Advanced Intelligent Transportation Systems Research Articles

A Careful Examination of the Security and Traffic Effects Associated with the Vehicle Ad-hoc Network (VANET)

During recent years, vehicular ad hoc networks, or VANETs, have emerged as one of the most exciting and difficult study fields. VANET is regarded as a subset of MANET, or mobile ad hoc network, which is primarily used on automobiles. With the use of traffic data, VANET hopes to supply an advanced Intelligent Transportation System (ITS) with a wealth of information. Mention how daily traffic data collection helps with transportation planning, particularly with regard to intra-city communication. Two of the main issues with the existing traffic system are jams in traffic and accidents. Around the world, numerous people lose their lives or suffer severe injuries in traffic accidents each year. Human lives on the road are directly impacted by these issues. VANET can help to avert these mishaps and facilitate the efficient operation of daily traffic. Furthermore, VANET offers a plethora of intriguing applications, including real-time traffic condition monitoring, dynamic route scheduling, blind crossing prevention, safety, and more. In addition, there are certain drawbacks in terms of traffic and security. The absence of a centralised infrastructure in VANET is one of the main security issues. One of the most difficult jobs in the resultant decentralised and self- organizing VANETs is the administration of the wireless channel to make an efficient use of its capacity because there is no centralised infrastructure in charge of synchronisation and coordination of transmissions. VANET is primarily used to lower the risk of accidents in urban areas with a high volume and complexity of vehicles. We introduce the VANET Security R&D Ecosystem and examine traffic in this article. The four main components of the R&D ecosystem are end customers, government agencies, automakers, and university research. Every facet of VANET is covered in detail. Our study primarily focuses on the security and traffic within VANETs, including how data is safeguarded from vehicle-to-vehicle (V2V), vehicle-to-roadside infrastructure communications devices (V2I), access points, and other sources.

IoV-6G+: A secure blockchain-based data collection and sharing framework for Internet of vehicles in 6G-assisted environment

The growing need for wide and ubiquitous accessibility to advanced Intelligent Transportation Systems (ITS) has led to the evolution of conventional Vehicle to Everything (V2X) paradigms into the Internet of Vehicles (IoVs). Next-generation IoVs establish seamless connections among humans, vehicles, Internet of Things (IoT) devices, and service platforms to enhance transit efficiency, road safety, and environmental conservation, with notable advancements in IoV technologies, specifically in secure data exchange and user privacy protection. Additionally, Unmanned Aerial Vehicles (UAVs) are envisioned as scalable and adaptable solutions for comprehensive IoV service coverage. The current approaches in IoV mainly concentrate on local infrastructure setups, overlooking the potential of infrastructure-less IoVs that lack extensive edge facilities. Also, the transmission of data over public channels by vehicles equipped with servers is susceptible to interception and tampering by malicious attackers. Moreover, the substantial amount of real-time data generated by vehicles, IoT devices, travelers, and social interactions pose a significant strain on servers as well as latency issues. To address these security concerns, this research suggests a secure-lightweight data collection and sharing framework for 6G-assisted smart transportation using blockchain and UAV, called IoV-6G+. The process entails drones collecting information and transmitting it to dedicated edge servers that securely aggregate the information and generate transactions on a cloud server. A cloud server securely collects data from edge servers, creates transactions, combines them into blocks, and then confirms and adds these blocks to the blockchain through a voting-based consensus method in a peer-to-peer network of cloud servers. Additionally, the security analysis of our proposed IoV-6G+ is assessed using the “Informal and formal (i.e., Scyther Tool, and Real-or-Random (RoR) Model) methods”, showcasing its efficacy in delivering a secure and efficient authentication mechanism for IoV. Moreover, the proposed IoV-6G+ exhibits lower computational and communication costs as well as energy consumption, along with enhanced security features, when compared to existing authentication frameworks.

Exploring static rebalancing strategies for dockless bicycle sharing systems based on multi-granularity behavioral decision-making

In the continuously evolving context of urbanization, more people flock to cities for job opportunities and an improved quality of life, resulting in undeniable pressure on transportation networks. This leads to severe daily commuting challenges for residents. To mitigate this urban traffic pressure, most cities have adopted urban dockless bicycle sharing systems (UDBSS) as an effective measure. However, making accurate decisions regarding UDBSS demand in different city locations is crucial, as incorrect choices can worsen transportation problems, causing difficulties in finding bicycles or excessive deployments leading to disorderly accumulation. To address this decision-making challenge, it is essential to consider uncertain factors like daily weather, temperature, and workdays. To tackle this effectively, we construct an adjustable multi-granularity (MG) complex intuitionistic fuzzy (CIF) information system using complex intuitionistic fuzzy sets (CIFSs). This system objectively determines classification thresholds using an evaluation-based three-way decision (TWD) method, creating adjustable MG CIF probability rough sets (PRSs). Additionally, to recognize the irrationality of decision-makers (DMs), we propose a method that combines prospect theory (PT) with regret theory (RT), providing a more comprehensive understanding of the influence of DMs' psychological factors on decision outcomes. Building upon these foundations, we present static rebalancing strategies for UDBSS based on MG PRSs and prospect-regret theory (P-RT) within the CIF information system. Finally, using UDBSS data collected from various sensors, we conduct experimental analysis to verify its feasibility and stability. In summary, this approach considers residents’ daily usage preferences, including bicycles utilization and return, with the aim of minimizing unmet resident demands and predicting usage patterns for the next day. It effectively addresses the issue of UDBSS distribution inefficiencies and holds a significant advantage in prediction, making it suitable for broader applications in transportation systems and contributing to the establishment of more advanced modern intelligent transportation systems (MITSs) in the future.

Maximizing VANET performance in cluster head selection using Intelligent Fuzzy Bald Eagle optimization

Vehicular Ad-hoc Network (VANET) is a type of wireless network that allows communication among vehicles and roadside units to develop advanced intelligent transportation systems. The success of VANETs depends on the stability of wireless communication, among vehicles, which is challenging to achieve due to high vehicle speed, rapidly changing topology, and unstable communication links. Moreover, the instability of the network caused by the mobile nature of vehicles in VANET reduces the performance of the network. Clustering in VANETs is a crucial technique that organizes the network and forms the basis of the routing protocol. Clustering algorithms are designed for VANETs to work efficiently and require several phases that must be integrated into the process before a clustering decision can be made. Therefore, this paper presents a novel Intelligent Fuzzy Bald Eagle (IFBE) optimization to enhance the performance of VANETs by optimizing the cluster head selection (CHS) process. The experimental results prove that the IFBE approach outperforms other existing mechanisms in terms of energy consumption, end-to-end delay, and packet delivery ratio. The proposed mechanism utilizes an intelligent fuzzy system to optimize the CHS process. The fuzzy system uses a set of rules and membership functions to evaluate the candidate nodes' suitability for being cluster heads. The Bald Eagle Search (BES) optimization meta-heuristic algorithm is used to find the optimal values for the fuzzy system's membership functions. The proposed mechanism was evaluated using the MATLAB simulator. Finally, the experimental result proved that the IFBE approach achieved minimum delay and energy consumption of 13.58 ms, and 15.5 J, and higher clustering efficiency and packet delivery rate of 94.15% and 97.65% respectively, which show that it performs better than other existing approaches.

Artificial intelligence (AI) empowered anomaly detection for autonomous vehicles in 6G-V2X

The development of advanced Intelligent Transportation Systems has been made possible by the rapid expansion of autonomous vehicles (AVs) and networking technology (ITS). The in-vehicle users' increased data needs from AVs put the vehicle's trajectory data in danger and make it more susceptible to security threats. In this paper, Autonomous vehicles (AVs) transform the intelligent transportation system by exchanging real-time and seamless data with other AVs and the network (ITS). Transportation that is automated has many advantages for people. However, worries about safety, security, and privacy continue to grow. The AVs need to exchange sensory data with other AVs and with their own for navigation and trajectory planning. When an unreliable sensor-equipped AV or one that is malicious enters connectivity in such circumstances, the results could be disruptive. To effectively detect anomalies and mitigate cyberattacks in AVs, this study suggests the Efficient Anomaly Detection (EAD) method. The EAD technique finds and isolates rogue AVs using the Multi-Agent Reinforcement Learning (MARL) algorithm, which operates over the 6G network to thwart modern cyberattacks and provide a quick and accurate anomaly detection mechanism. The expected outcomes demonstrate the value of EAD and have an accuracy rate that is 8.01% greater than that of the current systems.

Travel Time Prediction Utilizing Hybrid Deep Learning Models

Travel time prediction is vital to the development and maintainence of advanced intelligent transportation system technologies. The travel time on a road segment is dependent on various factors like dynamic traffic demands, incidents, weather conditions, and geometric factors. However, uncertainties associated with prediction performance consistency may reduce the effectiveness of such systems. To tackle these challenges, this paper proposes a hybrid deep learning algorithm-based methodology by integrating variational mode decomposition, multivariate long short-term memory, and quantile regression to predict estimates of travel time ranges instead of single-point predictions. Travel time data collected from loop detectors on motorways near the city of Dublin, Republic of Ireland were modeled. The proposed method was evaluated using various design scenarios and was found to perform efficiently in comparison with conventional deep learning algorithms.

The potential emergence of mid-tier transit

This paper reviews the emerging potential of mid-tier transit, articulating how a complex set of established and new factors could contribute both to better transit outcomes and the associated urban regeneration around station precincts. The analysis is based on two structured literature reviews, supported by insights from the authors’ original research. The first provides an overview of the established and new rationale for mid-tier technologies such as the established Light Rail Transit (LRT) and Bus Rapid Transit (BRT) as well as the new Trackless Tram Systems (TTS). The established role for mid-tier transit is now being given extra reasons for it to be a major focus of urban infrastructure especially due to the need for net zero cities. The second review, is a detailed consideration of established and new factors that can potentially improve patronage on mid-tier transit. The established factors of urban precinct design like stop amenities and improved accessibility and density around stations, are combined with new smart technology systems like advanced intelligent transport systems and real-time transport information for travellers, as well as new transport technologies such as micro-mobility and Mobility on Demand. Also explored are new processes with funding and development models that properly leverage land value capture, public private partnerships, and other entrepreneurial development approaches that are still largely not mainstreamed. All were found to potentially work, especially if done together, to help cities move into greater mid-tier transit.

AI-Empowered Trajectory Anomaly Detection and Classification in 6G-V2X

The immense growth of Autonomous Vehicles (AVs) and networking technologies have paved the way for advanced Intelligent Transportation Systems (ITS). AVs increase data demands from in-vehicle users, which pose a significant risk to the vehicular trajectory data and are extremely vulnerable to security threats. It is challenging to describe and detect the trajectory anomalies in urban motion behavior due to the enormous coverage and complexity of ITS in the V2X environment. Most existing systems rely on a restricted number of single detection strategies, such as determining frequent patterns and have limited accuracy in detecting anomalous trajectories. However, they focus only on outlier detection, failing to consider different patterns of anomalous trajectories. This paper proposes Efficient Trajectory Anomaly Detection and Classification (ETADC) framework in a 6G-V2X environment. The proposed ETADC framework employs the Deep Deterministic Policy Gradient algorithm (DDPG) to improve accuracy and efficiency by analyzing multiple strategies, namely driving speed, driving distance, driving direction, and driving time. The result analysis shows that the proposed ETADC technique outperforms the existing systems by 97% accuracy.

Guest Editorial Introduction to the Special Issue on Cognitive Networking for Intelligent Transportation Systems

Cognitive networking is expected to analyze and utilize the various information for improving the intelligence of transportation systems. For example, through the Vehicleto-Vehicle (V2V), Infrastructure-to-Vehicle, and Vehicular-to-Infrastructure (V2I) communications, which are the foundation and key support technologies determining the overall performance of advanced Intelligent Transportation Systems (ITS), road safety and traffic efficiency are significantly improved.

HAPS-ITS: Enabling Future ITS Services in Trans-Continental Highways

With the advent of rapid globalization and the inter-border supply chain network, the reliability and efficiency of transportation systems have become even more critical. Indeed, trans-continental highways need particular attention due to their important role in sustaining globalization. In this context, intelligent transportation systems (ITS) can actively enhance the safety, mobility, productivity, and comfort of trans-continental highways. However, ITS efficiency depends greatly on the roads where they are deployed, on the availability of power and connectivity, and on the integration of future connected and autonomous vehicles. To this end, high altitude platform station (HAPS) systems, due to their mobility, sustainability, payload capacity, and communication/caching/computing capabilities, are seen as a key enabler of future ITS services for trans-continental highways; this paradigm is referred to as HAPS-ITS. The latter is envisioned as an active component of ITS systems to support a plethora of transportation applications, such as traffic monitoring, accident reporting, and platooning. This article discusses how HAPS systems can enable advanced ITS services for trans-continental highways, presenting the main requirements of HAPS-ITS and a detailed case study of the Trans-Sahara highway.

Connected Vehicles: V2V and V2I Road Weather and Traffic Communication Using Cellular Technologies.

There is a continuous need to design and develop wireless technologies to meet the increasing demands for high-speed wireless data transfer to incorporate advanced intelligent transport systems. Different wireless technologies are continuously evolving including short-range and long-range (WiMAX, LTE, and 5G) cellular standards. These emerging technologies can considerably enhance the operational performance of communication between vehicles and road-side infrastructure. This paper analyzes the performance of cellular-based long-term evolution (LTE) and 5GTN (5G Test Network) in pilot field measurements (i.e., vehicle-to-vehicle and vehicle-to-infrastructure) when delivering road weather and traffic information in real-time environments. Measurements were conducted on a test track operated and owned by the Finnish Meteorological Institute (FMI), Finland. The results showed that 5GTN outperformed LTE when exchanging road weather and traffic data messages in V2V and V2I scenarios. This comparison was made by mainly considering bandwidth, throughput, packet loss, and latency. The safety critical messages were transmitted at a transmission frequency of 10 Hz. The performance of both compared technologies (i.e., LTE and 5GTN) fulfilled the minimum requirements of the ITS-Assisted Road weather and traffic platform to offer reliable communication for enhanced road traffic safety. The field measurement results also illustrate the advantage of cellular networks (LTE and 5GTN) with a clear potential to use it heterogeneously in future field tests with short-range protocols, e.g., IEEE 802.11p.

A QoS Aware Cluster Head Selection and Hybrid Cryptography Routing Protocol for Enhancing Efficiency and Security of VANETs

Nowadays, VANET (vehicular ad hoc network) is one of the key aspects of developing advanced intelligent transportation systems. Due to its huge mobility and rapid topology alteration, the network exposes to link failure that affects the firmness of the network and causes delay and congestion. Additionally, the dynamic change in the network routing affects the network’s security, makes it vulnerable to various attacks, and causes data loss. In order to overcome these drawbacks an efficient and highly secured routing protocol is needed. Subsequently, in this research, a new routing protocol is proposed that has the combination of quality of service (QoS)-aware cluster head (CH) selection and hybrid cryptography, which is named QoS+. The QoS+ protocol is mainly divided into QoS-based CH selection and hybrid cryptography modules. The CH selection module performs based on QoS parameters attempt to provide reliable and stable clusters and improve the firmness and connectivity during the communication process of the network. The hybrid cryptography module contains advanced encryption standard (AES) and elliptic curve cryptosystems (ECC) algorithms. It attempts to improve the security and privacy of the network. The QoS+ protocol is evaluated by a developed VANET simulator using NS2 software. The simulator consists of a network model, a load model, and an attack model. Various speed and transmission ranges and gray hole and wormhole attacks are used in the simulator. The outcome calculated from the performance analysis shows that the proposed QoS+ protocol has 7% to 24% higher message success rate, 500 to 800 higher packets normalized routing load, 350 to 550 Kbps higher throughput, 5% to 17% higher efficiency and 50ms to 12ms lower end to end delay when compared with the earlier works of ECHS and KMSUNET. The proposed QoS+ protocol also achieves superior performance in terms of CH efficiency, cluster member efficiency, and average cluster number with various speeds and transmission ranges.

An Application-Driven Framework for Intelligent Transportation Systems Using 5G Network Slicing

Vehicular networks are critical pieces in support of advanced intelligent transportation systems (ITS). These networks are formed by vehicles that can be connected to one another as well as to the infrastructure, and are subject to constant topology changes, disconnections, and data congestion. Each ITS application could have a different set of communication requirements, such as delay, bandwidth, and packet delivery ratio. Meeting these heterogeneous requirements in the complex dynamic environment of vehicular networks is a challenge. This paper develops a new framework for application-driven vehicular networks using 5G network slicing. We present the architecture of the proposed solution and design algorithms for heterogeneous traffic in a dynamic vehicular environment. Our simulations on realistic vehicular scenarios show significant improvements in network performance compared to the state-of-the-art approaches.

Multi-Channel Blockchain Scheme for Internet of Vehicles

With the development of advanced information and communication technology, the traditional centralized service model alone no longer meets the increasing demand of data exchange in intelligent transportation systems (ITS). While Internet of Vehicles (IoV) technology has been introduced to achieve more advanced ITS, there are still some unsettled issues such as flexibility and fault tolerance. The conventional centralized approach for ITS is vulnerable to the single point of failure, and lack of flexibility due to its dependence on a trusted third party (TTP). The emergence of blockchain technology provides a potential direction to address these problems. However, due to varying vehicle densities, it is challenging to select the best blockchain parameters to satisfy the application requirements. In this paper, we propose a multi-channel blockchain scheme that can use the best parameters in accordance with the vehicle density. The proposed scheme first defines multiple blockchain channels where each channel is optimized for a certain vehicle density level. Then, the system selects the best channel according to the vehicle density, and the application requirements on the transaction throughput and latency. We use extensive simulations to show that the proposed blockchain scheme achieves a significantly better performance as compared with existing baselines.

Concepts of Development of Alternative Travel in Autonomous Cars

Autonomous car travel planning is increasingly gaining attention from scientists and professionals, who are addressing the integration of autonomous cars into the general urban transportation system. Autonomous car travel planning depends on the transport system infrastructure, the dynamic data, and their quality. The efficient development of travel depends on the development level of the Intelligent Transport Systems (ITS) and the Cooperative Intelligent Transport Systems (C-ITS). Today, most cities around the world are competing with each other to become the smartest cities possible, using and integrating the most advanced ITS and C-ITS that are available. It is clear that ITS and C-ITS are occupying an increasing share of urban transport infrastructure, so the complex challenges of ITS and C-ITS development will inevitably need to be addressed, in the near future, by integrating them into the overall urban transport system. With this in mind, the authors proposed three autonomous car travel development concepts that should become a conceptual tool in the development of ITS and C-ITS.

Vehicular Communications for ITS: Standardization and Challenges

Intelligent transportation systems (ITS) are fast moving from connected vehicles on the road to autonomous driving. Vehicular communication is a key enabler for the deployment of advanced ITS applications such as platooning and remote vehicle control. DSRC and C-V2X are two key wireless technologies that will play a vital role in the implementation and deployment of an autonomous transport system. We review the current standardization efforts for both of these technologies with a focus on MAC and PHY layers. The automotive industry and research community have been working on new standards -- IEEE 802.11bd (for DSRC) and 5G NR V2X (for C-V2X) -- to meet the high reliability and low latency requirements of autonomous driving. We also highlight the major changes that have been made to previous standards such as IEEE 802.11p and C-V2X. Finally, we present open challenges that need to be addressed to further improve these standards for vehicular communications.

Observability Analysis and Adaptive Information Fusion for Integrated Navigation of Unmanned Ground Vehicles

Integrated navigation of unmanned ground vehicles (UGV) is significant for many advanced intelligent transportation system applications. Adaptive information fusion technique based on observability analysis has a great potential to enhance UGV integrated navigation systems for the capability of high-precision positioning and navigation. In an integrated navigation system, the tolerance against unknown and time-varying observation conditions is a key factor to satisfy the specific requirements of high-precision, self-adaption, and high reliability. Thus, a novel adaptive federated Kalman filter (FKF) is proposed with time-varying information sharing factors based on the criteria for the degree of observability. In addition, an error-state cascaded integration architecture is designed for UGV integrated navigation. Simulation with real datasets gathered from road tests in urban areas showed that the new adaptive integrated navigation system can autonomously update FKF information sharing factors according to the measurement quality and the observability of each navigation error-state. Therefore, the accuracy, robustness, and fault-tolerance ability of the whole system can be effectively improved in a high dynamic environment.

Propagation Channels of 5G Millimeter-Wave Vehicle-to-Vehicle Communications: Recent Advances and Future Challenges

Wireless vehicular communications and sensing technologies are key to enabling more advanced intelligent transportation systems (ITSs) with improved safety and efficiency. Within the realm of wireless communication, millimeter-wave (mmwave) technology has recently received much attention, providing rich spectrum resources to support the timely transmission of large amounts of data. This is especially important for vehicular applications because the number of sensors on modern vehicles is rapidly increasing and thus generating large amounts of data. To fully exploit this potential, understanding mm-wave vehicle-to-vehicle (V2V) propagation channels is crucial. In this article, we review the state of the art in mm-wave V2V channel measurements and modeling, describe recent directional V2V channel measurements performed in the 60-GHz band, and discuss future challenges to be addressed in mm-wave V2V channel measurements and modeling.

Dynamic spectrum access based simultaneous non-contiguous OFDM radar sensing and communication

With the concept of advanced intelligent transportation systems, the integration of data with technology underway with internet of things brings the concept of always connected and cognizant vehicles as an application area. The major constituents of internet of things are connection, convergence, convenience and the connected car. Road safety and vehicular infotainment are the most emerging issues for the automakers in this regard. Implementation of vehicular safety in urban lanes requires vehicle to vehicle sensing and data communication simultaneously. Because of operation spectrum scarcity, the requirement of vehicle to vehicle sensing and communication on a single platform simultaneously empowered with cognitive radio would be cost efficient. Dynamic Spectrum Access based integrated radar sensing and wireless communication transceiver is demonstrated in this work. In the proposed hardware model a direct sequence spread spectrum radar transceiver and an non contiguous orthogonal frequency division multiplexing communication transceiver is used.

The Combined Distribution and Assignment Model: A New Solution Algorithm and Its Applications in Travel Demand Forecasting for Modern Urban Transportation

With the development of the advanced Intelligent Transportation System (ITS) in modern cities, it is of great significance to upgrade the forecasting methods for travel demand with the impact of ITS. The widespread use of ITS clearly changes the urban travelers’ behavior at present, in which case it is difficult for the conventional four-step travel demand forecasting model to have good performance. In this study, we apply the combined distribution and assignment (CDA) model to forecasting travel demand for modern urban transportation, in which travelers may choose the destination and path simultaneously. Furthermore, we present a new solution algorithm for solving the CDA model. With the network representation method that converts the CDA model into a standard traffic assignment problem (TAP), we develop a new path-based algorithm based on the gradient projection (GP) algorithm to solve the converted CDA model. The new solution algorithm is designed to find a more accurate solution compared with the widely used algorithm, the Evans’ two-stage algorithm. Two road networks, Sioux Falls and Chicago Sketch, are used to verify the performance of the new algorithm. Also, we conduct some experiments on the Sioux Falls network to illustrate several applications of the CDA model in consideration of the influences of ITS.