Traffic Information Processing Research Articles

Audiovisual messages may improve the processing of traffic information and driver attention during partially automated driving: An EEG study

Partially autonomous vehicles can help minimize human errors. However, being free from some driving subtasks can result in a low vigilance state, which can affect the driver’s attention towards the road. The present study first tested whether drivers of partially autonomous vehicles would benefit from the addition of auditory versions of the messages presented in variable message signs (VMS), particularly, when they find themselves in a monotonous driving situation. A second aim was to test whether the addition of auditory messages would also produce an indirect effect on the driver’s vigilance, improving performance on other driving subtasks not related to the message processing. Forty-three volunteers participated in a driving simulator study. They completed two tasks: (a) a VMS task, where they had to regain manual control of the car if the VMS message was critical, and (b) a car-following task, where they had to pay attention to the preceding car to respond to occasional brake events. Behavioral and EEG data were registered. Overall, results indicated that the addition of audio messages helped drivers process VMS information more effectively and maintain a higher level of vigilance throughout the driving time. These findings would provide useful information for the development of partially automated vehicles, as their design must guarantee that the driver remains attentive enough to assume control when necessary.

Task Scheduling of Real-Time Traffic Information Processing Based on Digital Twins

The Intelligent Transportation System under Digital Twins can provide accurate data sources for traffic control. The present work focuses on the real-time information processing and task scheduling problems of the Internet of Vehicles (IoV) system based on Virtual Reality. They are the Quality/Distance Algorithm (QDA), Task Density Algorithm, Distance Balance Algorithm (DBA), and Bionic-DBA (B-DBA). The simulation experiment analysis suggests that the DBA algorithm takes the balance of travel distance into account and effectively improves task quality. The Utility Function in B-DBA and the Biological Heuristic Search Algorithm in Pareto Ant Colony Optimization play a critically important role in enhancing the overall task quality. In addition, a Transmission based on Privacy Protection (TPP) algorithm is designed to protect the attribute-based privacy information in the traffic information transmission system. This algorithm ensures that the real-time traffic information processing system resists various attacks from malicious nodes. It has been verified that when the number of selfish nodes accounts for 30%, the transmission efficiency of the TPP algorithm reaches 0.77. The research content has a practical reference value for providing users with continuous and high-quality IoV network services.

A Highway Traffic Image Enhancement Algorithm Based on Improved GAN in Complex Weather Conditions

To address the problems of low resolution and blurred details in highway images caused by factors such as rain and fog, illumination interference, and nighttime lighting, this paper proposes a highway traffic image enhancement algorithm based on improved GAN in complex weather conditions. The attention mechanism and the multiscale feature fusion were combined to improve the generator network, which could effectively reduce noise while improving the attention of high-frequency region information. The improved PatchGAN in the discriminator used a local discrimination strategy to distinguish the generated image from the real image, and then the Nash equilibrium was achieved through the continuous interaction between the generator and the discriminator, to ensure the integrity and authenticity of the restored image. Compared with other image enhancement algorithms, using PSNR and SSIM as measurement indicators, the experimental results showed that the proposed algorithm’s results were, respectively, 21.97% and 12.89% higher in nighttime enhancement, 26.16% and 12.75% higher in rain removal, and 26.56% and 12.1% higher in fog removal. The proposed algorithm can not only retain the image details and feature information, but also produce effective denoising, which increases the reliability of image-based traffic information processing and analysis.

A Novel Framework for Analysis of Road Traffic Information for Decision Support by Using Data Mining Techniques

As we know we want traffic data analysis, data mining measures are recommended to help manage and traffic information decision making. Such as this limit, the road type is added intelligence analysis and decision support are another gave. This model has a data collection system and the data warehouse at its heart, and works well mathematical intelligence techniques and the invention of decision support system. As the main part of this report, the main parts of the model, the main process and an implementation plan is developed. An intelligent urban traffic decision analysis system is a system designed to solve open road problems, improve road safety and quality, and improve road quality work. The application of big data mining technology can improve various capabilities types of analysis and processing of traffic information, spread rapidly and provide a decision reference for the application of intelligent transport systems, which will be carried out to ensure smooth and safe roads. This topic will introduce the concept of large data mining technology and intelligent transport systems, and review the architecture of smart city road systems based on big data technology and design implementation of the mining process.

Decision-Making Method of Autonomous Vehicles in Urban Environments Considering Traffic Laws

In order to improve the efficiency and safety of autonomous vehicles’ decision-making process in complex urban scenarios, a decision-making method for hierarchical processing of static traffic law information and dynamic traffic participant information is proposed in this paper. In the decision-making method, the candidate behavior set is constructed by extracting the element of traffic laws and fully considering the traffic laws constraints to ensure the legality and effectiveness of the decision-making algorithm. Besides, four evaluation indicators and two-level entry threshold are designed to select the optimal driving behavior with the consideration of driving efficiency, ride safety and macro path requirement. The advantage of proposed method is that it avoids the problem of poor adaptability to traffic laws and regulations as the existing methods usually make decisions under the condition of mixed traffic laws and traffic participant information. A complete driving task simulation and analysis, including six typical urban traffic scenarios, is given under Matlab environment. The results show that the proposed decision-making method is able to make reasonable and feasible decisions and highly consistent with the actual driver’s decision-making behavior in complex urban scenarios, which verifies the effectiveness of the proposed method.

Real-Time Traffic Prediction Using Neural Networks

Somewhat recently, the issue of traffic has become more extreme because of industrialization particularly in significant urban communities. A higher standard of living in cities is pushing people to utilise their own vehicles instead of public transportation. Thus, there is tremendous increase in traffic, which leads to a variety of other associated issues. In order to get from one place to another, one must spend more time travelling than they would have otherwise, which also raises the fuel consumption. The traffic data that is gathered by various detectors is highly dynamic and non-stationary. For example, the number of vehicles turning at an intersection cannot be stated with greater accuracy. Consequently, it is challenging to develop a mathematical model to determine green time. However, the introduction of Intelligent Transportation Systems (ITS) in modern times enables the detection of traffic events, communication, information processing, and user action. One of the most crucial criteria for this system to work successfully is the ability to precisely predict the pattern of the traffic stream. In order to predict future traffic flow, a system that uses artificial neural networks and real-time traffic data was proposed in this study. Neural networks have the ability to predict the future by learning from the past. Specifically, this study predict traffic volume in two levels such as short-term for every 15 minutes interval and mid-term for every one hour of a given day. These predictions help in dynamic traffic management applications like signal control, congestion management, and travel time predictions etc.

Traffic controlling and monitoring using IoT

The population is increasing the number of vehicles and the number of highways day after day; the time spent travelling, waste fuel, air pollution and related transport problems are also increasing. The greatest challenge for traffic administration authorities is therefore to monitor and monitor traffic. The related work has shown the Internet of Things and the integration of artificial intelligence to facilitate techniques for better urban and decision­making. This document creates a system model for IoT-based traffic information collection, processing and storing in real time. The aim is to ensure smooth mobility by providing roadside communications updates and rare incidents in real time. In particular, pre-alerting messages prevent and delay road congestion and peak hours in emergencies. The system also sends traffic updates from the database of administrative sensors. The system proposed evaluates the feature of the model and shows expected accurate results for vehicle detection and the minimum error in the estimates of occupancy.

Comparison of GPSR and FPSR Algorithms Based on High-Speed Vehicle Information

With the progress of network information technologies, the application of wireless network in high-speed traffic vehicle system has been greatly expanded. However, due to the high-speed movement of nodes, the reliability of high-speed traffic network construction is still relatively low. Therefore, the network data processing of high-speed traffic information has become a hot issue of social concern. In this study, based on the network data system of vehicle information, the greedy perimeter stateless routing (GPSR) algorithm and data forwarding probability strategy based on relevance in vehicular network (FPSR) algorithm were introduced to collect vehicle information from the high-speed traffic examples. The data analysis results of the two algorithms were compared from aspects of the routing stability, delay rate, and success rate. The experimental results showed that the FPSR algorithm was superior in the routing stability and the success rate when the vehicle drove at high speed, while the GPSR algorithm was better in the delay rate and the success rate when the vehicle drove at a low speed. This study can help further understand the performance of the information network, provide technical support for vehicles on high-speed traffic, and provide a theoretical reference for modern intelligent transportation.

Messages beyond the phone: Processing variable message signs while attending hands-free phone calls

We examined the effects of different types of cognitive distraction coming from a hands-free phone conversation on the processing of information provided by variable message signs (VMS), on driving performance indicators, and on a physiological index of mental effort (heart rate). Participants drove a route in a driving simulator and had to respond to VMS messages under three conditions: no-distraction, visuospatial distraction (attending phone calls with questions inducing visuospatial processing), and conceptual distraction (attending phone calls with questions requiring semantic memory). Results showed more errors responding to VMS messages in the visuospatial distraction condition. In addition, both types of questions increased the intraindividual variability of response distances and the heart rate, as compared to the no-distraction condition. These results provide new evidence that talking on a hands-free phone entails costs in the processing of traffic information (in particular, text messages displayed on VMS) and it increases the driver’s cognitive effort. Interestingly, the cognitive distraction had no effect on the driver’s control of the vehicle speed or lateral position. Therefore, the effects of potential risk factors can critically vary among the different driving subtasks due to modulatory factors, such as the level of attentional task demands (relatively high in the processing of messages on VMS, but relatively low in controlling the speed and lateral position of the vehicle in quiet traffic conditions). In consequence, the current paper provides new evidence to discuss hands-free phone policies and highlights the importance of designing technological countermeasures to prevent drivers missing critical information displayed on VMS.

A V2I communication-based pipeline model for adaptive urban traffic light scheduling

Adaptive traffic light scheduling based on realtime traffic information processing has proven effective for urban traffic congestion management. However, fine-grained information regarding individual vehicles is difficult to acquire through traditional data collection techniques and its accuracy cannot be guaranteed because of congestion and harsh environments. In this study, we first build a pipeline model based on vehicle-to-infrastructure communication, which is a salient technique in vehicular adhoc networks. This model enables the acquisition of fine-grained and accurate traffic information in real time via message exchange between vehicles and roadside units. We then propose an intelligent traffic light scheduling method (ITLM) based on a “demand assignment” principle by considering the types and turning intentions of vehicles. In the context of this principle, a signal phase with more vehicles will be assigned a longer green time. Furthermore, a green-way traffic light scheduling method (GTLM) is investigated for special vehicles (e.g., ambulances and fire engines) in emergency scenarios. Signal states will be adjusted or maintained by the traffic light control system to keep special vehicles moving along smoothly. Comparative experiments demonstrate that the ITLM reduces average wait time by 34%–78% and average stop frequency by 12%–34% in the context of traffic management. The GTLM reduces travel time by 22%–44% and 30%–55% under two types of traffic conditions and achieves optimal performance in congested scenarios.

Node Detection and Tracking in Smart Cities Based on Internet of Things and Machine Learning

It is essential to know that using technologies in a good manner will facilitate human live. Internet of Things (IoT) used widely due to developments in information and verbal exchange technologies. Some of the most famous fields of IoT applications are Identification, transmission, and healthcare which uses IoT technologies to collect information and recognizing the problem and propose the solution for it. In this paper, we try to find RFID nodes and their location. An Android Application used to provide help for those needed. Finding and detection the actual zone of our users are done by using KNN algorithm. We use 3NN because that model gets a better result in our dataset, for transmission depending on the users’ problem.
 We use a new equation to find weights that integrated with Dijkstra's algorithm, the equation is to calculate the weight between any two nodes using traffic information and image processing for finding a load of road by counting number of vehicles inside the image that collected from our readers. Dijkstra's algorithm is used to find the best path between source and destination using weights between nodes. The idea is used in healthcare, but can be used in many other fields like, security, Information and Communication Technology and Military.

Traffic Operation Data Analysis and Information Processing Based on Data Mining

With the acceleration of urbanization, urban traffic problems are becoming more and more prominent. In the face of massive traffic data, it is difficult to predict traffic condition with effective data analysis methods. In order to deal with traffic data better, this study applied data mining in traffic data analysis and processing, constructed a Hadoop based data analysis system to collect and preprocess data, and analyzed traffic data using parallel distributed calculation based on MapReduce. The improved fuzzy c-means (FCM) algorithm and the random forest algorithm were used. The simulation results showed that the error rate of the improved FCM algorithm is 10% and the accuracy rate of the random forest algorithm is 92.3%, indicating the system had high reliability. Then an experiment was carried out on the main traffic roads in Huadu district of Guangzhou, China. It was found that the method was efficient and accurate and had a good application prospect.

Environmental monitoring based on data processing of Internet of Things

The concept of online monitoring of the urban environment is proposed. It is based on the online processing of hydrometeorological and traffic information received through the Internet of Things. The traditional approach of the Internet of things includes transfer and storage of huge arrays of measurements in digital form. This concept of online monitoring is primarily an analysis, evaluation of the results of processing information received from wireless networks. The concept was implemented at Nizhny Novgorod State University of Architecture and Civil Engineering in several services including Eco-routes, Quite-routes, in which the air pollution of the urban environment by vehicle emissions and the noise level from traffic flows are estimated in real time mode. The calculation is based on meteorological data and traffic flow velocity. The calculation and assessment of environment pollution is carried out at the request of the user via Internet. The concept includes micro weather and algal blooming monitoring of reservoirs and ponds. Developed services is the first to provide free short time health risk assessment for both decision makers and common internet users.

WSN Optimization Algorithm for Traffic Environmental Monitoring

In accordance with the features of variety and disparity of traffic information, using wireless sensor network to monitor traffic environment, this paper proposes an optimization algorithm of applying wireless sensor to monitor traffic environment. In this paper, to optimize the coverage, we analyze the irregularity of network sensing areas based on environmental factors and propose a wireless sensor network optimization algorithm. According to the irregularity of network node sensing areas, we build an irregular network coverage model, divide the node monitoring area to improve the network coverage and make corrections to the coverage according to the network coverage connectivity and other features. The simulation test proves that the method proposed in this paper can avoid too many redundant nodes - it only requires a reasonable number of network nodes. This will reduce network energy costs and increase network connectivity without affecting the coverage, thus improving the network performance. the algorithm can satisfy the requirements of real-time acquisition, processing and remote supervision of traffic information.

Short-term traffic flow prediction in smart multimedia system for Internet of Vehicles based on deep belief network

In the multimedia system for Internet of Vehicles (IoVs), accurate traffic flow information processing and feedback can give drivers guidance. In traditional information processing for IoVs, few researches deal with traffic flow information processing by deep learning. Specially, most of the existing prediction technologies adopt shallow neural network, and their models for chaotic time series are prone to be restricted by multiple parameters. Over the last few years, the dawning of the big data era creates opportunities for the intelligent traffic control and management. In this paper, we take Restricted Boltzmann Machine (RBM) as the method for traffic flow prediction, which is a typical algorithm based on deep learning architecture. Considering traffic big data aggregation in IoVs, multimedia technologies provide enough real sample data for model training. RBM constructs the long-term model of polymorphic for chaotic time series, using phase space reconstruction to recognize the data. To the best of our knowledge, it is the first time apply RBM model to short-term traffic flow prediction, which can improve the performance of multimedia system in IoVs. Moreover, experimental results show that the proposed method has superior performance than traditional shallow neural network prediction methods.

Intelligent Traffic Decision Analysis System Based on Big Data Mining

The urban intelligent traffic decision analysis system is a system designed to solve urban road traffic problems, improve traffic safety and comfort, and improve the level of traffic services. The application of big data mining technology can improve the ability of various types of road traffic information analysis and processing, expand the scope of application, and provide decision-making reference for the application of intelligent transportation systems, so as to ensure the smoothness and safety of roads. This article will introduce the concept of big data mining technology and intelligent transportation system, and analyze the architecture of urban traffic intelligent system based on big data technology and the design and implementation of mining system.

Intelligent acquisition algorithm of dynamic traffic data based on Internet of Things

In order to improve the intelligent traffic management and dispatching capabilities, it is required to optimize the design of intelligent acquisition of dynamic traffic data and optimize the processing of traffic data information by dynamically mining traffic data. Therefore, an intelligent acquisition algorithm of dynamic traffic data based on Internet of Things is proposed in this paper. In this algorithm, the distributed wireless sensor networking is used to construct an Internet of Things model of traffic data acquisition and optimize deployment and design of Internet of Things nodes for data acquisition; the adaptive weighted algorithm is used to process the fusion of dynamic traffic data to extract spectral characteristic quantity of dynamic traffic data, and the spectrum analysis method is used to perform anomaly detection to dynamic traffic data; then the detection results are conducted with fuzzy clustering, so as to realize intelligent acquisition of dynamic traffic data in Internet of Things environment. The simulation results show that in acquisition of dynamic traffic data, this method has high acquisition accuracy under different SNR. In this paper, when the SNR is 4 dB, the acquisition accuracy is 100, while the traditional method is up to 16 dB, the acquisition accuracy is 100. The proposed method has the advantages of high accuracy, good data recall, strong anti-interference ability during acquisition and good self-adaptability.

Accident analysis for prevention of traffic accidents involving elderly pedestrians: characteristics of traffic behaviors and information processing in older adults

Accident analysis for prevention of traffic accidents involving elderly pedestrians: characteristics of traffic behaviors and information processing in older adults

PTFA: a secure and privacy-preserving traffic flow analysis scheme for intelligent transportation system

With the pervasiveness of vehicle information systems (VIS) and the advance of vehicular ad hoc networks (VANET), intelligent transportation systems (ITS), which can improve road traffic and reduce traffic accidents, have attracted considerable interest recently. However, the development of ITS still faces many challenges including privacy of individual data and locations. In this paper, we propose a secure and privacy-preserving traffic flow analysis scheme, called PTFA, for ITS. With PTFA, user privacy can be guaranteed when the manager acquires the fine-grained traffic information. We first map the information of roads to a super increasing sequence; then, construct an improved multi-dimensional aggregation scheme based on the homomorphic Paillier cryptosystem. For data communications from user to traffic regional-centre (TRC), data aggregation is performed directly on ciphertext at road side units (RSUs) without decryption, and the aggregation result of the original data can be obtained at the TRC. Detailed security analysis shows that the proposed PTFA scheme can efficiently achieve user privacy-preserving in ITS. In addition, performance evaluations via extensive simulations demonstrate the PTFA's effectiveness in terms of providing fine-grained traffic information processing and minimising communication cost.

Road Traffic Big Data and Its Key Technologies

A big data environment is formed in road traffic system. In order to clarify the research contents, key technologies and technical solutions of the road traffic big data, the connotation, features and research status of big data are summarized. Secondly, from the view of people, vehicles, roads and the environment, the data sources of road traffic big data are analyzed. Then, from the view of traffic information acquisition, transmission, processing and application, the key issues of road traffic big data are analyzed. Finally, from the perspective of data processing and management, the key technologies of road traffic big data processing and management are analyzed. Through the above analysis, the future applications of road traffic big data key issues, main technologies and technical basis for the road traffic big data are presented. The study of road traffic big data attempts to provide a technical guidance and related applications.