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For more than a decade, communication systems based on the IEEE 802.11p technology—often referred to as Dedicated Short-Range Communications (DSRC)—have been considered a de facto industry standard for Vehicle-to-Infrastructure (V2I) communication. The technology, however, is often criticized for its poor scalability, its suboptimal channel access method, and the need to install additional roadside infrastructure. In 3GPP Release 14, the functionality of existing cellular networks has been extended to support V2X use cases in an attempt to address the well-known drawbacks of the DSRC. In this paper, we present a complex simulation study in order to benchmark both technologies in a V2I communication context and an urban scenario. In particular, we compare the DSRC, LTE in the infrastructural mode (LTE-I), and LTE Device-to-Device (LTE-D2D) mode 3 in terms of the average end-to-end delay and Packet Delivery Ratio (PDR) under varying communication conditions achieved through the variation of the communication perimeter, message generation frequency, and road traffic intensity. The obtained results are put into the context of the networking and connectivity requirements of the most popular V2I C-ITS services. The simulation results indicate that only the DSRC technology is able to support the investigated V2I communication scenarios without any major limitations, achieving an average end-to-end delay of less than 100 milliseconds and a PDR above 96% in all of the investigated simulation scenarios. The LTE-I is applicable for the most of the low-frequency V2I services in a limited communication perimeter (<600 m) and for lower traffic intensities (<1000 vehicles per hour), achieving a delay pf less than 500 milliseconds and a PDR of up to 92%. The LTE-D2D in mode 3 achieves too great of an end-to-end delay (above 1000 milliseconds) and a PDR below 72%; thus, it is not suitable for the V2I services under consideration in a perimeter larger than 200 m. Moreover, the LTE-D2D mode 3 is very sensitive to the distance between the transmitter and its serving eNodeB, which heavily impacts the PDR achieved.

Optimizing the deployment of roadside units (RSUs) holds great potential for enhancing the delay performance of vehicular ad hoc networks. However, there has been limited focus on devising RSU deployment strategies tailored specifically for highway intersections. In this study, we introduce a novel probabilistic model to characterize events occurring around highway intersections. By leveraging this model, we analytically determine the expected event reporting delays for both highway segments and intersections. Subsequently, we propose an RSU deployment scheme specifically designed for highway intersections, aimed at minimizing the expected event reporting delay. To implement this scheme, we introduce an innovative algorithm named cooperative walking. Through illustrative examples, we demonstrate that our proposed RSU deployment strategy for highway intersections outperforms the commonly employed uniform RSU deployment scheme and the previously proposed balloon method in terms of delay performance.

As transportation systems evolve, ensuring safe and efficient mobility in Intelligent Transportation Systems remains a priority. Work zones, in particular, pose significant safety challenges due to lane closures, which can lead to abrupt braking and sudden lane changes. Most previous research on Connected and Autonomous Vehicles (CAVs) assumes ideal communication conditions, overlooking the effects of message loss and network unreliability. This study presents a comprehensive smart work zone (SWZ) framework that enhances lane-change safety by the integration of both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. Sensor-equipped SWZ barrels and Roadside Units (RSUs) collect and transmit real-time hazard alerts to approaching CAVs, ensuring coverage of critical roadway segments. In this study, a co-simulation framework combining VEINS, OMNeT++, and SUMO is implemented to assess lane-change safety and communication performance under realistic network conditions. Findings indicate that higher Market Penetration Rates (MPRs) of CAVs can lead to improved lane-change safety, with time-to-collision (TTC) values shifting toward safer time ranges. While lower transmission thresholds allow more frequent communication, they contribute to earlier network congestion, whereas higher thresholds maintain efficiency despite increased packet loss at high MPRs. These insights highlight the importance of incorporating realistic communication models when evaluating traffic safety in connected vehicle environments.

Transportation agencies continuously and consistently work to improve the processes and systems for mitigating the impacts of roadway incidents. Such efforts include utilizing emerging technologies to reduce the detection and response time to roadway incidents. Vehicle-to-infrastructure (V2I) communication is an emerging transportation technology that enables communication between a vehicle and the infrastructure. This paper proposes an algorithm that utilizes V2I probe data to automatically detect roadway incidents. A simulation testbed was developed for a segment of Interstate 64 in St. Louis, Missouri to evaluate the performance of the V2I-based automatic incident detection algorithm. The proposed algorithm was assessed during peak and off-peak periods with various incident durations, under several market penetration rates for V2I technology, and with different spatial resolutions for incident detection. The performance of the proposed algorithm was assessed on the basis of the detection rate, time to detect, detection accuracy, and false alarm rate. The performance measures obtained for the V2I-based automatic incident detection algorithm were compared with California #7 algorithm performance measures. The California #7 algorithm is a traditional automatic incident detection algorithm that utilizes traffic sensors data, such as inductive loop detectors, to identify roadway events. The California #7 algorithm was implemented in the Interstate 64 simulation testbed. The case study results indicated that the proposed V2I-based algorithm outperformed the California #7 algorithm. The detection rate for the proposed V2I-based incident detection algorithm was 100% in market penetrations of 50%, 80%, and 100%. However, the California #7 algorithm’s detection rate was 71%.

Vehicle-to-Infrastructure (V2I) communication is expected to bring tremendous benefits in terms of increased road safety, improved traffic efficiency and decreased environmental impact. In 2017, The 3rd Generation Partnership Project (3GPP) released 3GPP Release 14, which introduced Cellular Vehicle-to-Everything communication (C-V2X), bringing Vehicle-to-Everything (V2X) communication capabilities to cellular networks, hence creating an alternative to Dedicated Short-Range Communications (DSRC) technology. Since then, every new 3GPP Release including Release 15, a first full set of 5G standards, offered V2X capabilities. In this paper, we present a complex simulation study, which benchmarks the performance of LTE-based and 5G-based C-V2X technologies deployed for V2I communication in an urban setting. The study compares LTE and 5G deployed both in the Device-to-Device in mode 3 and in infrastructural mode. Target performance indicators used for comparison are average end-to-end (E2E) latency and Packet Delivery Ratio (PDR). The performance of those technologies is studied under varying communication conditions realized by a variation of vehicle traffic intensity, communication perimeter and message generation frequency. Furthermore, the effects of infrastructure deployment density on the performance of selected C-V2X communication technologies are explored by comparing the performance of the investigated technologies for three infrastructure density scenarios, i.e., involving two, four and eight base stations (BSs). The performance results are put into a context of the connectivity requirements of the most popular V2I communication services. The results indicate that both C-V2X technologies can support all the considered V2I services without any limitations in terms of the communication perimeter, traffic intensity and message generation frequency. When it comes to the infrastructure density deployment, the results show that increasing the density of the infrastructure deployment from two BSs to four BSs offers a remarkable performance improvement for all the considered V2I services as well as investigated technologies and their modes. Further infrastructure density increase (from four BSs to eight BSs) does not yield any practical benefits in the investigated urban scenario.

In today’s era of rapid technological advancement, unmanned aerial vehicles (UAVs) are transforming sectors such as remote delivery, surveillance, and disaster response. However, challenges related to energy consumption and operational efficiency continue to hinder their broader adoption. To address these issues, this study proposes an integrated system design combining dynamic wireless charging (DWC), intelligent trip planning, and intelligent edge computing (IEC). The proposed system leverages IEC for local data processing to reduce latency and optimize energy management, 6G networks for real-time vehicle-to-infrastructure (V2I) communication, and DWC to enable efficient, on-the-go energy replenishment. Additionally, a dynamic arrival management algorithm is introduced to minimize UAV wait times to enhance operational efficiency. Simulations of this system demonstrated significant improvements: larger UAVs achieved an average charging efficiency of 91.2%, while smaller UAVs achieved 92.75%, with dynamic arrival management reducing wait times by an average of 1.5 min for smaller UAVs and 5.0 min for larger UAVs. These findings underscore the system’s effectiveness in optimizing UAV operations and charging efficiency. This integrated approach offers a scalable framework to enhance UAV capabilities and sets a benchmark for future advancements in operational efficiency and charging technology for urban and environmental applications.

This paper proposes a theoretical framework for optimizing traffic flow in autonomous vehicle (AV) networks using 6G communication systems. We propose a novel technique to eliminate conventional traffic signals through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. The article demonstrates traffic flow optimization, density, and safety improvements through real-time management and decision-making. The theoretical foundation involves the combination of multi-agent deep reinforcement learning, coupled with complex analytical models across the partition managing intersections, thus forming the basis of proposed innovative city advancements. From the theoretical analysis, the proposed approach shows a relative improvement of 40-50% in intersection waiting time, 50-70% in accident probability, and 35% in carbon footprint. The above improvements are obtained by applying ultra-low latency 6G communication with the sub-millisecond response and accommodating up to 10000 vehicles per square kilometre. In addition, an economic evaluation revealed that such a system would generate a return on investment by 6.7 years, making this system a technical and financial system for enhancing an intelligent city.

This paper presents the findings from an autonomous shuttle pilot program conducted at the University of North Carolina at Charlotte between June and December 2023 as part of the North Carolina Department of Transportation’s Connected Autonomous Shuttle Supporting Innovation (CASSI) initiative. The shuttle completed 825 trips, transporting 565 passengers along a 2.2-mile mixed-traffic campus route. The study evaluates the shuttle’s operational performance, reliability, and challenges using data from onboard sensors, system logs, and operator reports. Key analyses are divided into four areas: service reliability, which assesses autonomy disengagements caused by signal loss, technical issues, and environmental factors; service robustness, focusing on the shuttle’s ability to maintain operations under adverse conditions; performance metrics, including average speed, autonomy percentage, and battery usage; and service usage, which examines the number of trips and passengers to gauge efficiency. Signal loss and battery-related issues were the primary causes of service interruptions, while environmental factors like weather and vegetation also affected shuttle performance. Recommendations include enhancing vehicle-to-infrastructure communication and optimizing battery management.

The connectivity probability analysis of vehicular networks can be employed for providing theoretical guidance for both obtaining an accurate real-time traffic information and reducing hazardous traffic situations. Most previous studies focused on analyzing the connectivity probability of vehicular networks in physical (PHY) layer protocol. However, the effects of packet collision in media access control (MAC) layer on the connectivity probability of vehicular networks have been rarely studied, where MAC and PHY layers actually interact on each other. In this paper, some parameters are dynamically set and analyzed under consideration of the influence of MAC and PHY layers on the connectivity probability of vehicular networks. Numerical results are shown to be consistent with the proposed theoretical analysis.

Vehicular ad hoc Networks (VANETs) enable vehicles to communicate with each other as well as with roadside units (RSUs). Although there is a significant research effort in radio channel modeling focused on vehicle-to-vehicle (V2V), not much work has been done for vehicle-to-infrastructure (V2I) using 3D ray-tracing tools. This work evaluates some important parameters of a V2I wireless channel link such as large-scale path loss and multipath metrics in a typical urban scenario using a deterministic simulation model based on an in-house 3D Ray-Launching (3D-RL) algorithm at 5.9 GHz. Results show the high impact that the spatial distance; link frequency; placement of RSUs; and factors such as roundabout, geometry and relative position of the obstacles have in V2I propagation channel. A detailed spatial path loss characterization of the V2I channel along the streets and avenues is presented. The 3D-RL results show high accuracy when compared with measurements, and represent more reliably the propagation phenomena when compared with analytical path loss models. Performance metrics for a real test scenario implemented with a VANET wireless sensor network implemented ad-hoc are also described. These results constitute a starting point in the design phase of Wireless Sensor Networks (WSNs) radio-planning in the urban V2I deployment in terms of coverage.