Search Results (146)

Urban traffic congestion remains a persistent global challenge, contributing to significant economic inefficiencies, elevated greenhouse gas emissions, and diminished quality of life. This paper presents a real-world video-based traffic monitoring study combined with a proposed adaptive signal control framework. In the monitoring component, YOLOv11 object detection was applied directly to footage recorded from an overhead bridge position on a 40 km/h road. The model successfully detected and tracked multiple road-user categories, including cars, trucks, buses, motorcycles, cyclists, and pedestrians, yielding 1041 vehicle detections across 25 unique tracked objects. Vehicle speeds were estimated from inter-frame centroid displacement, and a Region of Interest (ROI) occupancy model was used to classify congestion states as High, Medium, or Free Flow using thresholds grounded in Highway Capacity Manual (HCM) level-of-service criteria. The system detected 11 high-congestion frames (3.8%), 184 medium-congestion frames (63.9%), and 93 free-flow frames (32.3%), consistent with moderate congestion observed during the recording period. In the proposed control component, a Proximal Policy Optimisation (PPO)-based reinforcement learning signal controller is designed around the YOLOv11 detection outputs as its state representation. Based on comparable adaptive traffic signal control studies in the literature, the proposed framework is projected to achieve approximately 25% higher peak-hour throughput, 35% shorter queue lengths, and 32% lower average waiting times relative to a fixed-time signal baseline. The detection accuracy (mAP@0.5 = 93.2%) and inference speed (32 FPS) cited are published YOLOv11 benchmarks used as indicative performance references. This work bridges real-world perception and proposed intelligent control, providing a transparent and reproducible methodology for next-generation smart city traffic management. Full article

Phantom congestion in highway tunnels reduces operational efficiency and destabilizes traffic flow. In this study, the effects of a pacemaker system (PMS) on traffic operation in the Geumnam Tunnel on the Seoul–Yangyang Expressway were evaluated using a before–after analysis based on long-term vehicle detection system (VDS) data. Unlike past studies, this study provides an integrated empirical evaluation by jointly examining changes in spatiotemporal traffic flow, traffic capacity, and speed improvement at different level of service. The analyses were conducted using data from five VDS detectors installed upstream and downstream from the tunnel. After PMS installation, (i) increased average and 25th-percentile speeds at most detector locations and decreased speed standard deviation were observed near the tunnel exit and downstream sections, (ii) the maximum traffic volume increased from 1661 to 1765 veh/h/lane, and (iii) the mean speed and 25th-percentile speed increased by 6.5%, indicating speed-reduction alleviation among low-speed vehicles. Thus, the PMS increases vehicle speed, reduces speed variability, and enhances traffic flow stability and processing capability. These findings provide empirical evidence for the operational effectiveness of a PMS as a practical tool for mitigating phantom congestion in highway tunnel sections, reducing speed differences between vehicles, and improving traffic stream stability. Full article

Vulnerable Road Users (VRUs) are involved in a significant proportion of traffic fatalities, and they are highly exposed to severe injuries in urban traffic environments. For detecting and tracking VRUs, LiDAR technology offers precise 3D perception capabilities, overcoming challenges posed by their small size, dynamic behavior, and frequent presence in occluded or congested areas. This work aims to conduct a scoping review of LiDAR-based solutions for preventing and reducing accidents involving VRUs, synthesizing current methodologies, evaluating detection and tracking approaches, and identifying strategies to improve urban safety through data-driven interventions. An analysis of 49 publications indicates that effective monitoring of VRUs depends on a strategic balance between technological performance and practical limitations, such as system costs, calibration complexity, and hardware constraints. Privacy-preserving techniques, such as anonymization and LiDAR-based sensing, are essential to enable ethically responsible large-scale data collection. Full article

With the advancement of urbanization and motorization, urban traffic conditions increasingly affect both travel efficiency and system stability, yet existing studies based on high-frequency OD data mainly focus on single aspects such as congestion patterns or travel time variability, lacking a unified analytical framework that jointly captures time-varying travel impedance, reliability, and anomaly risks under comparable conditions, especially in cross-city contexts. This study constructs a standardized analytical framework with a novel integration based on a “city × weekday × 5 min interval” structure, using high-frequency navigation OD data from eight major cities in China over four consecutive weeks, totaling approximately 560,000 valid samples. Travel Time per Unit Distance (TTUD) is employed as the core metric, and a distance-stratified weighting approach is adopted to improve cross-city comparability. Reliability is characterized by variability, dispersion, and tail risk, and anomalous events are identified using a dynamic baseline. The results reveal clear intra-week temporal regularity and significant inter-city heterogeneity, with weekday evening peaks generally lasting longer than those on weekends, reflecting sustained commuting pressure and slower dissipation of travel demand. A total of 249 anomaly events are detected, with higher frequency and persistence on weekdays, highlighting the increased vulnerability of traffic systems during peak commuting periods and indicating that commuting periods are more prone to sustained deviations due to higher system load and demand instability. Overall, the proposed framework provides a unified and comparable basis for cross-city traffic performance evaluation and supports practical applications such as peak-period traffic management, congestion mitigation, and traffic risk monitoring. Full article

Persistent traffic congestion and the need for efficient traffic monitoring have increased the demand for automated vehicle-analysis systems based on CCTV footage. This study presents a CCTV-based vehicle monitoring system that integrates vehicle detection, tracking, counting, public/private vehicle class prediction, seven-category vehicle-type prediction, vehicle-color recognition, and traffic-state estimation using YOLOv12 and DeepSORT. To reduce manual annotation effort during the initial training stage, a semi-automated method for generating synthetic composite road scenes was developed by combining cropped vehicle images and road-background images. The detector was first trained on 10,000 synthetic images and then sequentially fine-tuned on real CCTV data. Four real-world traffic video clips from Metro Manila were used in the study. Three 5 min clips were used within the staged refinement workflow: the first two for iterative refinement and the third for final post-refinement evaluation of the adapted model. A separate fourth CCTV clip was reserved exclusively for blind evaluation without on-the-fly retraining. The final system achieved average accuracies of 97% for public/private vehicle class prediction, 90% for seven-category vehicle-type prediction, 82% for vehicle-color recognition, and 96.67% for vehicle counting on the final evaluation video. The results show that synthetic pretraining combined with limited real-world fine-tuning can improve performance in CCTV-based vehicle monitoring while reducing the amount of manually labeled real-world data required. The study also discusses the limitations of the current evaluation protocol and the need for broader multi-location testing. Full article

Traffic flow in long underground expressways is expected to exhibit amplified spatiotemporal variability due to confined geometry, longitudinal gradients, limited recovery space, and heterogeneous vehicle interactions. As these facilities remain at the planning stage, empirical field data are unavailable, necessitating simulation-based methodological development. Conventional performance indicators (average speed) primarily reflect macroscopic deterioration after congestion has materialized and are therefore insufficient for capturing early variability transitions. This study proposes a composite Spatiotemporal Variability Metric (STVM) designed to quantify instability-related variability dynamics and enable early congestion detection in confined expressway environments. The metric structure was established through the synthesis of prior traffic flow instability research and systematic evaluation of 72 predesigned microscopic simulation scenarios representing diverse geometric and operational conditions. STVM integrates six mechanism-informed components: short-term speed and density fluctuations, heavy-vehicle proportion, sectional saturation level, ramp interference intensity, and exit discharge efficiency. Comparative analyses against average speed demonstrated that variability escalation measured by STVM consistently precedes observable speed degradation by 5–20 min. Internal contribution analyses using correlation, regression, and random forest modeling further confirmed the dominant structural roles of fluctuation- and saturation-related components in governing variability escalation. These findings confirm the usefulness of the STVM in analyzing transition dynamics and supporting real-time ITS-based monitoring in confined expressway systems. Full article

In urban traffic systems, traditional signal control can no longer meet the increasing traffic demand, and local congestion is severe during peak hours. Fixed detector data is characterized by high deployment density, full sample detection and restorable vehicle paths, providing new data support for coordinated signal control. We propose an optimization method for regional coordinated control, with the Changsha road network as the study area. Firstly, based on License Plate Recognition (LPR) data, the road network is divided into sub-networks and combined with the boundary control for regional coordinated control. Then, the critical path is taken as the control object, and the phase coordination rate is introduced as the optimization objective. The particle swarm optimization algorithm improved by the logistic chaotic map is used as the global searcher, and sequential quadratic programming is adopted as the local searcher to solve the optimization strategy for the objective function. Finally, a simulated road network is constructed in VISSIM 6.0 simulation software to verify the effectiveness of the strategy. The results show that the optimization strategy reduces intersection delay and saturation by 20.3% and 19.3% in the critical path coverage area. Road travel time and the average number of vehicle stops are reduced by 21% and 22.1%. This indicates that the regional coordinated control based on the improved particle swarm algorithm can better alleviate the peak hour traffic congestion. Full article

Uninterrupted traffic flow monitoring is a prerequisite for optimal resource allocation and minimizing vehicular emissions in smart cities. However, centralized traffic management architectures are highly vulnerable to single points of failure. When structural sensor malfunctions occur, the resulting network unobservability paralyzes dynamic signalization, triggering cascading traffic congestion, extended idling times, and severe greenhouse gas emissions. To address this cyber-ecological vulnerability, we propose the Hybrid Multi-Agent State Estimation (H-MASE) protocol, a fully decentralized decision-support framework designed from an applied systems reliability engineering perspective. By deploying PSAs and VLAs directly onto IoT-enabled edge devices at smart intersections, H-MASE leverages a hop-by-hop edge computing topology to collaboratively track macroscopic route flow dynamics. Mathematically, this distributed estimation process is formulated as a network-wide least-squares convex optimization problem, where local projection operators function as exact Distributed Gradient Descent steps to minimize the global residual sum of squares. The distributed consensus mechanism acts as a spatial variance reduction tool, effectively dampening measurement noise and stochastic demand fluctuations. Furthermore, we introduce an autonomous anomaly detection logic that isolates severe structural faults rapidly, which is mathematically structured to prevent false alarms under bounded disturbance conditions. Numerical simulations demonstrate that the protocol yields a highly resilient optimality gap (e.g., a Root Mean Square Error of merely 0.81 vehicles per estimated state) even under catastrophic hardware failures. Ultimately, H-MASE provides a robust, fail-safe data foundation for sustainable urban logistics and green-wave signalization, ensuring that smart cities maintain ecological resilience and optimal resource utilization under severe structural disruptions. Full article

Traffic congestion is a problem in urban traffic that needs to be monitored and managed intelligently. In this study, a hybrid traffic management system is designed based on a combination of drone vision, large language model (LLM) inferences, and deep reinforcement learning (DRL). Using drones videos of real-time traffic, the lightweight You Only Look Once v11 model detects vehicles, and after, traffic flow levels are identified by the proposed LLM agent. A Circular-Deep Q-Networks-based DRL controller is proposed to reduce the average waiting time of vehicles. Simulation experiments validate improved congestion detection, reduced delay, and more effective communication for smart city traffic control. Full article

Traffic accident detection and object detection have become key areas of research due to their direct impact on safety, traffic congestion mitigation, and intelligent traffic planning. This study presents a structured analysis of classical detection methods and artificial intelligence-based techniques, highlighting their methodologies, objectives, and performance results. The study categorizes existing research into threshold-based approaches, statistical approaches, image processing, rule-based approaches, and machine learning approaches, with further emphasis on predictive modeling, graph-based approaches, and optimization approaches. Considerable emphasis is placed on identifying systems that are capable of operating under adverse weather conditions such as fog, rain, and snow. These scenarios significantly affect detection accuracy. Although several authors incorporate environmental resilience into their models, most studies still evaluate performance under ideal conditions, revealing a critical gap in research. This analysis highlights the need to develop robust detection mechanisms that can adapt to real-world variability and environmental disturbances. Findings show that AI-based methods significantly outperform classical approaches in terms of adaptability and scalability, but their dependence on training data limits their performance in adverse conditions. The study concludes with recommendations for future work to prioritize multimodal sensing, generalization across weather conditions, and integration of environmental intelligence to ensure reliable real-time detection of traffic events under all operating conditions. Full article

Urban intersections are critical nodes for roadway safety, congestion management, and autonomous vehicle coordination. Traditional traffic control systems based on fixed-time signals and static sensors lack adaptability to real-time risks such as red-light violations, near-miss incidents, and multimodal conflicts. This study presents a grid-enabled framework integrating computer vision and machine learning to enhance real-time intersection intelligence and road safety. The system overlays a computational grid on the roadway, processes live video feeds, and extracts dynamic parameters including vehicle trajectories, deceleration patterns, and queue evolution. A novel active learning module improves detection accuracy under low visibility and occlusion, reducing false alarms in collision and violation detection. Designed for edge-computing environments, the framework interfaces with signal controllers to enable adaptive signal timing, proactive collision avoidance, and emergency vehicle prioritization. Case studies from multiple intersections typical of US cities show improved phase utilization, reduced intersection conflicts, and enhanced throughput. A grid-based heatmap visualization highlights spatial risk zones, supporting data-driven decision-making. The proposed framework bridges static infrastructure and intelligent mobility systems, advancing safer, smarter, and more connected roadway operations. Full article

Traditional automated monitoring systems adopted for Intersection Traffic Control still face challenges, including high costs, maintenance difficulties, insufficient coverage, poor multimodal data integration, and limited traffic information analysis. To address these issues, the study proposes a sovereign AI-driven Smart Transportation governance approach, developing a mobile AI solution equipped with multimodal perception, task decomposition, memory, reasoning, and multi-agent collaboration capabilities. The proposed system integrates computer vision, multi-object tracking, natural language processing, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to construct a Pipeline-based Traffic Analysis System (PTAS). The PTAS can produce real-time statistics on pedestrian and vehicle flows at intersections, incorporating potential risk factors such as traffic accidents, construction activities, and weather conditions for multimodal data fusion analysis, thereby providing forward-looking traffic insights. Experimental results demonstrate that the enhanced DuCRG-YOLOv11n pre-trained model, equipped with our proposed new activation function $\beta silu$, can accurately identify various vehicle types in object detection, achieving a frame rate of 68.25 FPS and a precision of 91.4%. Combined with ByteTrack, it can track over 90% of vehicles in medium- to low-density traffic scenarios, obtaining a 0.719 in MOTA and a 0.08735 in MOTP. In traffic flow analysis, the RAG of Vertex AI, combined with Claude Sonnet 4 LLMs, provides a more comprehensive view, precisely interpreting the causes of peak-hour congestion and effectively compensating for missing data through contextual explanations. The proposed method can enhance the efficiency of urban traffic regulation and optimizes decision support in intelligent transportation systems. Full article

Traffic flow anomalies represent significant deviations from normal traffic behavior and disrupt the smooth operation of transportation systems. These may appear as unusually high or low traffic volumes compared to historical trends. Unexpectedly high volume can lead to congestion exceeding usual capacity, while unusually low volume might indicate incidents like road closures, or malfunctioning traffic signals. Identifying and understanding both types of anomalies is crucial for effective traffic management. This paper presents a clustering based approach for enhanced characterization of anamolies in traffic flows. Anomalies in traffic patterns are determined using three anomaly detection techniques: Elliptic Envelope, Isolation Forest, and Local Outlier Factor. These anomalies were newly detected in this work on the Montréal dataset after preprocessing, rather than directly reused from earlier studies. These methods were applied to a dataset that had been pre-processed using windowing techniques with different configuration settings to enhance the detection process. Then, to leverage the detected anomalies, we utilized clustering algorithms, specifically k-means and hierarchical clustering, to segment these anomalies. Each clustering algorithm was used to determine the optimal number of clusters. Subsequently, we characterized these clusters through detailed visualization and mapped them according to their unique characteristics. This approach not only identifies traffic anomalies effectively but also provides a comprehensive understanding of their spatial and temporal distributions, which is crucial for traffic management and urban planning. Full article

The increasing use of artificial intelligence (AI) and deep learning (DL) techniques has driven advances in vehicle classification and detection applications for embedded devices with deployment constraints due to computational cost and response time. In the case of urban environments with high traffic congestion, such as the city of Lima, it is important to determine the trade-off between model accuracy, type of embedded system, and the dataset used. This study was developed using a methodology adapted from the CRISP-DM approach, which included the acquisition of traffic videos in the city of Lima, their segmentation, and manual labeling. Subsequently, three SSD-based detection models (MobileNetV1-SSD, MobileNetV2-SSD-Lite, and VGG16-SSD) were trained on the NVIDIA Jetson Orin NX 16 GB platform. The results show that the VGG16-SSD model achieved the highest average precision (mAP $\approx 90.7\%$), with a longer training time, while the MobileNetV1-SSD ($512\times 512$) model achieved comparable performance (mAP $\approx 90.4\%$) with a shorter time. Additionally, data augmentation through contrast adjustment improved the detection of minority classes such as Tuk-tuk and Motorcycle. The results indicate that, among the evaluated models, MobileNetV1-SSD ($512\times 512$) achieved the best balance between accuracy and computational load for its implementation in ADAS embedded systems in congested urban environments. Full article

Urban traffic infrastructures like traffic signals, surveillance cameras, and embedded sensors play an essential role in providing sustainable mobility but are also susceptible to malfunctions, data drift, and degradation from environmental conditions. In this study, we propose AIP-Urban, an edge AI-enabled predictive maintenance framework that employs deep spatio-temporal learning with continuous anomaly detection for smart transportation systems. Our framework integrates IoT sensing, computer vision, and time-series analytics to identify and forecast infrastructure failures before they occur. For visual and numerical anomalies (e.g., traffic signal outage, abrupt congestion, sensor disconnection), we employ a hybrid CNN–Transformer model, while we utilise a Temporal LSTM predictor to estimate a degradation trend to predict maintenance events within 24 h. The models are deployed on Jetson Nano edge devices to enable real-time processing under energy constraints. Extensive simulation studies using datasets from SUMO, CityCam, and UA-DETRAC show that AIP-Urban achieved 94% accuracy for anomaly detection (F1 = 0.94), with RMSE = 0.11 for failure prediction and an edge inference latency of 72 ms, while power consumption remained below 7.8 W. Statistical tests (Wilcoxon p < 0.05) show goodness-of-fit compared to baseline models of CNN, LSTM, and Transformer only. This study shows promise in improving the reliability, safety, and sustainability of urban traffic using proactive, explainable, and energy-aware AI at the edge. AIP-Urban serves as a reproducible reference architecture for future AI-driven transportation maintenance systems that is aligned with intelligent and resilient smart cities principles. Full article