AN EFFICIENT INTELLIGENT TRAFFIC LIGHT CONTROL AND DEVIATION SYSTEM FOR TRAFFIC CONGESTION AVOIDANCE USING MULTI-AGENT SYSTEM

Guerrero-Ibáñez J., Zeadally S., Contreras-Castillo J.

Houbraken M., Logghe S., Schreuder M., Audenaert P., Colle D., Pickavet M.

The aim of this paper is to demonstrate the feasibility of a live Automated Incident Detection (AID) system using only Floating Car Data (FCD) in one of the first large-scale FCD AID field trials. AID systems detect traffic events and alert upcoming drivers to improve traffic safety without human monitoring. These automated systems traditionally rely on traffic monitoring sensors embedded in the road. FCD allows for finer spatial granularity of traffic monitoring. However, low penetration rates of FCD probe vehicles and the data latency have historically hindered FCD AID deployment. We use a live country-wide FCD system monitoring an estimated 5.93% of all vehicles. An FCD AID system is presented and compared to the installed AID system (using loop sensor data) on 2 different highways in Netherlands. Our results show the FCD AID can adequately monitor changing traffic conditions and follow the AID benchmark. The presented FCD AID is integrated with the road operator systems as part of an innovation project, making this, to the best of our knowledge, the first full chain technical feasibility trial of an FCD-only AID system. Additionally, FCD allows for AID on roads without installed sensors, allowing road safety improvements at low cost.

Zheng B., Sayin M.O., Lin C., Shiraishi S., Zhu Q.

With the fast development of autonomous driving and vehicular communication technologies, intelligent transportation systems that are based on VANET (Vehicular Ad-Hoc Network) have shown great promise. For instance, through V2V (Vehicle-to-Vehicle) and V2I (Vehicle-to-Infrastructure) communication, intelligent intersections allow more fine-grained control of vehicle crossings and significantly enhance traffic efficiency. However, the performance and safety of these VANET-based systems could be seriously impaired by communication delays and packet losses, which may be caused by network congestion or by malicious attacks that target communication timing behavior. In this paper, we quantitatively model and analyze some of the timing and security issues in transportation networks with VANET-based intelligent intersections. In particular, we demonstrate how communication delays may affect the performance and safety of a single intersection and of multiple interconnected intersections, and present our delay-tolerant intersection management protocols. We also discuss the issues of such protocols when the vehicles are non-cooperative and how they may be addressed with game theory.

Hamidi H., Kamankesh A.

Intelligent traffic management can be considered one of the most promising solutions to contemporary traffic problems. The traffic in transportation associated with emergency conditions including psychiatric improvement in transport network, allocation of variable traffic flows, reducing the number of the crowded traffic roads and paths as well as its negative effects (such as delays, waiting time, stress of driver, noise and air pollution, and blocking the assistive devices). This research has been used by new multi-agent systems to manage traffic. On the one hand, the proposed algorithm includes traffic flow improvement in emergency conditions until it is considered as real-time traffic information and in other hand, by preventing the increase the volume of a paths have effects in the reduce of crowded traffic situations at a specific time (for example, providing the proposed paths in the shortest time). In this article, the integrated environment is including JACK software for having a virtual agent behavior simulation. In general, we can use the different simulation form in a distribution network to display the crowded and traffic. In this article, the JACK software is used for having the explicit capabilities and supporting the common of this software in modelling the multi-agent systems, such as agents, design, event and capabilities. In addition, designing and analyzing of this interaction is simplest than the existed designs in JACK software. As a result of the proposed model, it seems reasonable that the proposed approach is different than previous works in each case. On the one hand, there are modeling system in the different tasks as intelligent agents dependent to present the simple and effective road network. In this case, it may correct and changes the actions of driver in emergency conditions by the concept of agent cooperation for achieving the common target.

Botta A., de Donato W., Persico V., Pescapé A.

Cloud computing and Internet of Things (IoT) are two very different technologies that are both already part of our life. Their adoption and use are expected to be more and more pervasive, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios.In this paper, we focus our attention on the integration of Cloud and IoT, which is what we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately and, more precisely, their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the new CloudIoT paradigm, which involves completely new applications, challenges, and research issues. To bridge this gap, in this paper we provide a literature survey on the integration of Cloud and IoT. Starting by analyzing the basics of both IoT and Cloud Computing, we discuss their complementarity, detailing what is currently driving to their integration. Thanks to the adoption of the CloudIoT paradigm a number of applications are gaining momentum: we provide an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges. These challenges are then analyzed in details to show where the main body of research is currently heading. We also discuss what is already available in terms of platforms-both proprietary and open source-and projects implementing the CloudIoT paradigm. Finally, we identify open issues and future directions in this field, which we expect to play a leading role in the landscape of the Future Internet. Vision and motivations for the integration of Cloud computing and Internet of Things (IoT).Applications stemming from the integration of Cloud computing and IoT.Hot research topics and challenges in the integrated scenario of Cloud computing and IoT.Open issues and future directions for research in this scenario.

Lamas-Seco J., Castro P., Dapena A., Vazquez-Araujo F.

Wang M., Shan H., Lu R., Zhang R., Shen X., Bai F.

Real-time path planning can efficiently relieve traffic congestion in urban scenarios. However, how to design an efficient path-planning algorithm to achieve a globally optimal vehicle-traffic control still remains a challenging problem, particularly when we take drivers' individual preferences into consideration. In this paper, we first establish a hybrid intelligent transportation system (ITS), i.e., a hybrid-VANET-enhanced ITS, which utilizes both vehicular ad hoc networks (VANETs) and cellular systems of the public transportation system to enable real-time communications among vehicles, roadside units (RSUs), and a vehicle-traffic server in an efficient way. Then, we propose a real-time path-planning algorithm, which not only improves the overall spatial utilization of a road network but reduces average vehicle travel cost for avoiding vehicles from getting stuck in congestion as well. A stochastic Lyapunov optimization technique is exploited to address the globally optimal path-planning problem. Finally, the transmission delay of the hybrid-VANET-enhanced ITS is evaluated in VISSIM to show the timeliness of the proposed communication framework. Moreover, system-level simulations conducted in Java demonstrate that the proposed path-planning algorithm outperforms the traditional distributed path planning in terms of balancing the spatial utilization and drivers' travel cost.

Wang S., Li R., Guo M.

Predicting the duration time of incidents is important for effective real-time Traffic Incident Management (TIM). In the current study, the k-Nearest Neighbor (kNN) algorithm is employed as a nonparametric regression approach to develop a traffic incident duration prediction model. Incident data from 2008 on the third ring expressway mainline in Beijing are collected from the local Incident Reporting and Dispatching System. The incident sites are randomly distributed along the mainline, which is 48.3 km long and has six two-way lanes with a single-lane daily volume of more than 10000 veh. The main incident type used is sideswipe and the average incident duration time is 32.69 min. The most recent one-fourth of the incident records are selected as testing set. Vivatrat method is employed to filter anomalous data for the training set. Incident duration time is set as the dependent variable in Kruskal–Wallis test, and six attributes are identified as the main factors that affect the length of duration time, which are ‘day first shift’, ‘weekday’, ‘incident type’, ‘congestion’, ‘incident grade’ and ‘distance’. Based on the characteristics of duration time distribution, log transformation of original data is tested and proven to improve model performance. Different distance metrics and prediction algorithms are carefully investigated. Results demonstrate that the kNN model has better prediction accuracy using weighted distance metric based on decision tree and weighted prediction algorithm. The developed prediction model is further compared with other models based on the same dataset. Results show that the developed model can obtain reasonable prediction results, except for samples with extremely short or long duration. Such a prediction model can help TIM teams estimate the incident duration and implement real-time incident management strategies.

Wang M., Liang H., Zhang R., Deng R., Shen X.

Coordinated charging can provide efficient charging plans for electric vehicles (EVs) to improve the overall energy utilization while preventing an electric power system from overloading. However, designing an efficient coordinated charging strategy to route mobile EVs to fast-charging stations for globally optimal energy utilization is very challenging. In this paper, we investigate a special smart grid with enhanced communication capabilities, i.e., a VANET-enhanced smart grid. It exploits vehicular ad-hoc networks (VANETs) to support real-time communications among road-side units (RSUs) and highly mobile EVs for collecting real-time vehicle mobility information or dispatching charging decisions. Then, we propose a mobility-aware coordinated charging strategy for EVs, which not only improves the overall energy utilization while avoiding power system overloading, but also addresses the range anxieties of individual EVs by reducing the average travel cost. Specifically, the mobility-incurred travel cost for an EV is considered in two aspects: 1) the travel distance from the current position of the EV to a charging station; and 2) the transmission delay for receiving a charging decision via VANETs. The optimal mobility-aware coordinated EV charging problem is formulated as a time-coupled mixed-integer linear programming problem. By solving this problem based on Lagrange duality and branch-and-bound-based outer approximation techniques, an efficient charging strategy is obtained. To evaluate the performance of the proposed strategy, a realistic suburban scenario is developed in VISSIM to track vehicle mobility through the generated simulation traces, based on which the travel cost of each EV can be accurately calculated. Extensive simulation results demonstrate that the proposed strategy considerably outperforms the traditional EV charging strategy without VANETs on the metrics of the overall energy utilization, the average EV travel cost, and the number of successfully charged EVs.

Yao B., Hu P., Zhang M., Jin M.

Abstract Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

Fei Tao, Ying Zuo, Li Da Xu, Lin Zhang

Recently, cloud manufacturing (CMfg) as a new service-oriented manufacturing mode has been paid wide attention around the world. However, one of the key technologies for implementing CMfg is how to realize manufacturing resource intelligent perception and access. In order to achieve intelligent perception and access of various manufacturing resources, the applications of IoT technologies in CMfg has been investigated in this paper. The classification of manufacturing resources and services, as well as their relationships, are presented. A five-layered structure (i.e., resource layer, perception layer, network layer, service layer, and application layer) resource intelligent perception and access system based on IoT is designed and presented. The key technologies for intelligent perception and access of various resources (i.e., hard manufacturing resources, computational resources, and intellectual resources) in CMfg are described. A prototype application system is developed to valid the proposed method.

Wu He, Gongjun Yan, Li Da Xu

The advances in cloud computing and internet of things (IoT) have provided a promising opportunity to resolve the challenges caused by the increasing transportation issues. We present a novel multilayered vehicular data cloud platform by using cloud computing and IoT technologies. Two innovative vehicular data cloud services, an intelligent parking cloud service and a vehicular data mining cloud service, for vehicle warranty analysis in the IoT environment are also presented. Two modified data mining models for the vehicular data mining cloud service, a Naïve Bayes model and a Logistic Regression model, are presented in detail. Challenges and directions for future work are also provided.

Taghvaeeyan S., Rajamani R.

This paper focuses on the development of a portable roadside magnetic sensor system for vehicle counting, classification, and speed measurement. The sensor system consists of wireless anisotropic magnetic devices that do not require to be embedded in the roadway-the devices are placed next to the roadway and measure traffic in the immediately adjacent lane. An algorithm based on a magnetic field model is proposed to make the system robust to the errors created by larger vehicles driving in the nonadjacent lane. These false calls cause an 8% error if uncorrected. The use of the proposed algorithm reduces this error to only 1%. Speed measurement is based on the calculation of the cross correlation between longitudinally spaced sensors. Fast computation of the cross correlation is enabled by using frequency-domain signal processing techniques. An algorithm for automatically correcting for any small misalignment of the sensors is utilized. A high-accuracy differential Global Positioning System is used as a reference to measure vehicle speeds to evaluate the accuracy of the speed measurement from the new sensor system. The results show that the maximum error of the speed estimates is less than 2.5% over the entire range of 5-27 m/s (11-60 mi/h). Vehicle classification is done based on the magnetic length and an estimate of the average vertical magnetic height of the vehicle. Vehicle length is estimated from the product of occupancy and estimated speed. The average vertical magnetic height is estimated using two magnetic sensors that are vertically spaced by 0.25 m. Finally, it is shown that the sensor system can be used to reliably count the number of right turns at an intersection, with an accuracy of 95%. The developed sensor system is compact, portable, wireless, and inexpensive. Data are presented from a large number of vehicles on a regular busy urban road in the Twin Cities, MN, USA.

Aazam M., Khan I., Alsaffar A.A., Huh E.

With the trend going on in ubiquitous computing, everything is going to be connected to the Internet and its data will be used for various progressive purposes, creating not only information from it, but also, knowledge and even wisdom. Internet of Things (IoT) becoming so pervasive that it is becoming important to integrate it with cloud computing because of the amount of data IoT's could generate and their requirement to have the privilege of virtual resources utilization and storage capacity, but also, to make it possible to create more usefulness from the data generated by IoT's and develop smart applications for the users. This IoT and cloud computing integration is referred to as Cloud of Things in this paper. IoT's and cloud computing integration is not that simple and bears some key issues. Those key issues along with their respective potential solutions have been highlighted in this paper.

Mohammed Ali S.S., George B., Vanajakshi L.

This paper presents an effective multiple-inductive-loop pattern suitable for heterogeneous and less lane-disciplined traffic and its performance evaluation. Vehicle detection system based on conventional inductive loops works well only for lane-based and homogeneous traffic. A multiple-loop system for sensing vehicles in a heterogeneous and less lane-disciplined condition has been reported recently. The scheme proposed in this paper employs a new configuration, where all the loops are connected in series, which considerably reduces the system complexity and improves reliability. Each loop has a unique resonance frequency and the excitation source given to the loops is programmed to have frequency components covering all the loop resonance frequencies. When a vehicle goes over a loop, the corresponding inductance and resonance frequency will change. The shift in frequency or its effect in any/every loop can be simultaneously monitored, and the vehicles can be detected and identified as a bicycle, a motorcycle, a car, a bus, etc., based on the signature. Another advantage of this scheme is that the loops are in parallel resonance; hence, the power drawn from the source will be minimal. A prototype multiple-loop system has been built and tested based on the proposed scheme. The developed system detected, classified, and counted vehicles accurately. Moreover, the system also computes and provides the speed of the vehicle detected using a single set of multiple loops. The accuracy of the speed measurement has been compared with actual values and found to be accurate and can be used for real-time intelligent transportation system (ITS) applications under heterogeneous and less lane-disciplined (e.g., Indian) conditions.

Zhang X.

The traffic flow prediction highly depends on space and time, and the solution method which improves utilization rate of spatiotemporal data. The paper proposes an algorithm that combines B-spline function method, Gating fusion mechanism, Attention mechanism (weak semantic supervision mechanism) with Space Time Embedding (STE) (BGASTE). B-spline function is used to fit the time data, and at the same time Gating monitoring mechanism is used to eliminate the abnormal data. For spatial data, the node2vec is used to digitize the space and connections between two spaces, and the attention mechanism fuse B-spline data for detail area prediction. BGASTE is a continuous iterative process and can continuously calibrate the prediction functions based on the blocked time data and therefore predict the future trend based on the previous outcome.

Boyarshinov M.G., Vavilin A.S.

The relevance of studying traffic congestion is determined by the need to find a scientifically based criterion for its emergence, development and elimination using modern methods of processing information about car flows. The objective of this study is to reveal a quantitative criterion for emergence and evolution of traffic congestion based on a deterministic estimate of time of movement of individual vehicles in the general flow between control boundaries as a random variable of mean value, mode, median, standard deviation, variation indicators, asymmetry, and kurtosis.The subject of the study related to the patterns of evolution of the listed deterministic indicators of traffic flows, which can be used for operational forecasting of formation, development, and elimination of traffic congestion. The initial data were obtained using hardware and software systems for fixing traffic violations installed on the urban street-and-road network. As a result of the study, it was found that for the same section of the road, the listed deterministic indicators of a random variable differ significantly during free movement of road transport and in case of a traffic jam. It seems promising to use the average value of duration of movement of cars to identify the stages of emergence, development, and disappearance (liquidation) of traffic congestion. The proposed indicator can serve as a basis for developing a mechanism for real-time assessment of the likelihood of emergence of traffic jams, as well as for developing recommendations for the rapid response of transport services to prevent and eliminate them.

Mohanty A., Rahamathunnisa U., Sudhakar K., Sathiyaraj R.

Chapter 2 Age of Computational AI for Autonomous Vehicles Akash Mohanty, Akash Mohanty School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSearch for more papers by this authorU. Rahamathunnisa, U. Rahamathunnisa School of Information Technology and Engineering and Technology, Vellore Institute of Technology, Vellore, IndiaSearch for more papers by this authorK. Sudhakar, K. Sudhakar Department of Computer Science and Engineering, Madanapalle Institute of Technology, Madanapalle, Andhra Pradesh, IndiaSearch for more papers by this authorR. Sathiyaraj, R. Sathiyaraj Department of CSE, GITAM School of Technology, GITAM University, Bengaluru, Karnataka, IndiaSearch for more papers by this author Akash Mohanty, Akash Mohanty School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSearch for more papers by this authorU. Rahamathunnisa, U. Rahamathunnisa School of Information Technology and Engineering and Technology, Vellore Institute of Technology, Vellore, IndiaSearch for more papers by this authorK. Sudhakar, K. Sudhakar Department of Computer Science and Engineering, Madanapalle Institute of Technology, Madanapalle, Andhra Pradesh, IndiaSearch for more papers by this authorR. Sathiyaraj, R. Sathiyaraj Department of CSE, GITAM School of Technology, GITAM University, Bengaluru, Karnataka, IndiaSearch for more papers by this author Book Editor(s):Sathiyaraj Rajendran, Sathiyaraj RajendranSearch for more papers by this authorMunish Sabharwal, Munish SabharwalSearch for more papers by this authorYu-Chen Hu, Yu-Chen HuSearch for more papers by this authorRajesh Kumar Dhanaraj, Rajesh Kumar DhanarajSearch for more papers by this authorBalamurugan Balusamy, Balamurugan BalusamySearch for more papers by this author First published: 25 February 2024 https://doi.org/10.1002/9781119847656.ch2 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary Autonomous vehicles have made a great impact on research and industrial growth over the past era. The automobile industry is now being revolutionized by self-driving (or driverless) technology owing to enhanced and advanced autonomous vehicles that make use of cutting-edge computational methods from the fields of machine intelligence and artificial intelligence (AI). Autonomous vehicles are now able to assess their surroundings with high accuracy, make sensible choices in real-time environments, and function legitimately without human intervention and technological advancements in the arena of computationally powerful AI algorithms. The development of autonomous vehicles relies heavily on cutting-edge computational technologies. The chapter aims to review the contemporary methods of computational models over time and presents the computational models in the arena of Machine Learning, its subset Deep Learning and Artificial Intelligence. The chapter initially discusses the role of AI, followed by its autonomy levels. 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Sukanya C., Sivanantham E.

Alqatow I., Jaradat M., Jayousi R., Rattrout A.

The number of vehicles in Palestine has significantly increased over the past decade leading to significant traffic congestion in cities. The narrow structure of roads within cities, coupled with a lack of development and updates, has exacerbated this problem. Congestion causes air pollution and driver frustration and costs a significant amount in fuel consumption. Additionally, collisions between vehicles waiting at traffic lights can occur due to high speeds or small distances between waiting cars. Finding solutions for this dynamic and unpredictable problem is a significant challenge. One proposed solution is to control traffic lights and redirect vehicles from congested roads to less crowded ones. A multi-agent system is utilized in this study. Based on the JaCaMo platform was developed to address the issue of traffic congestion and collision avoidance. Simulation using SUMO and JADE platforms demonstrated that traffic congestion could be reduced by 52.7% through traffic light timing control.

Ikidid A., El Ghazouani M., El Khanboubi Y., Zaouiat C.A., El Fazziki A., Sadgal M.

The aim of this work is to propose a modeling method for the industrial urban traffic control based on the agent paradigm. Thus, from a detailed functional specification of the urban traffic, we elaborate a method of analysis and design of the traffic light system in five levels. First of all, a problem description phase that allows us to define the problem. Then, a requirement analysis phase that allows us to describe the needs and identify the scenario to be studied. After, the analysis phase that allows us to design the structure of the system, at several levels of abstraction, and to focus on the interactions and behaviors of the components. The Agentification phase that permits us to go from the component to the agent. And finally, the implementation and evaluation phase.

Reddy Y.Y., Vijayalakshmi

This chapter aims to develop an Energy Optimization routing protocol to reduce delay, energy usage, and normalised routing overhead in Underwater Acoustic Sensor Networks (UASN) by using the Novel LION Optimization Algorithm instead of the Discrete Particle Swarm Optimization algorithm. A 20 samples were considered for the process in two groups and each group contains 10 samples were collected with pre-test of 80% power. For the group 1 the LION Optimization algorithm is used, whereas Discrete Particle Swarm Optimization algorithm is used in group 2. The measures of average energy consumption, delay, and normalised routing overhead from network were measured through simulation using NS 2 simulator. The statistical analysis of the performance measures was calculated Using SPSS Software. For vigorously varying environmental and geographical topology condition the proposed novel LION optimization algorithm achieve 12% of reduction in delay, provides 20% of energy consumption and achieves 40% of Normalized routing overhead when compared to Discrete Particle Swarm Optimization algorithm. Observed statistical analysis reveals that the significant value of P is less than 0.05 was achieved. The proposed LION Optimization algorithm performs drastically better energy efficiency than DPSO algorithm which changed into determined from simulation results.

Reddy Y.Y., Vijayalakshmi

The proposed work aims to provide an Energy Optimization routing protocol to enhance Underwater Acoustic Sensor Networks (UWASNenergy)'s efficiency, packet delivery ratio, and normalised routing overhead using the Novel CROW Optimization algorithm in comparison to the AODV Optimization algorithm. The number of samples taken for the two groups is 20. Each group containing a pre-test power of 80% led to the collection of 10 samples. A novel CROW optimization technique is used in group 1, while an AODV algorithm is used in group 2. The NS 2 simulator carries out simulation and measures network performance using the metrics of average energy consumption, delay, and normalised routing overhead. Using SPSS Software, a statistical analysis was performed. A novel CROW optimization algorithm achieves 15% of energy consumption, 25% of delay and 2% of Normalized routing overhead when compared to AODV algorithm. A statistical analysis reveals that the significant value that was achieved is (P 

Sathiyaraj R., Rahamathunnisa U., Jagannatha Reddy M.V., Parameswaran T.

With the development of advanced technologies, healthcare services are improved with improved treatment for diseases and improved patient outcomes. In assisting medical practitioners, cognitive computing systems process a massive amount of data instantaneously to answer specific queries and provide customized intelligent recommendation systems for decision making and diagnosis. Healthcare industries make use of data analytics to identify and diagnose several diseases. This technology supports a user-oriented approach in discovering the patterns which are hidden in the data. Big Data is utilized to facilitate analytics support from the enormous amount of data gathered for processing. Big Data and Cognitive Computing jointly make it possible to achieve accuracy and effectiveness in Healthcare. Healthcare services have been under great anxiety during recent years, particularly when the economic and financial crisis put a significant emphasis on sustainability. Hence, Cognitive Computing and Big Data analytics are more vital in the healthcare sector to meet the technical issues and to provide a solution for any complex problems. This chapter addresses the necessity of converging cognitive computing and big data in providing a proficient and valuable solution for Healthcare applications. In this chapter, we put forward two approaches which are based on cognitive computing and big data analytics. Firstly, a smart healthcare framework is proposed to detect and classify EEG Pathology. This approach uses very deep convolutional networks (VDCN) and ImageNet Classification convolutional network models and it also integrates deep learning. The experimental results reveal the importance of the proposed EEG (Electroencephalography) Pathology classification system in the Healthcare domain. Secondly, a heart disease prediction system, this assists in predicting the heart related diseases. This methodology applies classifiers with neural networks and supports in prediction of the disease. The importance of the proposed frameworks is shown with suitable demonstrations in the chapter. Our proposed methodology will be a good decision making support system for doctors in medical information system.

Lv Z., Gander A.J., Lv H.

With the constant acceleration of digitization process, Digital Twins technology plays a more and more vital role in the development and construction of Sustainable Cities with some core advanced computer technologies, including Big Data, Artificial Intelligence, Cloud Computing, and Edge Computing. The research is aimed to study the application of Digital Twins in cities and its development prospect based on the construction of Smart Cities. Besides, Digital Twins technology is discussed based on the systematic construction of digitization of urban smart public service facilities. The intelligence of public facilities is investigated in terms of urban public drainage facilities, public lighting systems, and intelligent traffic systems. Finally, the integration of the above facilities with future Digital Twins technology is prospected. By combining communication technology and information technology, the analysis is carried out from the social, economic, and environmental perspectives of sustainable development. This work has reference value for the subsequent intelligent and systematic development of infrastructure of smart city and sustainable city.

Sekhar G.C., Vijayalakshmi K.

Xu W., Zhai Y.

Abstract Intelligent traffic recognition system is the development direction of the future traffic system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology into the entire ground traffic management system. It establishes a real-time, accurate, and efficient integrated transportation management system that plays a role in a wide range and all directions. The aim of this article is to integrate cross-modal biometrics into an intelligent traffic recognition system combined with real-time data operations. Based on the cross-modal recognition algorithm, it can better re-identify the vehicle cross-modally by building a model. First, this article first presents a general introduction to the cross-modal recognition method. Then, the experimental analysis is conducted on the classification of vehicle images recognized by the intelligent transportation system, the complexity of vehicle logo recognition, and the recognition of vehicle images with different lights. Finally, the cross-modal recognition algorithm is introduced into the dynamic analysis of the intelligent traffic recognition system. The cross-modal traffic recognition system experiment is carried out. The experimental results show that the intraclass distribution loss function can improve the Rank 1 recognition rate and mAP value by 6–7% points on the basis of the baseline method. This shows that improving the modal invariance feature by reducing the distribution difference between different modal images of the same vehicle can effectively deal with the feature information imbalance caused by modal changes.

Patidar P., Dobson G.B., Carley K.M., Agarwal Y.

Large cities worldwide are facing many challenges, such as a growing population, which leads to an increase in traffic congestion. In many places, city road infrastructure does not cope well with growth in the number of vehicles leading to long waiting times on roads and decreasing fuel economy. Due to inflexibility to update city roads comfortably in most places, the adaptation of smart traffic intersections provides a promising approach to curb growing traffic and reduce wait times on road intersections. Still, faltering public budgets do not allow costly transport infrastructure expenditures, and deploying and maintaining sensors to enable smart intersections throughout a city is resource-intensive. In this paper, we explore the idea of efficiently deploying smart intersections within given budget constraints in a city. We propose a generic simulation-based framework that models city traffic based on historical patterns and utilizes agent-based modeling to select an optimal subset of road intersections to improve traffic conditions for a given city network. We validate our framework with state of the art urban mobility framework and demonstrate its utility with a simple road network. We further employ our framework to study the impact of variation in traffic characteristics and budget constraints on the efficiency of deploying smart lights.

Krukowicz T., Firląg K., Sterniczuk E.

The article describes the problem of incorrect U-turns at intersections with traffic lights. Statistical data on road incidents related to U-turns are presented. Then, the international, Polish and foreign regulations concerning u-turning at intersections with traffic lights were analysed. The situations in which U-turns are allowed or prohibit-ed are presented. The differences in design rules for junctions with U-turns in different countries have been taken into account. A literature review was also carried out that outlined various current U-turns around the world, including the design of turning places, the location of turning points, road safety when turning, and the impact of U-turns on traffic conditions. The further part of the article presents the results of field tests of the U-turn at 6 intersections located in Warsaw. The research was conducted by video observation. The results were broken down by age, gender, place of regis-tration of the vehicle, type of vehicle, and the effect of incorrect turning. Data on road incidents at the examined intersections were also analysed. Data from the database kept by the Police were compared with the measure-ment data. A regression analysis was performed between the types of recorded incorrect manoeuvres and the number of accidents at the intersection. The results of statistical analysis carried out do not indicate the existence of a relationship between the number of identified incorrect U-turns and the number of road incidents at inter-sections. Based on the research, it was found that the phenomenon of incorrect U-turns at intersections with traffic lights is common, and the use of directional (protected) signals does not eliminate this phenomenon. The conclusions indicate practical solutions to reduce the number of illegally U-turning vehicles. The recommended actions are related to the stage of shaping the road network, designing the road geometry and organizing traffic and traffic lights, and auditing road safety, as well as the stage of road operation.

Li S., Ma H.

In this study, the authors first discuss the adaptive synchronisation control for discrete-time non-linear multi-agent systems (MASs) with parameter and non-parameter uncertainty terms under leaderless strongly connected digraph, which has not been mentioned in the existing literature. To cope with these two kinds of uncertainties, a state variable is used as an auxiliary tool to compensate for the non-parameter uncertainties, meanwhile, a new dead zone is constructed to ensure the boundedness of the estimated values of the unknown parameters. Based on the local information within the neighbourhood, distributed adaptive synchronisation protocols are designed. Moreover, they strictly prove that the designed adaptive control protocols can harmonise all agents' dynamics to achieve their synchronisation in theory. At the end of this study, an example is provided to indicate the validness of the proposed control protocols.