2026· International journal of research and innovation in applied science· Vol 11, pp. 1223-1234· 0 citations
TL;DR
This comprehensive review examines recent advances in intelligent traffic management systems that integrate deep learning-based vehicle detection with adaptive signal control mechanisms, specifically focusing on emergency vehicle prioritization to provide a comprehensive framework for researchers and practitioners developing next-generation intelligent transportation systems.
Abstract
Urban traffic congestion and delayed emergency response times represent critical challenges in modern smart cities. This comprehensive review examines recent advances (2024-2025) in intelligent traffic management systems that integrate deep learning-based vehicle detection with adaptive signal control mechanisms, specifically focusing on emergency vehicle prioritization. We analyze 30 state-of-the-art systems that leverage YOLOv9 architectures, multimodal fusion techniques, and edge computing platforms to achieve real-time traffic optimization. Key findings reveal a paradigm shift toward attention-enhanced detection models (YOLOv9+CBAM), audio-visual fusion for robust emergency vehicle identification, and edge deployment on resource-constrained hardware (Raspberry Pi, Jetson platforms). Systems employing multimodal confirmation mechanisms demonstrate superior reliability, with reported accuracies exceeding 96% and response time reductions of up to 35%. However, standardized benchmarking for false positive rates remains limited. This review synthesizes architectural innovations, prioritization strategies, and performance characteristics to provide a comprehensive framework for researchers and practitioners developing next-generation intelligent transportation systems. We identify critical research gaps and propose future directions toward more reliable, scalable, and context-aware traffic management solutions.
Urban traffic congestion remains a major barrier to sustainable mobility, necessitating a transition from static signaling to Intelligent Traffic Light Systems (ITLS). This study presents a systematic review in computer vision, artificial intelligence, and adaptive control algorithms for urban intersection optimization. Following a methodological framework informed by PRISMA guidelines, the study examines state-of-the-art architectures, including deep learning-based perception models and reinforcement learning agents. The findings indicate that, although two-stage detectors provide benchmark accuracy for vehicle perception, single-stage models and Vision Transformers offer the high-speed processing required for real-time traffic management. In addition, deep reinforcement learning enables autonomous, lane-specific optimization that outperforms traditional actuated and fixed-time systems. The review also identifies a persistent research gap in the deployment of these computational frameworks in the resource-constrained and heterogeneous infrastructures of developing countries. For the urban context of Riobamba, Ecuador, a phased implementation strategy is proposed that balances computational demands with the city’s morphological and social characteristics. By bridging the gap between high-fidelity simulation and practical field deployment, this review provides a scalable framework for improving throughput, reducing emissions, and enhancing safety. Overall, these advances offer a promising pathway toward more resilient transportation systems in rapidly evolving Andean urban centers.
Eugenia Naranjo, Juan Diego Erazo Rodríguez, Iván Sinaluisa et al.· Automation· 0 citations
Urban traffic congestion is a primary driver of greenhouse gas emissions, wasted fuel, and degraded air quality, presenting a significant barrier to achieving sustainable cities (SDG 11) and climate action (SDG 13). Standard Adaptive Traffic Signal Control (ATSC) systems are either financially prohibitive for developing countries or lack certified safety mechanisms for physical deployment on live roads. This paper proposes and validates SYTRAC (System for Adaptive Traffic Control), a low-cost, safety-critical Adaptive Traffic Signal Control system designed for resource-constrained urban environments. SYTRAC implements an asynchronous co-design that combines real-time visual vehicle detection on an NVIDIA Jetson Nano GPU with deterministic safety execution on a Siemens S7-1200 Programmable Logic Controller (PLC). The core of the system is the Density-Weighted Adaptive Green Extension (DWAGE) algorithm. DWAGE provides a stable, interpretable, and computationally lightweight alternative to complex optimization methods such as genetic algorithms, particle swarm optimization, or Deep Reinforcement Learning. We establish a formal mathematical queue-stability guarantee using a closed-form Foster–Lyapunov drift argument. A three-mode fault-tolerant state machine with a 2 s watchdog automatically transitions to fixed-time fallback in the event of hardware or camera stream failures, protecting physical intersection safety. The system was validated through hardware-in-the-loop field deployments at a live intersection in Ouargla, Algeria. SYTRAC achieved a statistically significant 22.1% reduction in average vehicle delay (p<0.001), while microscopic simulations confirmed up to 28.0% delay suppression during lane-blockage incidents. Critically, this delay reduction translates to an environmental saving of 53.5–72 kg of CO2 avoided per day, alongside annual fuel savings of 8430 L. Assembled within a $1257 hardware budget, SYTRAC delivers a cost-effective, open-source, and reproducible platform that bridges the gap between adaptive intelligence and industrial safety, providing a scalable blueprint for sustainable urban traffic management in emerging economies.
F. Bouriachi, N. Djelal, B. Kanouni et al.· Sustainability· 0 citations
This systematic review critically examines recent advancements in deep learning-based traffic flow prediction models, emphasizing studies published between 2018 and 2026 and indicates that although graph-based and transformer-based architectures currently achieve state-of-the-art predictive performance, integrating explainability, real-time adaptation, and privacy-preserving learning remains a significant research priority.
Dr. P. K. S. Bhadauria· International Journal of Cre...· 0 citations
Traffic congestion at urban intersections is commonly associated with non-adaptive Fixed Time Signal Control (FTSC), which cannot respond effectively to variations in vehicle types, traffic demand, and intersection policies. Although deep reinforcement learning (DRL) has been increasingly applied to traffic signal control, comprehensive evaluations under multi-intersection environments with heterogeneous vehicles and different turning policies remain limited. This study evaluates four value-based DRL algorithms, namely DQN, DDQN, Dueling DQN, and Dueling DDQN, for optimizing traffic signal control in a simulated four-intersection network. The simulation incorporates heterogeneous vehicle types, priority-weighted vehicles, and two turning policy scenarios, and the revised evaluation also includes Fixed Time Signal Control, Longest Queue First, and Max Pressure as baseline controllers. Results from repeated training and testing evaluations show that the DRL-based controllers generally outperform FTSC and remain competitive against adaptive baselines across waiting time and speed metrics. Case 2, which allows direct left turns, consistently performs better than Case 1; however, this improvement is interpreted as the combined effect of DRL-based control and a less restrictive traffic policy. In offline testing for Case 2, Dueling DQN reduces ambulance waiting time from 167.6 to 42.2 s, corresponding to a 74.83% reduction. Overall, the findings demonstrate the potential of DRL-based traffic signal control in controlled simulation conditions and highlight that algorithm performance is strongly influenced by traffic policy design and environmental complexity.
D. Prastiyanto, A. A. Manaf, Muhammad Ahnaf Maulana et al.· Scientific Reports· 0 citations
With the acceleration of urbanization, traffic congestion at multiple intersections has become a core bottleneck restricting urban operational efficiency. To address this, this paper proposes a traffic signal dynamic optimization algorithm, the Cross-Attention Mechanism and Dueling Double DQN (CAM-D3QN). This method utilizes a novel crisscross attention module to dynamically model spatial dependencies between intersections and incorporates the Dueling Double DQN architecture for robust Q-value estimation. Validated on CityFlow using Grid-4×4 and Hangzhou-real networks, CAM-D3QN significantly outperforms the state-of-the-art baseline, GPLight, achieving relative improvements of approximately 10.5% in average vehicle delay, 11.3% in average queue length, 3.3% in throughput, alongside notable reductions in stops (12.1%) and fuel consumption (7.2%). Ablation experiments further demonstrate that removing the cross-attention module increases queue length by 30.6% in sudden congestion scenarios. The proposed method achieves superior performance to the baseline across four typical traffic scenarios on the Hangzhou-real road network, demonstrating its generalization capabilities. By leveraging the coordinated optimization of dynamic spatial perception and robust value assessment, this paper provides an effective solution for efficient and robust coordinated traffic signal control. The framework can be combined with traffic states acquired from radar, roadside sensors or wireless communication units in intelligent transportation systems.
L. Chang, D. Wei· Advanced Electromagnetics· 0 citations
Problem of managing and controlling traffic congestion in Intelligent Transportation System (ITS). Classical, statistical and model-driven methods usually fail to take into account the nonlinear dynamic properties of congestion especially for that which concern abrupt transitions between free-flowing and congested states. Deep Learning (DL) has recently shown effective results as it is able to process large scale, high resolution data and elaborate on complex patterns that traditional methods tend to miss. To this end, a comprehensive survey is presented to investigate the most recent literature on DL-based methods for congestion prediction, detection and control, focusing on methodological advances, model diversity and performance improvements with respect to accuracy, scalability and computational complexity. At the same time, it highlights remaining gaps such as lack of robustness support in real-world scenarios, haphazard metrics interpretations and trade-offs between predictive accuracy and scalability. Through a critical review of the literature, this paper. In reviewing the available literature, this work underscores the potential of DL to improve congestion management and provides important pointers for future development in order to make it more applicable to sustainable and intelligent transportation systems.
Al Ani Mohammed Nsaif Mustafa, Mohd Murtadha Bin Mohamad, F. Muchtar· Acta Universitatis Sapientia...· 0 citations