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In the field of traffic management and control systems, we are witnessing a symbiotic evolution, where intelligent infrastructure is progressively collaborating with smart vehicles to produce benefits for traffic monitoring and security, by rapidly identifying hazardous behaviours. This exponential growth is due to the rapid development of deep learning in recent years, as well as the improvements in computer vision models. These technologies allow for monitoring tasks without the need to install numerous sensors or stop the traffic, using the extensive camera network of surveillance cameras already present in worldwide roads. This study proposes a computer vision-based solution that allows for real-time processing of video streams through edge computing devices, eliminating the need for Internet connectivity or dedicated sensors. The proposed system employs deep learning algorithms and vision techniques that perform vehicle detection, classification, tracking, speed estimation, and vehicle geolocation. [ABSTRACT FROM AUTHOR]

The article describes the problem of excessive traffic concentration at signalized pedestrian crossings. The main drawback of such crossings is the inappropriate duration of green signals for vehicles. Too long green times for vehicular traffic significantly increase time lost by pedestrians, while too short ones cause short-term congestion, resulting in long queues of vehicles and a deteriorated level of service. The negative effects of both of these situations also include dangerous behavior of road users, such as red light running or crossing. Recent (June 2021) changes in the road traffic law in Poland have privileged unprotected road users at pedestrian crossings. New models and technical and design standards proposed in this country (March 2021) recommend the installation of pedestrian priority crossings at particularly dangerous spots and in urban areas with a high proportion of pedestrian traffic. All this is done mainly for traffic-calming purposes. However, previously these measures were not a common practice in Poland. Therefore, the objective of this article was to develop a simple method of green signal timing, ensuring, firstly, priority to pedestrian traffic at such places, and, secondly, sustained capacity of vehicular traffic lanes. The developed models are practical, universal and easy to adapt in other countries. They ensure a very good or good quality of vehicle traffic service, with average lost time not exceeding 25 s in a vast majority of analyzed traffic level cases. The article also describes a traffic control algorithm dependent on traffic detection. [ABSTRACT FROM AUTHOR]

Against the backdrop of ongoing urbanization, issues such as traffic congestion and accidents are assuming heightened prominence, necessitating urgent and practical interventions to enhance the efficiency and safety of transportation systems. A paramount challenge lies in realizing real-time vehicle monitoring, flow management, and traffic safety control within the transportation infrastructure to mitigate congestion, optimize road utilization, and curb traffic accidents. In response to this challenge, the present study leverages advanced computer vision technology for vehicle detection and tracking, employing deep learning algorithms. The resultant recognition outcomes provide the traffic management domain with actionable insights for optimizing traffic flow management and signal light control through real-time data analysis. The study demonstrates the applicability of the SE-Lightweight YOLO algorithm, as presented herein, showcasing a noteworthy 95.7% accuracy in vehicle recognition. As a prospective trajectory, this research stands poised to serve as a pivotal reference for urban traffic management, laying the groundwork for a more efficient, secure, and streamlined transportation system in the future. To solve the existing vehicle detection problems in vehicle type recognition, recognition and detection accuracy need to be improved, alongside resolving the issues of slow detection speed, and others. In this paper, we made innovative changes based on the YOLOv7 framework: we added the SE attention transfer mechanism in the backbone module, and the model achieved better results, with a 1.2% improvement compared with the original YOLOv7. Meanwhile, we replaced the SPPCSPC module with the SPPFCSPC module, which enhanced the trait extraction of the model. After that, we applied the SE-Lightweight YOLO to the field of traffic monitoring. This can assist transportation-related personnel in traffic monitoring and aid in creating big data on transportation. Therefore, this research has a good application prospect. [ABSTRACT FROM AUTHOR]

Due to the rapid increase of the population in the world, traffic signal controlling and monitoring has become an important issue to be solved with regard to the direct relation between the number of populations and the cars' usage. In this regard, an intelligent traffic signaling with a rapid urbanization is required to prevent the traffic congestions, cost reduction, minimization in travel time, and CO2 emissions to atmosphere. This paper provides a comprehensive review of computer vision techniques for autonomic traffic control and monitoring. Moreover, recent published articles in four related topics including density estimation investigation, traffic sign detection and recognition, accident detection, and emergency vehicle detection are investigated. The conducted survey shows that there is no fair comparison and performance evaluation due to the large number of involved parameters in the abovementioned four topics which can control the traffic signal controlling system such as (computation time, dataset availability, and an accuracy). [ABSTRACT FROM AUTHOR]

A distributed firewall is a security application that monitors and controls traffic on an organization's network. While centralized firewalls are used against attacks coming from outside a network, distributed firewalls are considered for inside attacks from internal networks such as wireless access and VPN tunnel. Distributed firewalls use policies, which are stated by rules, to find anomalous packets. However, such static rules may be incomplete. In this case, by monitoring firewall logs, the anomalies can be detected. Such logs become big when networks have high traffic, but their hidden knowledge contains valuable information about existing anomalies. In this paper, to detect the anomalies, we extract patterns from big data logs of distributed firewalls using data mining and machine learning. The proposed method is applied to big logs from distributed firewalls in a real security environment, and results are analyzed. [ABSTRACT FROM AUTHOR]

Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve the efficiency of traffic management on highways. Due to the fact that this type of classification problem has scarcely been proposed, we have adapted various classification algorithms and corresponding datasets specifically designed for detecting special traffic events. By utilizing datasets containing data on road debris and malfunctioning or crashed vehicles obtained from Korean highways, we demonstrate the feasibility of our algorithms. Our main contributions encompass a thorough adaptation of various deep-learning algorithms and class definitions aimed at detecting actual emergencies on highways. We have also developed a dataset and detection algorithm specifically tailored for this task. Furthermore, our final end-to-end algorithm showcases a notable 9.2% improvement in performance compared to the object accident detection-based algorithm. [ABSTRACT FROM AUTHOR]

The real-time vehicular traffic system is an integral part of the urban vehicular traffic system, which provides effective traffic signal control for a large multifaceted traffic network and is a highly challenging distributed control problem. Coordinating vehicular traffic enables the network model to deliver an efficient service flow. Consider that there are four lanes of vehicular traffic in this situation, allowing parallel vehicle movements to occur without causing an accident. In this instance, the vehicular system's control parameters are time and vehicle volume. In this work, vehicular traffic flow is examined, and an algorithm to estimate vehicle waiting time in each direction is estimated. The effectiveness of the proposed vehicle traffic signal distribution control system by comparing the experimental results with a real-time vehicular traffic system is verified. This is also illustrated numerically. [ABSTRACT FROM AUTHOR]

High quality road infrastructure is essential to support economic growth for any region. For South Africa’s landlocked economic hub 79% of goods are transported using roads infrastructure. Protection of the road infrastructure is implemented by means of overload control monitoring at traffic control centres (TCCs) on freight corridors. Statistics collected from TCC operations indicate that 75% to 85% of statically weighed vehicles are legally loaded, with the implication that unnecessary time was wasted for these vehicles. This paper therefore proposes an algorithm, called the intelligent weigh-in-motion (IWIM) algorithm, with the purpose to decrease static weighing of vehicles by implementing data sharing between TCCs on the freight corridor, combined with intelligent interpretation of this data. The selected algorithm was chosen after testing multiple artificial intelligence (AI) models (logistic regression, random forest tree, and artificial neural network) to achieve the best performance to decrease static weighing of vehicles while not increasing the number of overloaded vehicles allowed to proceed on the corridor. The best performing model to differentiate between overloaded and legal vehicles, random forest tree, achieved an average improvement of 65,83% in terms of vehicles to be statically weighed when compared to the current rule-based system employed at TCCs. [ABSTRACT FROM AUTHOR]

Aerial vehicle detection has significant applications in aerial surveillance and traffic control. The pictures captured by the UAV are characterized by many tiny objects and vehicles obscuring each other, significantly increasing the detection challenge. In the research of detecting vehicles in aerial images, there is a widespread problem of missed and false detections. Therefore, we customize a model based on YOLOv5 to be more suitable for detecting vehicles in aerial images. Firstly, we add one additional prediction head to detect smaller-scale objects. Furthermore, to keep the original features involved in the training process of the model, we introduce a Bidirectional Feature Pyramid Network (BiFPN) to fuse the feature information from various scales. Lastly, Soft-NMS (soft non-maximum suppression) is employed as a prediction frame filtering method, alleviating the missed detection due to the close alignment of vehicles. The experimental findings on the self-made dataset in this research indicate that compared with YOLOv5s, the mAP@0.5 and mAP@0.5:0.95 of YOLOv5-VTO increase by 3.7% and 4.7%, respectively, and the two indexes of accuracy and recall are also improved. [ABSTRACT FROM AUTHOR]

Effective accident detection and traffic flow forecasting are of great importance for quick respond, impact elimination and intelligent control of the traffic flow consisting of autonomous vehicles. This paper proposes a traffic accident detection method for connected and automated transport systems by conducting a grid-based parameter extracting and SVC-based traffic state classification. Allowing for the dynamic spread of traffic flow over time, from upstream to downstream and from accident lanes to other lanes, a spatiotemporal Markov model is established to predict the evolution of traffic flow after accident by introducing the grid as state detection unit and fitting the spatiotemporal evolution with the parameter space mean speed to match the need of both detection accuracy and monitoring scope. Compared with actual accident data, the validation results indicate that the proposed methods present a good performance in accident detection with the accident detection rate as 87.72% and a higher precision rate than both SVM (support vector machine) and ANN (artificial neural network) models in traffic flow prediction. With the active traffic accident identification and dynamic traffic flow prediction, it is beneficial to shorten detection time, reduce possible impacts of traffic accidents and carbon emissions from congestion. The methods can be implied to traffic state recognition and traffic flow prediction, which is one of the significant sections of connected and automated transport systems, and serve as references for accident handling and urban traffic management. [ABSTRACT FROM AUTHOR]

As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context. [ABSTRACT FROM AUTHOR]

Vehicle classification is a challenging task in the area of image processing. It involves the classification of various vehicles based on their color, model, and make. A distinctive variety of vehicles belonging to various model categories have been developed in the automobile industry, which has made it necessary to establish a compact system that can classify vehicles within a complex model group. A well-established vehicle classification system has applications in security, vehicle monitoring in traffic cameras, route analysis in autonomous vehicles, and traffic control systems. In this paper, a hybrid model based on the integration of a pre-trained Convolutional Neural Network (CNN) and an evolutionary feature selection model is proposed for vehicle classification. The proposed model performs classification of eight different vehicle categories including sports cars, luxury cars and hybrid power-house SUVs. The used in this work is derived from Stanford car dataset that contains almost 196 cars and vehicle classes. After performing appropriate data preparation and preprocessing steps, feature learning and extraction is carried out using pre-trained VGG16 first that learns and extracts deep features from the set of input images. These features are then taken out of the last fully connected layer of VGG16, and feature optimization phase is carried out using evolution-based nature-inspired optimization model Genetic Algorithm (GA). The classification is performed using numerous SVM kernels where Cubic SVM achieves an accuracy of 99.7% and outperforms other kernels as well as excels in terns of performance as compared to the existing works. [ABSTRACT FROM AUTHOR]

The paper's main aim is to present the impact on the city's road traffic generated by the Port of Gdynia's operations and propose the optimal solution for transport network development around the port. Firstly, the authors demonstrate a case study determining the impact of heavy goods vehicles (HGVs) generated by port facilities on local traffic. To this end, the average travel time of cars in the network on selected measurement sections is conditioned on the varying number of HGVs generated by the port. Next, based on the data obtained from the traffic monitoring system, PTV Vissim software is used as a modelling tool to analyse and assess the impact on local traffic. Finally, considering the analysis' results, the vulnerability of the transport network is discussed. The optimal solution for the transport network around the port's area is proposed. The paper is an extended version of the materials presented at the XIX Maritime Traffic Engineering Conference. [ABSTRACT FROM AUTHOR]

Road safety is a major global concern, as millions of lives are lost every year because of road accidents. Towards an effort to increase road safety, several Internet-of-Vehicle systems have been developed over the last years in order to better monitor vehicle and driver behavior and issue warnings that effectively prevent life-threatening accidents. These systems face a number of challenges including connectivity issues and high installation and/or maintenance costs. The current work introduces the ODOS2020 system, an integrated Internet-of-Vehicles system aiming to increase road safety. The system comprises several On-the-Road Units for vehicle-related data collection from affordable, energy-efficient magnetometers and calculation of critical parameters, such as each passing vehicle's speed and direction. A Road-Side Unit accumulates data from the On-the-Road Units, sends data to a cloud infrastructure for further analysis and sends dedicated warnings to the drivers based on their road behavior and/or specific traffic conditions via a dedicated Human–Machine Interface. The overall system architecture and the key features of its modules are being presented, as well as the evaluation results of specially designed tests performed in an actual motorway under real use case scenarios. The evaluation results showed both a very good technical performance of the system and a high level of user acceptance. This in turn means that the system can be employed for effective traffic control and road accident avoidance via monitoring of critical vehicle parameters and early warning of the drivers based on their and other drivers' behavior, road conditions and real-time, unpredictable events. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning provides a new approach to solving complex signal optimization problems at intersections. Earlier studies were limited to traditional traffic detection techniques, and the obtained traffic information was not accurate. With the advanced in technology, we can obtain highly accurate information on the traffic states by advanced detector technology. This provides an accurate source of data for deep reinforcement learning. There are many intersections in the urban network. To successfully apply deep reinforcement learning in a situation closer to reality, we need to consider the problem of extending the knowledge gained from the training to new scenarios. This study used advanced sensor technology as a data source to explore the variation pattern of state space under different traffic scenarios. It analyzes the relationship between the traffic demand and the actual traffic states. The model learned more from a more comprehensive state space of traffic. This model was successful applied to new traffic scenarios without additional training. Compared our proposed model with the popular SAC signal control model, the result shows that the average delay of the DQN model is 5.13 s and the SAC model is 6.52 s. Therefore, our model exhibits better control performance. [ABSTRACT FROM AUTHOR]

The intersection management system can increase traffic capacity, vehicle safety, and the smoothness of all vehicle movement. Platoons of connected vehicles (CVs) use communication technologies to share information with each other and with infrastructures. In this paper, we proposed a deep reinforcement learning (DRL) model that applies to vehicle platooning at an isolated signalized intersection with partial detection. Moreover, we identified hyperparameters and tested the system with different numbers of vehicles (1, 2, and 3) in the platoon. To compare the effectiveness of the proposed model, we implemented two benchmark options, actuated traffic signal control (ATSC) and max pressure (MP). The experimental results demonstrated that the DRL model has many outstanding advantages compared to other models. Through the learning process, the average waiting time of vehicles in the DRL method was improved by 20% and 28% compared with the ATSC and MP options. The results also suggested that the DRL model is effective when the CV penetration rate is over 20%. [ABSTRACT FROM AUTHOR]

Predicting spatiotemporal congestion situations of a traffic network is a prerequisite for urban traffic control. This study proposes a spatiotemporal traffic congestion situation prediction method based on the recurrent gated unit-convolutional neural network (GRU-CNN). Considering the time and space attributes of traffic data, the third-order tensor of the traffic data is extracted from the time domain, and the GRU is used to predict the traffic flow parameters of the traffic network. Then, the third-order tensor of multisource spatiotemporal traffic data is compressed into traffic data images and combined with the spatial structure. The feature extraction technology of a CNN is used to extract and identify the traffic network congestion features. Actual urban traffic network data are selected for model verification. The multistep prediction of the traffic flow parameters effectively ensures prediction accuracy. The proposed model is trained by the actual classification dataset. The prediction results of the test set demonstrate the model's reliability. Based on predicting the traffic parameters of the network, this model can give a highly accurate judgment of the traffic situation for the entire network. Compared with other models, the proposed model further improves the accuracy of road network traffic state discrimination and has better robustness. [ABSTRACT FROM AUTHOR]

Detecting and counting on road vehicles is a key task in intelligent transport management and surveillance systems. The applicability lies both in urban and highway traffic monitoring and control, particularly in difficult weather and traffic conditions. In the past, the task has been performed through data acquired from sensors and conventional image processing toolbox. However, with the advent of emerging deep learning based smart computer vision systems the task has become computationally efficient and reliable. The data acquired from road mounted surveillance cameras can be used to train models which can detect and track on road vehicles for smart traffic analysis and handling problems such as traffic congestion particularly in harsh weather conditions where there are poor visibility issues because of low illumination and blurring. Different vehicle detection algorithms focusing the same issue deal only with on or two specific conditions. In this research, we address detecting vehicles in a scene in multiple weather scenarios including haze, dust and sandstorms, snowy and rainy weather both in day and nighttime. The proposed architecture uses CSPDarknet53 as baseline architecture modified with spatial pyramid pooling (SPP-NET) layer and reduced Batch Normalization layers. We also augment the DAWN Dataset with different techniques including Hue, Saturation, Exposure, Brightness, Darkness, Blur and Noise. This not only increases the size of the dataset but also make the detection more challenging. The model obtained mean average precision of 81% during training and detected smallest vehicle present in the image [ABSTRACT FROM AUTHOR]

Smart cities have been widely investigated, and several algorithms, techniques, and protocols have recently been developed to serve smart city environments. Intelligent traffic management is one of the main research fields in this area. It aims to control and monitor the traffic over downtown and highway scenarios. The real-time traffic characteristics on road networks highly affect safety conditions and driving behaviors. Detecting hazardous traffic conditions and congested areas early helps drivers make the best decision to avoid dangerous situations. After extensive training, machine learning algorithms are mainly used for prediction in this field. In the intelligent traffic control target, trained machines are beneficial in obtaining instant and accurate predictions regarding traffic congestion levels and their distributions. Several parameters control the efficiency of the selected machines. This includes the dataset quality, the regression algorithm's parameters, and how they have been tuned. In this work, we first create high-quality datasets representing the moving vehicles for different main roads in various countries, including Bank St in Canada, North Rodeo Drive (NRD) in Los Angeles, USA, Queen Rania Al Abdullah (QRA) road in Jordan, and Sheikh Zayed Road (SZR) in Saudi Arabia. Then, we test the most popular regression algorithms and evaluate their performance in terms of temporal prediction of traffic characteristics and traffic congestion in different geographical road scenarios. We optimized these regression algorithms and tuned the used parameters using the grid search approach. After that, more advanced metaheuristic optimization algorithms were used to improve the prediction accuracy of the selected machine. Experimentally, we have proved the enhancements in predicting traffic characteristics for the tested road scenarios. Both approaches show enhanced results compared to using the regression algorithm without tuning the parameters. It shows advanced results for both approaches for the coefficient of determination measure, named R squared (R 2), with approximately 0.07, 0.04, and 0.08 enhanced values for Bank St., NRD, and SZR roads, respectively. In contrast, it shows approximately 0.95, 0.49, and 3.62 enhanced values for Bank St., NRD, and SZR roads, respectively, for the Root Mean Squared Error (R M S E) measure. [ABSTRACT FROM AUTHOR]

The performance of the traffic system can drastically drop when nonrecurrent congestion caused by incidents occurs. Early detection and clearing of traffic incidents will enable the mitigation of the congestion and early restoration of normal traffic conditions. The research in this paper utilized the vehicle information from the recent technological advancement in transportation systems, connected vehicles (CV), and loop-detector information for nonconnected vehicles (NCVs) and developed a novel algorithm to (1) control traffic signals for normal traffic conditions in the absence of incidents, (2) detect traffic incidents using CV/NCV information, and (3) control traffic signals during the occurrence and dissipation of incidents. All the 3 strategies were integrated into one algorithm, which runs as per the real-time traffic conditions, in the presence or absence of incidents. Space-mean speeds of the vehicles on nonincident lanes and throughput maximization criteria were taken as the indicators for the activation of specific signal timings directed at the incident-affected approach. Diverse incident scenarios were tested on a four-legged isolated intersection using the VISSIM simulation tool. Incident detection results showed a higher detection rate and lower mean detection time at higher CV penetration and higher traffic volumes, and at the incident locations nearer to the stop-line. The proposed incident-responsive signal control strategy at 40% and higher CV penetration showed better performance over EPICS adaptive signal control solution, in reducing average travel time delay and the average number of stops per vehicle. [ABSTRACT FROM AUTHOR]

The identification of the position of rail vehicles plays a crucial role in the control of rail traffic. Available, up-to-date information on the position of vehicles allows us to efficiently deal with selected traffic situations where the position of vehicles is very important. The main objective of this article is to introduce (i) a concept of a solution for identification of the current position of rail vehicles based on the worldwide-recognized system of the GNSS with the use of an original railway network data model, and (ii) the use of this concept as supplementary support for the dispatcher control of rail traffic on regional lines. The solution was based on an original, multilayer rail network data model supporting (i) the identification of rail vehicle position and (ii) novel algorithms evaluating the mutual positions of several trains while detecting the selected crisis situation. In addition, original algorithms that enable automatic network model-building (on the database server level) directly from the official railway infrastructure database were developed. The verification of the proposed solutions (using rail traffic simulations) was focused on the evaluation of (i) the changing mutual positions (distances) of trains on the railway network, (ii) the detection of nonstandard or crisis traffic situations, and (iii) the results of the calculations of necessary braking distances of trains for stopping and collision avoidance. The above verification demonstrated the good applicability of the proposed solutions for the potential deployment within supplementary software support for real traffic control. The described concept of the supplementary support determined for railway traffic control (using the localization of trains by means of the GNSS) is intended mainly for regional, single-rail lines. This type of line is very often not sufficiently equipped with standard signaling and interlocking equipment to ensure the necessary traffic safety. Therefore, when deploying this support, the new algorithms for the automatic detection of critical traffic situations represent a significant potential contribution to increasing operational safety. [ABSTRACT FROM AUTHOR]

Intelligent transportation infrastructure has gained significant research attention recently. In this paper, an innovative sensor network of smart road stud (SRS) is developed to enhance traffic detection infrastructure characterized by its functionality in traffic data collection, long/short range wireless data transmission, self-sustained power supply, and remote custom controlled lighting-based traffic guidance. Compared to the traditional traffic detectors and road studs, SRS nodes are installed on lane lines instead of lane center to enable the additional applications besides the detection function, such as traffic monitoring, congestion warning, routing guidance, and so on. SRS detects vehicles based on three-axis geomagnetic sensors. A vehicle detection algorithm is proposed correspondingly under different operation scenarios to count vehicles in two adjacent lanes. Its detecting accuracy can be further improved by a sensor network of multiple SRSs working cooperatively. Field test results show that the vehicle detection accuracy based on the SRS network is about 98% per lane, which is the same level as the commercial detector installed in center of lane, even under the non-standard driving behaviors such as crossing lane line. The high performance, value-added service, and low cost enable wide-range applications of SRS networks as part of intelligent traffic detection infrastructure. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) refer to a group of battery-operated tiny sensor nodes having vast application areas in daily use. These are spatially dispersed and dedicated wireless sensor nodes for observing and recording the different parameter and physical conditions of the surroundings. There is a recent advancement in the field of network connectivity and computations in WSNs. The key functions of WSNs are a data extraction and to transmit the extracted data to the server placed at an isolated location. Various types of WSNs like underground underwater, terrestrial, and multimedia networks get applications domains such as in industrial automation, traffic monitoring and control medical device monitoring, and many other areas. Despite the thriving market, there are several challenges like energy efficiency, limited storage and computation, low bandwidth, high error rates, scalability, and survivability in harsh environment; hence, network lifespan expanding is a critical demanding issues. So many researchers have earlier focused towards finding the optimal path in between member node and sink node, so that energy depletion can be reduced to improve the network lifespan. There are different challenges in WSNs but one of the most challenging issues is how to minimize the energy consumption; numerous bio-inspired techniques have been proposed previously to obtain an optimal path between the member node and the sink node. In this manuscript, we are presenting a comprehensive survey on optimization technique-based routing and clustering. The study of this comprehensive survey offers in-depth summary of the past researches in the area of WSNs. [ABSTRACT FROM AUTHOR]

In complex urban intersection scenarios, due to heavy traffic and signal control, there are many slow-moving or temporarily stopped vehicles behind the stop lines. At these intersections, it is difficult to extract traffic parameters, such as delay and queue length, based on vehicle detection and tracking due to the dense and severe occlusion of vehicles. In this study, a novel background subtraction algorithm based on sparse representation is proposed to detect the traffic foreground at complex intersections to obtain traffic parameters. By establishing a novel background dictionary update model, the proposed method solves the problem that the background is easily contaminated by slow-moving or temporarily stopped vehicles and therefore cannot obtain the complete traffic foreground. Using the real-world urban traffic videos and the PV video sequences of i -LIDS, we first compare the proposed method with other detection methods based on sparse representation. Then, the proposed method is compared with other commonly used traffic foreground detection models in different urban intersection traffic scenarios. The experimental results show that the proposed method performs well in keeping the background model being unpolluted from slow-moving or temporarily stopped vehicles and has a good performance in both qualitative and quantitative evaluations. [ABSTRACT FROM AUTHOR]

Accurate identification of ships is the key technology of intelligent transportation in water. At the same time, it also provides a judgment basis for water traffic safety control. This paper proposed a detection method of ships in water based on improved You Only Look Once version 3 (YOLOv3), which is called Feature Attention, Feature Enhancement YOLOv3 (AE-YOLOv3). The feature attention module was constructed by introducing the attention mechanism, which was embedded in Darknet-53 for feature recalibration, which improved the feature extraction ability of the model in the complex navigable background. For the problem of insufficient semantic information of low-level features in the feature fusion process, a feature enhancement module was constructed and applied to the feature fusion part to enhance the receptive field size of the corresponding feature layer and the correlation degree of feature extraction network. Experiments were carried out on the public SeaShips dataset. Experiments show that the detection accuracy is as high as 98.72%, which is better than that of other mainstream ship identification models, fully verifying the superiority of this method in the detection of waterborne traffic ships. [ABSTRACT FROM AUTHOR]

To effectively manage the traffic flow in order to reduce traffic congestion, it is necessary to know the volumes and quantitative indicators of this flow. Various detection methods are known for detecting a vehicle in a lane, which, in turn, have their own advantages and disadvantages. To detect vehicles and analyse traffic intensity, the authors use a pulse coherent radar (PCR) sensor module. Testing of various modes of operation of the radar sensor was carried out to select the optimal mode for detecting vehicles. The paper describes a method for fixing vehicles of different sizes, filtering and separating the vehicle from the traffic flow. The developed vehicle detection device works in conjunction with signal traffic lights, through which traffic control takes place. The signal traffic lights, which have their own sensors and control units, communicate with each other via a radio channel; there is no need for cable laying. The system is designed to work on road maintenance sites. The paper describes the experimental data when testing on a separate section of the road. The experiment showed the advantage of traffic lights (cars passed the regulated traffic light faster) from the point of view of calculating the traffic flow over the normal traffic light operation. Reducing downtime in traffic jams, in turn, has a beneficial effect on the environmental situation, since at the moment internal combustion engines prevail in vehicles. [ABSTRACT FROM AUTHOR]

Increased traffic flow results in high road occupancy. Traffic road occupancy is often used as a parameter for the prediction of traffic conditions by traffic engineers. Although traffic monitoring systems are based on a large number of technologies, challenges are still present. Most of the methods work efficiently for free-flow traffic but not in heavy congestion. Image processing techniques are more effective than other methods, as they are based on loop sensors and detectors to monitor road traffic. A huge number of image frames are processed in image processing hence there is a need for a more efficient and low-cost image processing technique for accurate vehicle detection. In this paper, a novel approach is adopted to calculate road occupancy. The proposed framework has robust performance under road conjunction and diverse environmental conditions. A combination of image segmentation threshold technique and shadow removal technique is used. The study comprised of segmenting 1056 images extracted from recorded videos. The obtained results by image segmentation were compared with traffic road occupancy calculated manually using Autocad. A final percentage difference of 8.17 was observed. [ABSTRACT FROM AUTHOR]

Featured Application: This paper proposes an object detection and tracking system that can count vehicles, estimate the velocity of vehicles, and provide traffic flow estimations for traffic monitoring and control applications. This paper proposes a neural network that fuses the data received from a camera system on a gantry to detect moving objects and calculate the relative position and velocity of the vehicles traveling on a freeway. This information is used to estimate the traffic flow. To estimate the traffic flows at both microscopic and macroscopic levels, this paper used YOLO v4 and DeepSORT for vehicle detection and tracking. The number of vehicles passing on the freeway was then calculated by drawing virtual lines and hot zones. The velocity of each vehicle was also recorded. The information can be passed to the traffic control center in order to monitor and control the traffic flows on freeways and analyze freeway conditions. [ABSTRACT FROM AUTHOR]

The rapidly increasing traffic congestion is affecting the population of the world in a variety of ways including personal (physical and mental health), family, social, environmental, accidents and passage of emergency and law and order vehicles. Out of the many varieties of approaches tested for solutions, the current research used roadside sensors to collect data and use them for simulations using PTV Vissim simulator with MS Excel algorithmic optimisation. The location of the test was which St Marks Road in the Merton region of the UK. This is indeed an inlet into a major road (A217). A subset of data from the Department of Transport (2019) [1] was chosen which had an AADT of less than 2,000. In 2018, it was an AADT of 1,086 for this location. Simulation results showed the superiority of adaptive green signal timing over fixed timing signals. The simulation results were applied to optimisation to specify the green light timing requirements. Accordingly, congestion can be prevented using adaptive green signal timings. The results were discussed within the scope of many approaches used by different researchers. [ABSTRACT FROM AUTHOR]

Summary. We present vehicle detection classification using the Convolution Neural Network (CNN) of the deep learning approach. The automatic vehicle classification for traffic surveillance video systems is challenging for the Intelligent Transportation System (ITS) to build a smart city. In this article, three different vehicles: bike, car and truck classification are considered for around 3,000 bikes, 6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract different vehicle dataset's different features without a manual selection of features. The accuracy of CNN is measured in terms of the confidence values of the detected object. The highest confidence value is about 0.99 in the case of the bike category vehicle classification. The automatic vehicle classification supports building an electronic toll collection system and identifying emergency vehicles in the traffic. [ABSTRACT FROM AUTHOR]

The 5G network revolution will be enabled by deep integration of Software Defined Networking (SDN) and Network Function Virtualization (NFV) to support multi-tenancy, per-user and per-application quality of service and experience. However, full softwarization and current SDN platforms may not be able to sustain the complexity and the heterogeneity of different requirements, for example, strict latency, jitter, high precision traffic and advanced monitoring. For such services, SDN/NFV needs to be boosted not only considering orchestration and control plane, but also data plane programmability. In this article, the potential of the P4 language is illustrated with the aim to show its disruptive novel functionalities at the data plane level currently not available in a SDN/NFV network, opening the way to new orchestration frameworks and enabling a novel autonomic and flexible network at the edge. Use cases, assessments and softwarized performance results are proposed and discussed in the edge and IoT scenario, targeting advanced traffic engineering, cyber security, multi-tenancy, 5G offloading, and telemetry, to demonstrate the feasibility of such an approach. [ABSTRACT FROM AUTHOR]

Vehicle plate number recognition plays an important role in traffic control and surveillance systems. A key stage in any vehicle plate number recognition system is to first locate the vehicle plate number. In this paper, we present a modified GrabCut algorithm for localizing vehicle plate numbers. In contrast with the traditional interactive GrabCut technique, a modified GrabCut algorithm was designed to identify and extract vehicle plate numbers in a completely automatic manner. Our approach extends the use of the traditional GrabCut algorithm with addition of a feature extraction method which uses geometric information to give accurate foreground extraction. Finally, to evaluate the performance of the proposed technique, the localization accuracy is tested with a dataset of 500 vehicle images with vehicle plates from different countries. An accuracy of 99.8% was achieved for the localization of vehicle plates. Comparative analysis is also reported. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed research is beneficial in two main ways. Firstly, commuters are empowered to receive route guidance that incorporates a mix of transport modes, including cars, buses, trams, bicycles, and walking, in a bid to reduce travel-related carbon emissions and encourage sustainable travel behavior. Secondly, the traffic control and management authorities may use the multi-agent traffic recommender system to view and monitor the live status of the traffic, thus empowering them to take appropriate road traffic control actions to optimize the traffic streams. The use and coordination of multiple modes of travel efficiently, although beneficial, remains an overarching challenge for urban cities. This paper implements a distributed architecture of an eco-friendly transport guidance system by employing the agent-based paradigm. The paradigm uses software agents to model and represent the complex transport infrastructure of urban environments, including roads, buses, trolleybuses, metros, trams, bicycles, and walking. The system exploits live traffic data (e.g., traffic flow, density, and CO2 emissions) collected from multiple data sources (e.g., road sensors and SCOOT) to provide multimodal route recommendations for travelers through a dedicated application. Moreover, the proposed system empowers the transport management authorities to monitor the traffic flow and conditions of a city in real-time through a dedicated web visualization. We exhibit the advantages of using different types of agents to represent the versatile nature of transport networks and realize the concept of smart transportation. Commuters are supplied with multimodal routes that endeavor to reduce travel times and transport carbon footprint. A technical simulation was executed using various parameters to demonstrate the scalability of our multimodal traffic management architecture. Subsequently, two real user trials were carried out in Nottingham (United Kingdom) and Sofia (Bulgaria) to show the practicality and ease of use of our multimodal travel information system in providing eco-friendly route guidance. Our validation results demonstrate the effectiveness of personalized multimodal route guidance in inducing a positive travel behavior change and the ability of the agent-based route planning system to scale to satisfy the requirements of traffic infrastructure in diverse urban environments. [ABSTRACT FROM AUTHOR]

Private enterprises and governments around the world use speed cameras to control traffic flow and limit speed excess. Cameras may be exposed to difficult weather conditions and typically require frequent maintenance. When deciding the order in which maintenance should be performed, one has to consider both the traveling times between the cameras and the traffic flow that each camera is supposed to monitor. In this paper, we study the problem of routing a set of technicians to repair cameras by minimizing the total weighted latency, that is, the sum of the weighted waiting times of each camera, where the weight is a parameter proportional to the monitored traffic. The resulting problem, called the weighted k-traveling repairman problem (wkTRP), is a generalization of the well-known traveling repairman problem and can be used to model a variety of real-world applications. To solve the wkTRP, we propose an iterated local search heuristic and an exact branch-and-cut algorithm enriched with valid inequalities. The effectiveness of the two methods is proved by extensive computational experiments performed both on instances derived from a real-world case study and on benchmark instances from the literature on the wkTRP and on related problems. [ABSTRACT FROM AUTHOR]

A systematic review was undertaken to examine the solutions available for traffic congestion and associated problems in smart cities. Google Scholar and Google were used as search engines, leading to the final selection of 35 eligible papers for inclusion in this review, after a serious of screening based on definite criteria. Intelligent transport systems were found to be the most suitable solution to traffic congestion and associated problems in smart cities. Certain models and frameworks of smart cities include smart mobility and transport management systems. These can be approximated to intelligent transport systems. True intelligent transport systems are infrastructure-based or intelligent vehicle based or more preferably, a combination of both. The Internet of Things and cloud computing should be built into the system as they enable the operation of smart transport networks. Some methods of designing traffic control systems combining both Eulerian and Lagrangian approaches have been discussed for the possibility of using any of them to design a new automatic traffic monitoring and control system for smart cities. The practical implication of this research is that it can improve quality of life of people by minimizing traffic congestion. Limitations of this paper include this being a systematic review, availability of very few papers and not considering adaptive intelligent traffic control systems. Explanations for these limitations have been provided. [ABSTRACT FROM AUTHOR]

With the tremendous technological progress and the widespread use of a variety of technologies, we note how smart cities are providing services efficiently by using technologies. The aim of this project is to build a Smart Traffic Control System (STCS) to facilitate and optimize traffic flow, minimize traffic congestion, and reduce the waiting time by detecting the density on each street. This work has been carried on four phases. Firstly, collecting data by a questionnaire and we received 331 responses. Secondly, using Proteus simulation. Thirdly, building a low fidelity prototype, and fourthly: building the STCS model by using hardware (Arduino tools) and software (Arduino Software IDE). Finally, we learned how to build a system and we recommend using such a system in busy roads to reduced congestion and making traffic flow more efficient. [ABSTRACT FROM AUTHOR]

The premise of implementing an effective traffic control strategy is the accurate traffic state recognition. In the existing study, traffic state recognition methods were processed by using statistical characteristics and long-term scale detection of field traffic data. Hence, the dynamic characteristics and subtle changes in traffic flow were easy to overlook. At present, more and more advanced traffic detection technology provides reliable and accurate data for measuring and distinguishing the state of urban road traffic, such as the cooperative vehicle-infrastructure system, wide-area radar technology, and 5G technology. This study proposes a novel method called HTSI (High Precision Traffic State Identification Method), which is based on the advanced detection technology in traffic state recognition at the intersection: The raw data used for intersection traffic state recognition is high-precision detection data of tracking characteristics, which make the data look like a picture of the intersection at God's perspective. To this end, we construct an image model for intersections and implement image feature extraction in a way that is different from traditional image processing. Then, the traffic state recognition problem at the intersection is translated into an image searching problem with tags. The image searching is realized by the hashing algorithm. Finally, the comprehensive experiments prove that the proposed method is more accurate and finer than other methods. [ABSTRACT FROM AUTHOR]

With the improvement of our country's economic level and quality of life, the numbers and scales of highway networks and motor vehicles are constantly expanding, which makes the current road traffic burden more and more serious. As an important means of traffic automation management, license plate recognition (LPR) technology plays an important role in traffic surveillance and control. However, the recognition rate and accuracy of the traditional license plate recognition methods still need to be improved. In the case of poor surrounding environment, it is prone to localization failure, vehicle license plate recognition errors or unrecognizable phenomena. Wavelet transform, as another landmark signal processing method after Fourier transform, has been widely used in the field of image processing. In China, the number of horizontal lines is usually larger than that of vertical lines. If the two vertical boundaries of the license plate can be detected successfully, the four angles of the license plate can be determined efficiently to complete the license plate positioning. In view of the advantages of wavelet transform technology and the characteristics of vehicle license plate, in this paper, a vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching is proposed. The edge of the license plate is detected by wavelet transform technology, and then the license plate is located by vertical edge matching technology. After the location is realized, the characters are segmented by vertical projection method and the characters are recognized by improved BP neural network algorithm. The experimental results show that the proposed vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching performs well in algorithm performance, which provides a good reference for the development of vehicle license plate recognition system. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed algorithm and architecture has the capability of estimating the queue length of waiting vehicles at a signalized intersection, where traffic cameras, edge server, and some connected vehicles are available. This method can resolve the queue length estimation problem in a mixed traffic scenario, especially when there are variable types of vehicles. It can provide key information for traffic lights control and improve the traffic efficiency. Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm. [ABSTRACT FROM AUTHOR]

The recent advent of the embedded devices is equipped with multicore processor as it significantly improves the system performance. In order to utilize all the core in multicore processor in an efficient manner, application programs need to be parallelized. An efficient thread level parallelism (ETLP) scheme is proposed in this paper and uses computationally intensive edge detection algorithm for evaluation. Edge detection is the important process in various real time applications namely vehicle detection in traffic control, medical image processing etc. The main objective of ETLP scheme is to reduce the execution time and increase the CPU core utilization. The performance of ETLP scheme is evaluated with basic edge detection scheme (BEDS) for different image size. The experimental results reveal that the proposed ETLP scheme achieves efficiency of 49% and 72% for the image size 300 × 256 and 1024 × 1024 respectively. Furthermore an ETLP scheme reducing 66% execution time for image size 1024 × 1024 when compared with BEDS. [ABSTRACT FROM AUTHOR]

Acquiring and processing video streams from static cameras has been proposed as one of the most efficient tools for visualizing and gathering traffic information. With the latest advances in technology and visual media, combined with the increased needs in dealing with congestion more effectively and directly, the use of Unmanned Aerial Aircraft Systems (UAS) has emerged in the field of traffic engineering. In this paper, we review studies and applications that incorporate UAS in transportation research and practice with the aim to set the grounds from the proper understanding and implementation of UAS related surveillance systems in transportation and traffic engineering. The studies reviewed are categorized in different transportation engineering areas. Additional significant applications from other research fields are also referenced to identify other promising applications. Finally, issues and emerging challenges in both a conceptual and methodological level are revealed and discussed.

In smart cities, it is expected that transport, communication as well as the movement of people and goods will take place in the shortest possible time while maintaining a high level of safety. In recent years, due to the significant increase in the number of passengers and vehicles on the road and the capacity limitations of transport networks, it has become necessary to use new technologies for intelligent control and traffic management. Intelligent transport systems use advanced technologies in the field of data gathering, information processing, and traffic control to meet current transport needs. To be able to effectively control and manage road traffic, it is necessary to have reliable mathematical models that allow for a faithful representation of the real traffic conditions. Models of this type are usually the basis of complex algorithms used in practice in road traffic control. The application of appropriate models reflecting the behavior of road users contributes to the reduction of congestion, the vehicles travel time on the transport network, fuel consumption and the emissions, which in turn support broadly understood energy savings. The article proposes a model that allows for the estimation of the maximum queue size at the signal-controlled intersection approach (so-called: maximum back-of-queue). This model takes into account the most important traffic characteristics of the vehicles forming this queue. The verification allowed for the conclusion that the proposed model is characterized by high compliance with the actual traffic and road conditions at the intersections with signal controllers located in built-up areas in Poland. The obtained compliance confirms the possibility of using the model for practical applications in calculating the maximum back-of-queue at signal-controlled intersections located in built-up areas in Poland.

Floating car data are beneficial in estimating traffic conditions in wide areas and are playing an increasing role in traffic surveillance. However, widespread application is limited by low-sample frequency which makes it hard to get a complete picture of a vehicle's motion. An accurate and reliable reconstruction of a vehicle's trajectory could effectively result in a higher sampling frequency enabling a more accurate estimation of road traffic parameters. Existing methods require additional information such as nearby vehicles, signal timing strategies, and queue patterns which are not always available. To address this problem, this paper presents a method used with low-sample frequency data to reconstruct vehicle trajectories through intersections, without the need for extra information. Furthermore, the additional parameters for the speed-time curve distributions for deceleration rate and acceleration rate are generated. A piecewise deceleration and acceleration model is developed to calculate the acceleration rate for different travel modes in the trajectory. The distribution parameters of the acceleration data for each travel mode are then estimated using a new Expectation Maximization (EM) algorithm. The acceleration statistics are then used to reconstruct the corresponding parts of the trajectory. Compared to the reference trajectories (truth), the test results show that the method developed in this paper achieves improvement in accuracy ranging from 16 to 67% over the commonly used linear interpolation method. In addition, the proposed method is not very sensitive to the sampling interval of the floating car data, unlike the linear interpolation method where the error grows rapidly with increasing sampling interval. [ABSTRACT FROM AUTHOR]

Abstract Vehicular traffic safety relies, to a great extent, on traffic control and traffic offence detection. While this is currently in the domain of governmental and road maintenance services, many alternatives, such as crowdsensing-based approaches have emerged. We present a traffic event reporting scheme, focusing on event detection and data source reputation mechanisms. Proposed scheme is evaluated on a set of representative scenarios. Proposal is critically discussed and is followed by guidelines for future work. [ABSTRACT FROM AUTHOR]

The introduction of autonomous vehicles (autonomous vehicles) will reshape the many social interactions that are part of traffic today. In order for autonomous vehicles to become successfully integrated, the social interactions surrounding them need to be purposefully designed. To ensure success and save development efforts, design methods that explore social aspects in early design phases are needed to provide conceptual directions before committing to concrete solutions. This paper contributes an exploration of methods for addressing the social aspects of autonomous vehicles in three key areas: the vehicle as a social entity in traffic, co-experience within the vehicle and the user--vehicle relationship. The methods explored include Wizard of Oz, small-scale scenarios, design metaphors, enactment and peer-to-peer interviews. These were applied in a workshop setting with 18 participants from academia and industry. The methods provided interesting design seeds, however with differing effectiveness. The most promising methods enabled flexible idea exploration, but in a contextualized and concrete manner through tangible objects and enactment to stage future use situations. Further, combinations of methods that enable a shift between social perspectives were preferred. Wizard of Oz and small-scale scenarios were found fruitful as collaboration basis for multidisciplinary teams, by establishing a united understanding of the problem at hand. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a vexing and complex issue in many urban areas. Of particular interest are the intersections where traffic bottlenecks are known to occur despite being traditionally signalized. Reinforcement learning (RL), which is an artificial intelligence approach, has been adopted in traffic signal control for monitoring and ameliorating traffic congestion. RL enables autonomous decision makers (e.g., traffic signal controllers) to observe, learn, and select the optimal action (e.g., determining the appropriate traffic phase and its timing) to manage traffic such that system performance is improved. This article reviews various RL models and algorithms applied to traffic signal control in the aspects of the representations of the RL model (i.e., state, action, and reward), performance measures, and complexity to establish a foundation for further investigation in this research field. Open issues are presented toward the end of this article to discover new research areas with the objective to spark new interest in this research field. [ABSTRACT FROM AUTHOR]

This paper explores the problem of processing the immense volume of measurement data arising during network traffic monitoring. Due to the everincreasing demands of current networks, observing accurate information about every single flow is virtually infeasible. In many cases the existing methods for the reduction of flow records are still not sufficient enough. Since the accurate knowledge of flows termed as "heavy-hitters" suffices to fulfill most of the monitoring purposes, we decided to aggregate the flow records pertaining to non-heavy-hitters. However, due to the ever-changing nature of traffic, their identification is a challenge. To overcome this challenge, our proposed approach - the adaptive aggregation of flow records - automatically adjusts its operation to the actual traffic load and to the monitoring requirements. Preliminary experiments in existing network topologies showed that adaptive aggregation efficiently reduces the number of flow records, while a significant proportion of traffic details is preserved. [ABSTRACT FROM AUTHOR]

Traffic surveillance systems are interesting to many researchers to improve the traffic control and reduce the risk caused by accidents. In this area, many published works are only concerned about vehicle detection in normal conditions. The camera may vibrate due to wind or bridge movement. Detection and tracking of vehicles is a very difficult task when we have bad weather conditions in winter (snowy, rainy, windy, etc.), dusty weather in arid and semi-arid regions, at night, etc. Also, it is very important to consider speed of vehicles in the complicated weather condition. In this paper, we improved our method to track and count vehicles in dusty weather with vibrating camera. For this purpose, we used a background subtraction based strategy mixed with an extra processing to segment vehicles. In this paper, the extra processing included the analysis of the headlight size, location, and area. In our work, tracking was done between consecutive frames via a generalized particle filter to detect the vehicle and pair the headlights using the connected component analysis. So, vehicle counting was performed based on the pairing result, with Centroid of each blob we calculated distance between two frames by simple formula and hence dividing it by the time between two frames obtained from the video. Our proposed method was tested on several video surveillance records in different conditions such as dusty or foggy weather, vibrating camera, and in roads with medium-level traffic volumes. The results showed that the new proposed method performed better than our previously published method and other methods, including the Kalman filter or Gaussian model, in different traffic conditions. [ABSTRACT FROM AUTHOR]

In this paper, an acceleration sensing node for pavement vibration was developed to monitor traffic information, including vehicle speed, vehicle types, and traffic flow, where a hardware design with low energy consumption and node encapsulation could be accomplished. The service performance of the sensing node was evaluated, by methods including waterproof test, compression test, sensing performance analysis, and comparison test. The results demonstrate that the sensing node is low in energy consumption, high in strength, IPX8 waterproof, and high in sensitivity and resolution. These characteristics can be applied to practical road environments. Two sensing nodes were spaced apart in the direction of travelling. In the experiment, three types of vehicles passed by the monitoring points at several different speeds and values of d (the distance between the sensor and the nearest tire center line). Based on cross-correlation with kernel pre-smoothing, a calculation method was applied to process the raw data. New algorithms for traffic flow, speed, and axle length were proposed. Finally, the effects of vehicle speed, vehicle weight, and d value on acceleration amplitude were statistically evaluated. It was found that the acceleration sensing node can be used for traffic flow, vehicle speed, and other types of monitoring. [ABSTRACT FROM AUTHOR]

One of the most critical issues in modern cities is transportation management. Issues that are encountered in this regard, such as traffic congestion, high accidents rates and air pollution etc., have pushed the use of Intelligent Transportation System (ITS) technologies in order to facilitate the traffic management. Seen in this perspective, this paper brings forward a road traffic management system based on wireless sensor networks; it introduces the functional and deployment architecture of the system and focuses on the analysis component that uses a new extension of batches Petri nets for modeling road traffic flow. A real world implementation of visualization and data analysis components were carried out. [ABSTRACT FROM AUTHOR]