Search Results (111)

Traffic anomaly detection is crucial for urban management, yet current research is often confined to small-scale endeavors. This study collected 9 months of real-time Wuhan traffic-monitoring data from Amap. We propose Traffic-ConvLSTM, a multi-scale spatial-temporal technique based on long short-term memory (LSTM) networks and convolutional neural networks (CNNs) to effectively achieve long-term anomaly detection at the city level. First, we converted traffic track points into an image representation, which enables spatial correlation between traffic flow and roads and correlations between traffic flow and roads, as well as the surrounding environment, to be captured. Second, the model utilizes convolution kernels of different sizes to extract spatial features at road-, regional-, and city-level scales while incorporating the temporal features of different time steps to capture hourly, daily, and weekly dynamics. Additionally, varying weights are assigned to the convolution kernels and temporal features of varying spatio-temporal scales to capture the heterogeneous strengths of spatio-temporal correlations within patterns of traffic anomalies. The proposed Traffic-ConvLSTM model exhibits improved performance over existing techniques in the task of identifying long-term and large-scale traffic anomaly occurrences. Furthermore, the analysis reveals significant traffic anomalies during holidays and urban sporting events. The diverse travel patterns observed in response to various activities offer insights for large-scale urban traffic anomaly management, providing recommendations for city-level traffic-control strategies. Full article

Acoustic perception of the automotive environment has the potential to advance driving potentials with enhanced safety. The challenge arises when these acoustic perception systems need to perform under resource and power constraints on edge devices. Neuromorphic computing has introduced spiking neural networks in the context of ultra-low power sensory edge devices. Spiking architectures leverage biological plausibility to achieve computational capabilities, accurate performance, and great compatibility with neuromorphic hardware. In this work, we explore the depths of spiking neurons and feature components with the acoustic scene analysis task for siren sounds. This research work aims to address the qualitative analysis of sliding windows’ variation on the feature extraction front of the preprocessing pipeline. Optimization of the parameters to exploit the feature extraction stage facilitates the advancement of the performance of the acoustics anomaly detection task. We exploit the parameters for mel spectrogram features and FFT calculations, prone to be suitable for computations in hardware. We conduct experiments with different window sizes and the overlapping ratio within the windows. We present our results for performance measures like accuracy and onset latency to provide an insight on the choice of optimal window. The non-trivial motivation of this research is to understand the effect of encoding behavior of spiking neurons with different windows. We further investigate the heterogeneous nature of membrane and synaptic time constants and their impact on the accuracy of anomaly detection. On a large scale audio dataset comprising of siren sounds and road traffic noises, we obtain accurate predictions of siren sounds using a recurrent spiking neural network. The baseline dataset comprising siren and noise sequences is enriched with a bird dataset to evaluate the model with unseen samples. Full article

This paper explores new sensor technologies and their integration within Connected Autonomous Vehicles (CAVs) for real-time road condition monitoring. Sensors like accelerometers, gyroscopes, LiDAR, cameras, and radar that have been made available on CAVs are able to detect anomalies on roads, including potholes, surface cracks, or roughness. This paper also describes advanced data processing techniques of data detected with sensors, including machine learning algorithms, sensor fusion, and edge computing, which enhance accuracy and reliability in road condition assessment. Together, these technologies support instant road safety and long-term maintenance cost reduction with proactive maintenance strategies. Finally, this article provides a comprehensive review of the state-of-the-art future directions of condition monitoring systems for traditional and smart roads. Full article

Unmanned aerial vehicles (UAVs) are effective tools for identifying road anomalies with limited detection coverage due to the discrete spatial distribution of roads. Despite computational, storage, and transmission challenges, existing detection algorithms can be improved to support this task with robustness and efficiency. In this study, the K-means clustering algorithm was used to calculate the best prior anchor boxes; Faster R-CNN (region-based convolutional neural network), YOLOX-s (You Only Look Once version X-small), YOLOv5-s, YOLOv7-tiny, YOLO-MobileNet, and YOLO-RDD models were built based on image data collected by UAVs. YOLO-MobileNet has the most lightweight model but performed worst in accuracy, but greatly reduces detection accuracy. YOLO-RDD (road distress detection) performed best with a mean average precision (mAP) of 0.701 above the Intersection over Union (IoU) value of 0.5 and achieved relatively high accuracy in detecting all four types of distress. The YOLO-RDD model most successfully detected potholes with an AP of 0.790. Significant or severe distresses were better identified, and minor cracks were relatively poorly identified. The YOLO-RDD model achieved an 85% computational reduction compared to YOLOv7-tiny while maintaining high detection accuracy. Full article

Wind speed differences between weekdays and holidays at urban sites in Japan were investigated in search of the influence of urban anthropogenic heat on surface wind speed using data from the Automated Meteorological Data Acquisition System (AMeDAS) of the Japan Meteorological Agency (JMA) for 44 years. The wind speed was found to be lower on holidays than on weekdays, not only in large cities but also in areas with medium degrees of urbanization, which is interpreted to be due to the stronger stability of the surface boundary layer under lower temperatures with smaller amounts of anthropogenic heat. The rate of decrease is about −3% in central Tokyo, and about −0.5% for the average over stations with population densities between 1000 and 3000 km⁻². Additionally, an analysis using the spatially dense data on the Air Pollution Monitoring System of Tokyo Metropolis for 28 years showed that negative anomalies in wind speed on holidays were detected at many stations in the Tokyo Wards Area, although negative temperature anomalies were limited to a few stations in the central area or near big roads, suggesting different spatial scales in the response of temperature and wind speed to anthropogenic heat. Full article

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures. Full article

Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver’s arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system. Full article

Urban transport has evolved based on three main visions: automobility, multimodality, and accessibility. The first dominates North American cities; the second, European; the third, significantly discussed in the literature, is still in the early stages of practical development, with a few limited examples. Each of the first two visions has an aligned planning paradigm to support aspirational goals and future directions. But implementation has been disappointing, owing to the appearance of anomalies; that is, unanticipated and unexplained mismatches between the vision and the paradigms that refuse to be resolved. The attempts are self-defeating, and result, for example, in congestion and road accidents. A review of the literature with some new insights can shed light on the problems and the anomalies of these two visions. For the third vision, a new paradigm has been proposed based on accessibility and polycentric and multi-timed cities, promising new insights and breakthroughs in the way of thinking about transport and cities. Some practical examples of accessibility cities are presented with a focus on people, places, land use changes, telecommunications, transportation demand management (TDM), and public and non-motorized transport. Some minor anomalies are discussed. In conclusion, enhancing accessibility in cities is crucial for creating more inclusive and sustainable urban environments that are less dependent on cars, but this vision and this paradigm still require further development to be accepted and implemented. Full article

In central Sakha (Yakutia) Republic, groundwater icings, primarily formed by intrapermafrost water, are less prone to contamination and serve as a stable freshwater resource. The periodic growth of icings threatens infrastructure such as roads, railways, and bridges in permafrost areas. Therefore, research in this field has become urgently necessary. This study aims to analyze the impacts of various factors on the scale of icing formation using Landsat satellite data, Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO) data, Global Land Data Assimilation System (GLDAS) data, and field observation results. The results showed that the surface area of icings in the study area showed an overall increasing trend from 2002 to 2022, with an average growth rate of 0.06 km²/year. Suprapermafrost water and intrapermafrost water are the main sources of icings in the study area. The total Groundwater Storage Anomaly (GWSA) values from October to April showed a strong correlation with the maximum icing areas. Icings fed by suprapermafrost water were influenced by precipitation in early autumn, while those fed by intrapermafrost water were more affected by talik size and distribution. Climate warming contributed to the degradation of the continuous permafrost covering an area of 166 km² to discontinuous permafrost, releasing additional groundwater. This may also be one of the reasons for the observed increasing trend in icing areas. This study can provide valuable insights into water resource management and infrastructure construction in permafrost regions. Full article

Orb web spiders are common and highly diversified animals found in almost all habitats. They have remarkable plasticity against biotic and abiotic factors, making them excellent indicators of environmental health. The web creation behavior of spiders is influenced by disturbances in the environment. The aim of this research was to observe the alteration in the web-building behavior of Neoscona vigilans caused by human activities, specifically traffic disturbances. Spider webs were located and photographed at nighttime along the roadside, and their web characteristics were calculated. Spiders were captured from webs for their body measurements. Spider fourth leg length, carapace width, and body length had a significant association with web size and diameter, CTL, capture area, and mesh size. The quantity of trapped prey, the height of the plant, and the foliage radius increased with the distance from the road. Conversely, anchor points and web elevation from the ground dropped. The highest and lowest proportions of anomalies (modifications/defects) were recorded as holes (52.7%) in 105 webs (100%) and supernumerary (0.7%) in 55 webs (52.4%), respectively. Road disturbance had a negative influence on the spider’s behavior as the webs formed in close proximity to the road had a higher frequency of anomalies, with a gradual decrease distantly. We can gain further insight into how different environmental changes, disruptions, and pollutants lead to this imperfection in the otherwise flawless perfect structure of spider webs. Full article

The accurate prediction of urban road collapses is of paramount importance for public safety and infrastructure management. However, the complex and variable nature of road subsidence mechanisms, coupled with the inherent noise and non-stationarity in the data, poses significant challenges to the development of precise and real-time prediction models. To address these challenges, this paper develops an Adaptive Difference Least Squares Support Vector Regression (AD-LSSVR) model. The AD-LSSVR model employs a difference transformation to process the input and output data, effectively reducing noise and enhancing model stability. This transformation extracts trends and features from the data, leveraging the symmetrical characteristics inherent within it. Additionally, the model parameters were optimized using grid search and cross-validation techniques, which systematically explore the parameter space and evaluate model performance of multiple subsets of data, ensuring both precision and generalizability of the selected parameters. Moreover, a sliding window method was employed to address data sparsity and anomalies, ensuring the robustness and adaptability of the model. The experimental results demonstrate the superior adaptability and precision of the AD-LSSVR model in predicting road collapse timing, highlighting its effectiveness in handling the complex nonlinear data. Full article

The importance of monitoring in assessing structural safety and durability continues to grow. With recent technological advancements, Internet of Things (IoT) sensors have garnered attention for their complex scalability and varied detection capabilities, becoming essential devices for monitoring. However, during the data collection process of IoT sensors, anomalies arise due to network instability, sensor noise, and malfunctions, degrading data quality and compromising monitoring system reliability. In this study, Interquartile Range (IQR), Long Short-Term Memory Autoencoder (LSTM-AE), and time-series decomposition were employed for anomaly detection in Structural Health Monitoring (SHM) processes. IQR and LSTM-AE produce irregular patterns; however, time-series decomposition effectively detects such anomalies. In road monitoring influenced by weather and traffic, the time-series decomposition approach is expected to play a crucial role in enhancing monitoring accuracy. Full article

Obstacle avoidance is essential for the effective operation of autonomous mobile robots, enabling them to detect and navigate around obstacles in their environment. While deep learning provides significant benefits for autonomous navigation, it typically requires large, accurately labeled datasets, making the data’s preparation and processing time-consuming and labor-intensive. To address this challenge, this study introduces a transfer learning (TL)-based automatic labeling segmentation (ALS) framework. This framework utilizes a pretrained attention-based network, DifferNet, to efficiently perform semantic segmentation tasks on new, unlabeled datasets. DifferNet leverages prior knowledge from the Cityscapes dataset to identify high-entropy areas as road obstacles by analyzing differences between the input and resynthesized images. The resulting road anomaly map was refined using depth information to produce a robust drivable area and map of road anomalies. Several off-the-shelf RGB-D semantic segmentation neural networks were trained using pseudo-labels generated by the ALS framework, with validation conducted on the GMRPD dataset. Experimental results demonstrated that the proposed ALS framework achieved mean precision, mean recall, and mean intersection over union (IoU) rates of 80.31%, 84.42%, and 71.99%, respectively. The ALS framework, through the use of transfer learning and the DifferNet network, offers an efficient solution for semantic segmentation of new, unlabeled datasets, underscoring its potential for improving obstacle avoidance in autonomous mobile robots. Full article

Connected and automated vehicles (CAVs), integrated with sensors, cameras, and communication networks, are transforming the transportation industry and providing new opportunities for consumers to enjoy personalized and seamless experiences. The fast proliferation of connected vehicles on the road and the growing trend of autonomous driving create vast amounts of data that need to be analyzed in real time. Anomaly detection in CAVs refers to identifying any unusual or unforeseen behavior in the data generated by vehicles’ various sensors and components. Anomaly detection aims to identify any unusual behavior that might indicate a problem or a malfunction in the vehicle. To identify and detect anomalies efficiently, a method must deal with noisy data, missing data, dynamic frequency data, and low- and high-magnitude data, and it must be accurate enough to detect anomalies in a dynamic sensor streaming environment. Therefore, this paper proposes a fast and efficient hard-voting-based technique named FT-HV, comprising three fine-tuned machine learning algorithms to detect and classify anomaly behavior in CAVs for single and mixed sensory datasets. In experiments, we evaluate our approach on the benchmark Sensor Anomaly dataset that contains data from various vehicle sensors at low and high magnitudes. Further, it contains single and mixed anomaly types that are challenging to detect and identify. The results reveal that the proposed approach outperforms existing solutions for detecting single anomaly types at low magnitudes and detecting mixed anomaly types in all settings. Furthermore, this research is envisioned to help detect and identify anomalies early and efficiently promote safer and more resilient CAVs. Full article

Detecting cracks in the pavement is a vital component of ensuring road safety. Since manual identification of these cracks can be time-consuming, an automated method is needed to speed up this process. However, creating such a system is challenging due to factors including crack variability, variations in pavement materials, and the occurrence of miscellaneous objects and anomalies on the pavement. Motivated by the latest progress in deep learning applied to computer vision, we propose an effective U-Net-shaped model named DepthCrackNet. Our model employs the Double Convolution Encoder (DCE), composed of a sequence of convolution layers, for robust feature extraction while keeping parameters optimally efficient. We have incorporated the TriInput Multi-Head Spatial Attention (TMSA) module into our model; in this module, each head operates independently, capturing various spatial relationships and boosting the extraction of rich contextual information. Furthermore, DepthCrackNet employs the Spatial Depth Enhancer (SDE) module, specifically designed to augment the feature extraction capabilities of our segmentation model. The performance of the DepthCrackNet was evaluated on two public crack datasets: Crack500 and DeepCrack. In our experimental studies, the network achieved mIoU scores of 77.0% and 83.9% with the Crack500 and DeepCrack datasets, respectively. Full article