Optimal Extreme Learning Machine based Traffic Congestion Control System in Vehicular Network

Traffic congestion is handled by government agencies as an urban mobility problem, which generates stress to drivers and economic loss. Intelligent transport systems can assist in the identification and reduction of vehicular traffic congestion. In this context, this paper proposes CARTIM, a protocol for collaborative identification and minimization of vehicular congestion. The protocol uses V2V (Vehicle-to-Vehicle) communication to cooperatively measure the local level of vehicular traffic congestion. Additionally, if any infrastructure is present, consolidated information dissemination may occur to vehicles in other regions through V2I (Vehicle-to-Infrastructure) communication. To effectively identify a traffic congestion locally, CARTIM employs a fuzzy logic-based system, which is used in the treatment of qualitative information (vehicle density etc). The protocol also efficiently uses the collaborative communication channel, preventing overload. The simulation results showed that CARTIM can detect congestion (better than related work) and minimize it.

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Optimizing spectral efficiency based on poisson queuing model for procuring cognitive intelligence in vehicular communication

The fast emergence of autonomous vehicles promises a drastic change on how road traffic congestion is detected, controlled, and mitigated. To this end, we believe that it is beneficial to explore different ways of collaboration between autonomous vehicles, with the aid of modern road infrastructure, to optimize the commuters' travel time. We, therefore, propose in this paper a novel solution based on a multi-parties collaboration framework and built upon the WAVE standard to optimize the usage of the road network and lower commuters' travel time. Our solution, which is based on a Belief-Desire-Intention architecture, enables autonomous vehicles to opt for selfish or collaborative behaviors depending on their goals and current situations. The results obtained from our preliminary prototype under three representative road maps demonstrate the effectiveness of our approach in dealing with traffic congestion.

Vehicular communication networks are a type of network in which vehicles and roadside units are the communicating nodes, providing each other with information such as safety warnings and traffic information. As a cooperative approach, vehicular communications can be more effective in avoiding accidents and traffic congestion, rather than each vehicle trying to solve these problems individually. Therefore, intelligent transportation systems (ITS) have been developed to address the challenges of the safety, security, and efficacy of the current transportation systems. The field of inter vehicular communications (IVC) includes both vehicle-to-vehicle (V2V) and vehicle-to infrastructures (V2I) communication and is also known as the vehicular ad hoc network (VANET). The VANET is recognized as an important component of ITS in various national plans [1]. Direct communication between vehicles may be set up by means of mobile ad hoc networks (MANETs), which do not depend on the altered foundation. Research on MANETs covers the application prerequisites and correspondence conventions for everything from sensor systems to handheld PCs and vehicular frameworks [2].

This paper proposes a generalized model based on the granular computing to recognize and analyze the traffic congestion of urban road network. Using the method of quotient space to reduce the attributes associating with traffic congestion, the identification of traffic congestion evaluation system is established including 3 first class indexes and second class indexes of 11. The weight of evaluation indexes are sorted by value in descending order, which are calculated based on rough set theory. In order to improve the efficiency of traffic congestion identification, the appropriate granular is determined by the model parameter μ. When μ is larger, the identification is more effective and the run time of model is longer conversely. Experiments show when the value of μ is between 0.8 and 0.98, the effect of traffic congestion identification is comprehensive optimal.

Road capacity infrastructure and temporary interruptions in trips constitute the main reasons behind the traffic jam phenomenon. City urbanization and growth further intensify these two reasons through the increase of work area and the demand for mobility. In such a scenario, several issues can emerge, such as higher mobility costs, more frequent traffic jams, more significant environmental damage, reduced quality of life, and more pollution. Therefore, this work presents a Proactive and Reactive Approach for Urban Traffic Management in Vehicular Networks, RIDER, to minimize traffic congestion. RIDER is a fully-distributed protocol that can assume proactive and reactive behaviors for sharing traffic condition information. Vehicles with traffic condition information can organize them-selves to improve traffic flow and reduce traffic congestion. In the proposed solution, vehicles monitor the road traffic condition and proactively share this information when needed, considering adaptive multi-hop communication. If vehicles do not have nearby road traffic information, they executed a reactive traffic information discovery. RIDER was evaluated and compared to previous works, regarding the number of transmitted messages, packet collisions, and traffic congestion metrics.

The rapid urbanization anticipated by the United Nations, projecting that 68% of the global population will reside in urban areas by 2050, underlines the escalating challenges such as public health, traffic congestion, and social inequality in cities. Smart cities, utilizing Information and Communication Technologies (ICTs), emerge as a pivotal solution by optimizing urban infrastructure and enhancing life quality, aiming for sustainable urban development that balances economic growth, environmental protection, and social equity. This article systematically explores the intricate relationships among smart city concepts through a cluster analysis, highlighting how these interact within the scope of urban planning to address such multifaceted challenges. The analysis utilizes a bibliometric review and thematic clustering to synthesize existing definitions and characteristics of smart cities, assess the integration of sustainable practices, and identify the intersections where urban planning can effectively leverage ICTs to enhance city livability and sustainability. Our findings suggest that while smart cities are advancing, significant gaps remain, particularly in integrating technology with aging urban infrastructures and ensuring inclusivity in technology access. Future directions are proposed, emphasizing interdisciplinary approaches and the need for adaptive policies that can accommodate rapid technological advancements without compromising the social and environmental fabric of urban life. This study not only advances academic knowledge but also offers practical insights for public and private sectors committed to fostering more intelligent, sustainable cities aligned with global sustainability goals.

Vehicular networks improve traffic safety and efficiency by wireless communications among vehicles and infrastructures. However, security has always been a challenge to vehicular networks, which may cause severe harm to the intelligent transportation systems. Sybil attack is considered as a serious security threat to vehicular networks since the attacker can disseminate false messages with multiple forged identities. In this paper, we designed a behavior feature and multi-scenario-based Sybil attack detection method, named BMSAD in vehicular networks. In BMSAD, we propose solutions for two scenarios: normal traffic and traffic congestion. A node is allowed to verify the authenticity of another node by estimating their geographic distance based on received signal strength, and compare them to its claimed localizations. Then we design long- and short-term pattern to analyze the similarity of vehicles’ behavior features and trajectory for Sybil nodes detection, which act as the second line of defense in normal traffic scenario. At last, the Sybil nodes of the traffic congestion scenario are detected by logical inference based on kinematics theory. And experiment results demonstrate that the proposed scheme achieves high detection rate and low false positive rate.

The concept of Wireless Sensor Networks (WSNs) has garnered significant global attention due to their wide range of applications. According to the IEEE 802.11 standard, WSNs are wireless networks consisting of sensor nodes (SNs) that interconnect via wireless communication links. These SNs are capable of sensing, processing, and wirelessly transmitting data, even in challenging environments. WSNs are primarily utilized for communication in various domains, including smart cities, healthcare, residential areas, and military applications. However, the deployment of WSNs in environments such as smart cities come with some challenges, particularly traffic congestion. Traffic congestion in smart cities, particularly during peak hours, is often caused by a high volume of vehicles traveling at the same time, resulting in delays, accidents, and inefficiencies under various weather conditions, including sunny and rainy days. Therefore, this paper proposes a novel algorithm called the Fusion-based Intelligent Congestion Management (FICM) algorithm, developed through the integration of the Navigation Reference Spatial Data (NRSD) algorithm and Fusion-based Multimodal Abnormal Detection (FMAD) algorithm. The objective of FICM is to mitigate on-road traffic congestion within smart cities effectively. The algorithm’s performance was evaluated using Network Simulator 3 (NS-3) by comparing its effectiveness with the NRSD and FMAD algorithms. Under sunny weather conditions, the NS-3 simulation results revealed that the FICM algorithm achieved an average False Alarm Rate (FAR) of 0.83%, a Mean Time to Detection (MTTD) of 76.0%, and a Detection Rate (DR) of 84.3%, outperforming both the NRSD and FMAD algorithms. Similarly, under rainy weather conditions, the FICM algorithm demonstrated an average FAR of 14.09%, an MTTD of 57.03%, and a DR of 78.04%, surpassing the performance of the NRSD and FMAD algorithms within the smart city environment.

Vehicle traffic congestion is reflected as delays while traveling. Traffic congestion has a number of negative effects and is a major problem in today's society. Several techniques have been deployed to deal with this problem. In this paper, we have proposed an innovative approach to deal with the problem of traffic congestion using the characteristics of vehicular ad-hoc networks (VANET). We have used the adaptive proportional integral rate controller, a congestion control algorithm designed for the Internet, to deal with the problem of vehicle traffic congestion in vehicular networks. The adaptive PI rate controller is a rate based controller that employs control theory to manage the problem of data traffic congestion in computer networks. Using simulations we have demonstrated the applicability of the algorithm in the domain of vehicle traffic congestion in a VANET.

The role of connected vehicular networks has become vital for the future of smart and modern cities, as it can be envisioned as a stand alone connected network or a bridge between various networks, and end-users. Vehicular networks design provides safety, traffic control management, and cruise control services, among others. Many applications and protocols such as mobility management, service discovery, and routing were proposed over the past years to elevate the performance of vehicular networks communication and connectivity. In that context, understanding the vehicles' mobility characteristic, behavior, and pattern would assist those applications to become user-centric, adaptive, and proactive based on the dynamics of vehicles' projections. In this paper, we evaluate the performance of time-series forecasting techniques for vehicular mobility prediction. We first describe and extract vehicles mobility features into time-series sequences and discuss several machine learning techniques. Then, we compare the performance of recent time-series ML-based techniques, LSTM, and GRNN in terms of complexity and accuracy.

The Ad-hoc Vehicular networks (VANET) was developed through the implementation of the concepts of ad-hoc mobile networks(MANET), which is swiftly maturing, promising, emerging wireless communication technology nowadays. Vehicular communication enables us to communicate with other vehicles and Roadside Infrastructure Units (RSU) to share information pertaining to the safety system, traffic analysis, Authentication, privacy, etc. As VANETs operate in an open wireless connectivity system, it increases permeable of variant type's security issues. Security concerns, however, which are either generally seen in ad-hoc networks or utterly unique to VANET, present significant challenges. Access Control List (ACL) can be an efficient feature to solve such security issues by permitting statements to access registered specific IP addresses in the network and deny statement unregistered IP addresses in the system. To establish such secured VANETs, the License number of the vehicle will be the Identity Number, which will be assigned via a DNS server by the Traffic Certification Authority (TCA). TCA allows registered vehicles to access the nearest two or more regions. For special vehicles, public access should be restricted by configuring ACL on a specific IP. Smart-card given by TCA can be used to authenticate a subscriber by checking previous records during entry to a new network area. After in-depth analysis of Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Average Delay, and Handover Delay, this research offers more secure and reliable communication in VANETs.

This article through to the impact of road traffic system congestion of fuzzy information and fuzzy factor analysis, put forward the theory of the fuzzy method to evaluate the quality of the traffic network system environment, combined with the operational research and queuing theory and relevant principle, evaluate the sanlitun area of traffic conditions, indicates that the factors affecting the traffic system, and a crossroads in sanlitun area are the secondary fuzzy comprehensive evaluation, the traffic congestion of the road network system between good and general. Results show that this method can be qualitative and quantitative comprehensive evaluation of transportation systems to the traffic, to achieve harmony in transportation system.

Information and communication technology (ICT) is a promising solution for mitigating road traffic congestion. ICT allows road users and vehicles to be managed based on real-time road status information. In Tanzania, traffic congestion causes losses of TZS 655 billion per year. The main objective of this study was to develop an optimal approach for integrating real-time road information (RRI) to mitigate traffic congestion. Our research survey focused on three cities that are highly affected by traffic congestion, i.e., Arusha, Mwanza, and Dar es Salaam. The results showed that ICT is not yet utilized fully to solve road traffic congestion. Thus, we established a possible approach for Tanzania based on an analysis of road traffic data provided by organizations responsible for road traffic management and road users. Furthermore, we evaluated the available road information management techniques to test their suitability for use in Tanzania. Using the floating car data technique, fuzzy logic was implemented for real-time traffic level detection and decision making. Based on this solution, we propose a RRI system architecture, which considers the effective utilization of readily available communication technology in Tanzania.