Search Results (254)

Non-recurrent congestion (NRC), caused by unforeseen events such as crashes, lane closures, and adverse weather, accounts for approximately half of all delays on urban freeways, yet it remains difficult to distinguish from routine congestion at recurrent bottlenecks. This study proposes an NRC detection framework based on a Physics-Informed Neural Network (PINN) that embeds the Lighthill–Whitham–Richards (LWR) conservation law into the learning process to construct a physically consistent baseline of normal traffic states. The traffic flow physics is represented by a two-regime fundamental diagram combining the Greenshields model for free-flow conditions and the Underwood model for congested conditions, and the network is trained by minimizing a composite loss that adaptively balances the data fitting error against the LWR residual. NRC is then detected when the observed density exceeds the PINN-estimated baseline density beyond a tolerance threshold of 150%. The framework was evaluated on a 12 km segment of the Seoul Ring Expressway in Korea using six months of 15 min data collected from seventeen sensor stations. The results show that the proposed model reliably isolates NRC events from recurrent peak-period congestion. From the perspective of symmetry, the framework interprets recurrent traffic as a temporally symmetric background state governed by a conservation law, and non-recurrent congestion as a local breaking of this symmetry, which the physics-constrained residual is designed to expose. The key contribution of this study is a theoretically grounded, label-free anomaly detection approach that couples machine learning with traffic flow theory, offering traffic management centers an automated and interpretable tool for incident detection and response. Full article

Urban traffic systems are increasingly vulnerable to non-recurrent congestion and abnormal traffic fluctuations, posing significant challenges to intelligent traffic management and resilient transportation operations. Existing traffic anomaly detection methods often struggle to simultaneously characterize heterogeneous anomaly patterns under dynamically evolving traffic states, while severe class imbalance and limited data plausibility further constrain detection reliability. To address these challenges, this study proposes a traffic-prior-guided state-aware framework for robust urban traffic anomaly detection. A Multi-Scale Natural Neighborhood (MS-NaN) module transforms one-dimensional traffic flow sequences into a nine-dimensional representation integrating sequence dynamics, multiscale statistical deviations, and spatiotemporal phase characteristics, thereby embedding traffic state priors into the detection process. Building upon these representations, the Dual-Branch Context-Gated Network (DB-CGNet) separately captures instantaneous traffic disruptions and trend-evolving congestion patterns. An adaptive context-aware gated fusion mechanism then combines the branch features to enhance robustness under complex and non-stationary traffic conditions. To improve evaluation realism, high-fidelity baseline traffic data are generated through B-spline smoothing and first-order autoregressive residual modeling, and anomaly patterns are constructed under Highway Capacity Manual (HCM)-constrained capacity reduction mechanisms. Experiments conducted on a 91-day urban expressway dataset demonstrate that the proposed method achieves the best overall performance among eight benchmark models under a 72 min observation window, attaining an F1-score of 0.7757 and an area under the receiver operating characteristic curve (AUC) of 0.9112. Ablation studies further reveal the critical role of traffic prior features in detecting short-duration evolving anomalies. The proposed framework provides a robust and interpretable solution for intelligent urban traffic monitoring, anomaly warning, and resilient traffic operation management. Full article

To address the severe coverage blind spots and concurrent collision bottlenecks faced by LoRa networks in dense deployments and complex three-dimensional (3D) occlusion environments, this paper proposes a distributed cross-layer protocol framework for LoRa ad hoc networks supporting heterogeneous traffic. To break through the limitations of a single star architecture, this framework constructs a 3D penetration loss model at the physical layer and designs a distributed relay deployment algorithm based on hybrid simulated annealing, achieving blind-spot-free connectivity in complex spaces. At the MAC layer, a non-preemptive priority access mechanism based on symbol energy detection is introduced. Through differentiated backoff windows with time-domain isolation, it precisely guarantees the quality of service (QoS) requirements of heterogeneous traffic and significantly suppresses concurrent collisions. At the network layer, the CAM-AODV routing algorithm is proposed, which integrates hop count, link quality, MAC queue congestion, and nodal residual energy to achieve dynamic traffic diversion and network-wide energy balancing under bursty high loads. Simulation results demonstrate that this cross-layer framework effectively breaks the traditional network capacity bottlenecks. In a large-scale, high-density scenario with 300 nodes, CAM-AODV reduces the average end-to-end delay by 19.46% compared to the traditional AODV. Under high-concurrent loads, the packet delivery ratio (PDR) of the proposed framework improves by 16.32% over the traditional protocol, while the system delay is reduced by 13.66%. Furthermore, under the two aforementioned evaluation scenarios, the Energy Balancing Index (EBI) is significantly improved by 11.13% and 10.57%, respectively, compared to the traditional protocol. This study provides an efficient joint optimization scheme for building high-capacity, wide-coverage, and long-lifespan complex Internet of Things (IoT) networks. Full article

Adherence to right-of-way (RoW) rules at uncontrolled T-intersections helps avoid accidents and alleviate congestion. In non-uniform traffic, right-turning behaviour can be characterised by distinct driving traits, such as non-compliance (failure to yield), a nonchalant attitude, and competitive behaviour. This paper presents a cost-effective computer vision framework using UAV videos to analyse right-turning behaviour and assess safety and operational performance (congestion) at uncontrolled T-intersections. A conflict cone of a vehicle is defined to automatically detect a right-of-way violation (RoWV) and yield. The impact of driving-related parameters and external traffic on non-compliant behaviour is analysed using the Tweedie generalised linear model. This paper proposes an aggregated surrogate safety measure, condPET, and a novel parameter, congValue, to identify critical conflicts and congestion due to non-compliant behaviour. Lateral evasive action is used to detect a constrained path because of nonchalant and competitive behaviours. The results indicate that only 7.50% of vehicles yielded, 6.25% of conflicts were critical (compared to 38.94% using PET alone and 11.05% using CS), and localised congestion occurred for 44.00% of the total video time. Overall, 45.34% of vehicles created a constrained path, and 26.00% committed RoW violations, causing congestion and increasing the average travel time on major roads by 2.0 and 3.5 times, respectively. Our methodology enables computer vision-based automated assessments of both road traffic safety and operational performance at uncontrolled T-intersections under non-uniform traffic conditions, providing a valuable tool for road-traffic-monitoring systems. Full article

Background: Four-way shuttle-based storage and retrieval systems (FSS/RSs) have recently emerged as flexible and scalable solutions for high-density warehousing, enabling shuttle movement in four directions and supporting multi-deep dual-access storage configurations. However, these features increase the complexity of vehicle coordination and collision management. This study proposes a multi-agent path-planning methodology for FSS/RSs with multi-deep dual-access lanes hosting homogeneous items. Methods: An A*-based path-planning framework was developed and integrated with a dynamic collision-management strategy comprising collision detection, priority assignment, and collision avoidance. The methodology was evaluated through a multi-scenario analysis considering different fleet sizes, priority-assignment strategies, safety-area extensions, transaction-entry patterns, and collision-management policies. Results: The results show that fleet size is the most influential operational parameter, significantly affecting throughput, waiting times, and collision frequency. Increasing the number of vehicles improves system productivity but also increases traffic interactions and congestion. The analyses further highlight the effects of safety-area size, priority rules, and transaction-entry patterns on operational performance and system robustness. Conclusions: The proposed methodology effectively combines path planning and collision management in four-way shuttle systems, providing a decision-support tool for evaluating operational trade-offs among throughput, congestion control, and system stability in multi-deep dual-access warehouse environments. Full article

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. Full article

Conventional object detection models suffer from significant performance degradation in dense urban traffic scenarios. To address these critical limitations and enable accurate real-time road congestion decision making, this study proposes an optimized YOLOv8-based detection paradigm that decouples multi-scale feature enhancement from dynamic focused bounding box regression. Specifically, a multi-scale feature enhancement (MFE) module is designed to extract high-resolution shallow features directly from the P2 layer of the YOLOv8 backbone. Then, a convolutional block attention module (CBAM) is embedded into the feature fusion neck to adaptively filter complex urban background noise and recalibrate channel–spatial feature responses for vehicle target saliency. Furthermore, the standard CIoU loss is replaced with the Wise-IoU (WIoU) dynamic focusing loss function, which suppresses gradient interference from low-quality, occluded samples and stabilizes bounding box regression for dense vehicle targets. The high-precision vehicle detection outputs are fed into a quantitative congestion index (CI) model, which fuses vehicle density and average speed to realize real-time congestion-level classification. Extensive experiments on the public UAVDT benchmark dataset demonstrate that the proposed model achieves an mAP@0.5 of 83.1% (3.8 percentage points higher than the YOLOv8 baseline), an mAP_S (small target) of 23.2% (a 4.3 percentage point improvement), and a real-time congestion decision accuracy of 83.8%. Ablation studies verify the independent and synergistic effectiveness of the MFE, CBAM, and WIoU modules, with the MFE module making the greatest contribution to small-target detection performance (+1.7% mAP@0.5). The proposed model maintains a real-time inference speed of 86 FPS (frames per second) on an NVIDIA RTX 3090 GPU, far exceeding the 30 FPS threshold for real-time traffic monitoring. Full article

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

The smart environment hides numerous security challenges that need to be addressed promptly. Smart cities have emerged as a novel concept, integrating emerging technologies and data-driven solutions to improve urban living conditions. Traffic surveillance cameras at intersections enable continuous traffic monitoring and rapid incident detection, optimizing signal timing to improve road safety and reduce traffic congestion and travel delay. These cities present new challenges for the police force, forcing them to blend into the environment. The paper proposes novel hierarchical Police Internet of Things (PIoT) concepts that should enable and secure timely, high-priority policing forecasting and decision-making processes in smart cities. Hierarchical edge, fog, and cloud computing were presented according to the police decision-making process. This concept is carefully developed to improve the timeliness of predictive policing, planning, management, and decision-making using artificial intelligence and fuzzy logic. The proposed vertical PIoT concept is supported by vertical data processing. In hierarchical computing, machine learning models for time series prediction and fuzzy-logic-based decision-making are applied to enable comprehensive analysis in a smart environment. Two case studies dealing with crime and traffic issues are presented in detail. 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

With the rapid development of society, the number of road vehicles has increased significantly, leading to a growing severity of traffic accident issues. Timely and accurate detection of road traffic anomalies or accidents is crucial for reducing fatalities and alleviating traffic congestion. Consequently, the detection of road traffic anomalies has become a focal point of research in recent years. With the assistance of computer technologies such as deep learning, researchers have developed more accurate and effective methods for detecting road traffic anomalies. However, the small proportion of anomaly-prone areas in surveillance video frames, combined with the complex and difficult-to-capture patterns of accidents, presents new challenges for the application of deep models to traffic anomaly detection from a surveillance perspective. In light of this, this paper annotates the TADS dataset we previously proposed, a popular text-assisted video representation learning method, to develop a more efficient detection method. Utilizing the well-known video-text model CLIP, we have constructed a detection model that leverages unique text and eye-gaze annotation data from the TADS dataset to learn anomaly representations more effectively, thereby improving the detection of road traffic anomalies from a surveillance perspective. Experimental results demonstrate the superiority of our model for detecting traffic anomalies from a surveillance perspective, as well as the utility of the text and eye-gaze data included in the dataset. Full article