systems. The research is motivated by a critical gap in existing literature: while individual technologies like AI or connectivity are studied, there is limited comprehensive analysis of their synergistic impact on proactive risk prediction and adaptive traffic control in urban arterial networks. The authors conducted a systematic literature review (SLR) of peer-reviewed articles published between 2014 and 2023. A comprehensive search across seven electronic databases, including Google Scholar, IEEE Xplore, and ScienceDirect, initially retrieved 382 articles. After applying strict inclusion and exclusion criteria—focusing on studies analyzing connected and autonomous vehicles in real-world applications and excluding non-English or purely human-driven vehicle studies—the pool was refined to 140 articles. A snowballing technique was employed to ensure comprehensiveness, resulting in a final selection of 130 primary studies for detailed analysis. These studies were evaluated for quality based on their alignment with research questions regarding safety, efficiency, real-time data application, and future perspectives. The review finds that approximately 30% of the analyzed literature explores both safety and efficiency, highlighting the significance of integrated approaches. Traditional methods, such as static signal control and legal enforcement, are effective but struggle to adapt to dynamic urban traffic conditions. In contrast, Intelligent Transportation Systems (ITS) utilizing AD models and vehicle-to-everything (V2X) connectivity offer significant improvements. AD models, leveraging machine learning and sensor fusion, accurately predict traffic patterns and driver behavior, enabling proactive risk mitigation. Intelligent network connections facilitate real-time data sharing between vehicles and infrastructure, improving traffic light synchronization and response times. Case studies within the reviewed literature demonstrate that these technologies reduce collision rates and enhance pedestrian safety through features like smart crosswalks and autonomous braking. However, the review also identifies substantial challenges, including cybersecurity risks, lack of standardization for interoperability, inadequate legislative frameworks, and public concerns regarding data privacy and job displacement. The study concludes that the synergy of AD and intelligent network connections represents a transformative shift in urban traffic management, enabling a dynamic ecosystem capable of proactive safety improvements. The findings underscore the need for further research into the socioeconomic impacts, ethical application of AI, and integration of these technologies with existing infrastructure. The authors recommend future work focus on overcoming interoperability barriers, addressing cybersecurity threats, and developing sustainable legislative frameworks to support the widespread adoption of AI-driven traffic solutions. This review provides a foundational assessment for transportation and computer science professionals aiming to advance Intelligent Transportation Systems.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-20 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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