Urban Air Mobility (UAM) offers a promising solution to urban congestion by utilizing low-altitude airspace, effectively alleviating pressure on ground transportation. The integration of air-taxi services with existing ground transport infrastructure enables streamlined, efficient door-to-door travel. However, current research on air-taxi systems often overlooks the role of passenger decision-making in selecting the optimal boarding vertiport, a factor that can greatly impact overall system efficiency and user satisfaction. To address this problem, we propose a Unified Air-Ground Mobility Coordination (UAGMC) framework. This framework, powered by deep reinforcement learning (RL) and Vehicle-to-Everything (V2X) communication, optimizes vertiport selection and dynamically plans air-taxi routes based on real-time air and ground traffic conditions. Experimental results show that our approach reduces average travel time by 32 % compared to traditional allocation methods using proportional distribution. This framework advances overall travel efficiency and provides novel insights for integrating multimodal transportation systems.

With the development of autonomous driving technology, Multi-Access Edge Computing (MEC) is an effective paradigm to support delay-sensitive applications in vehicular networks. However, achieving the real-time offloading strategy and resource allocation in MEC-empowered vehicular networks becomes a challenge. In this paper, we first formulate an offloading optimization problem to minimize system latency and energy consumption. To obtain the optimal policy in real time, the formulated problem is transformed into a Markov Decision Process (MDP) and then solved by the proposed Attention and Feature Fusion Deep Deterministic Policy Gradient (AFF-DDPG) algorithm, where a multi-head attention mechanism is combined with feature fusion to improve the accuracy of the decision. In addition, the exploration ability and learning efficiency of the AFF-DDPG algorithm are further enhanced by exploiting Ornstein-Uhlenbeck (OU) noise and the priority experience replay mechanism. The simulation results show that the proposed AFF-DDPG algorithm achieves a 9.44 % improvement over the DDPG algorithm.

In high-speed railway (HSR) scenarios, it is essential to ensure reliable handover for sustaining always-online communications of trains. However, this reliability is challenged by limited wireless coverage at cell edges and frequent handovers. To tackle these challenges, this paper explores the potential of dynamic beamforming to simultaneously improve the probability of successful handover and mitigate communication disruptions. First, we establish a beamforming-based transmission model for trains during handovers. Based on this model, we derive the impact of the beam directions of the serving and target cells on communication performance. Second, we formulate an optimization problem aiming at maximizing the conditional data rate of the train within handover regions, where the impacts of handover failure, rapid mobility, and beamforming overhead are considered. Third, to solve this optimization problem, we propose a dynamic beam direction adjustment algorithm by leveraging the property of deep reinforcement learning. The algorithm efficiently determines the optimal beam direction adjustment strategy based on the dynamic channel conditions. Finally, compared to state-of-the-art deep learning methods and beamforming strategies, simulation results demonstrate that the proposed method achieves superiority in communication quality at cell edges and handover performance, providing an efficient and reliable technical solution for HSR communications.

The morphological diversity, referring to the variations in traffic network topologies defined in this paper, often emerges and brings difficulties in successfully transferring a pre-trained prediction model from one traffic network to another. Moreover, most existing research primarily assumes that traffic data in source and target networks follow independent and identically distributed (i.i.d.) patterns, which is usually not consistent with real-world situations, particularly when considering morphological diversity. For this inconsistency, many efforts have been made, but they mainly concentrate on temporal aspects, which significantly differ from traffic prediction due to spatial and temporal correlations among road segments, influenced by variations in road topology and traffic behavior. This paper introduces a causality-based spatiotemporal out-of-distribution (OOD) generalization method, which is adaptable to most GNNs for diverse, large-scale, dynamic traffic systems with zero-shot. Furthermore, to enhance the generalization and adaptability of the proposed method, we introduce graph matching and equal-sized graph partitioning to alleviate spatial shift between the source and target traffic networks, reduce and align the scale of the networks. Experiments carried out on traffic flow datasets demonstrate that our method significantly improves the performance of various GNN-based traffic predictors in the situation of morphological diversity, achieving a maximum reduction in MAE of 33.08%. Compared to other OOD-driven baselines, our approach also shows a notable improvement, with up to a 40.58% decrease in MAE.

The deepening process of urbanization poses formidable challenges to the current transportation carrying capacity. The utilization of near-ground space (NGS) and urban air mobility (UAM) greatly enhance spatial dimensions and traffic flexibility of the transportation system. However, the current limited sensing capability falls short in meeting the real-time collaborative environmental sensing and intelligent control requirements of aerial transportation. Integrated sensing and communication (ISAC) combines the sensing system of UAM with 6G communication technologies, enabling them to collaborate and achieve data sensing, transmission, processing, and decision control. The use of artificial intelligence-generated content (AIGC) facilitates real-time data fusion and decision-making, adapting to dynamic and unpredictable environments. In this paper, we first model and analyze the traffic flow in three-dimensional space, achieving knowledge embedding based on artificial potential energy field theory. Next, we design a multimodal data fusion neural network structure, which utilizes the Variational Autoencoder (VAE) to generatively achieve feature fusion and compression. Finally, we construct a UAM digital simulation platform using AirSim, which generates considerable aerial data. The simulation results demonstrate that our proposed approach achieves a feature recognition accuracy of 90.38%. The total latency is below 0.6ms, which exhibits high real-time performance.

The unique nature of road bottleneck areas involves a sudden decrease in lane capacity, making them prone to congestion. Particularly on highways, this issue demands optimization and resolution. The traditional road infrastructure control technique, known as variable speed limit (VSL) technology, not only mandates the installation of gantries for variable message signs but also remains susceptible to various factors such as weather conditions and driver compliance rates. Meanwhile, as the intelligence level of connected and automated vehicles (CAVs) continues to advance, CAV can serve not only the traditional transportation function but also be controlled as a mobile intelligent control platform in the future. Leveraging this, this paper proposes a novel paradigm for highway traffic management. It involves substituting VSL control by controlling CAVs in mixed traffic scenarios to optimize road infrastructure and enhance traffic performance. Specifically, this paper initially proposes a CAVs control strategy aimed at global optimization to enhance overall road operations. Additionally, this paper delves deeper into the impact of compliance rates and penetration rates on the effectiveness of the existing VSL control. Furthermore, our proposed control strategy is compared with the widely implemented VSL, demonstrating a significant enhancement in traffic performance. This strategy leads to a 19.1% increase in average speed as compared to no-control strategy, while the optimization effect achieved by VSL control is only 9.6%. Simulation results reveal the transformation of CAVs into mobile intelligent control platforms, not only optimizing traffic congestion but also effectively replacing traditional infrastructure control to maximize socio-economic benefits.

With the proliferation of cellular vehicle-to-everything (C-V2X), connected and automated vehicles (CAVs) are gradually being commercialized. CAVs can interact with road infrastructure and human-driven vehicles (HDVs) to acquire relevant traffic information, thereby altering the characteristics of the traditional traffic flow. The emergence of CAVs is widely believed to bestow benefits to the traffic system in terms of safety, efficiency, and energy consumption. Nevertheless, as with most phenomena, there are two sides to the coin. Further exploration is necessary to determine whether the emergence of CAVs will trigger adverse effects and the underlying factors that may induce adverse effects. To be specific, this article first delves into how selfish driving behaviors (egoism CAV control strategy) can have an unfavorable impact on the performance of the traffic systems, thereby lowering the traffic efficiency. Subsequently, we develop an unselfish (altruism) CAV control strategy that aims to achieve the global optimization and improve the overall road operational capacity. Based on the simulation results obtained at different inflow and outflow rates on highway, it is evident that egoism driving behavior leads to a 11.55% decrease in average speed performance as compared to the noncontrol strategy, while altruism driving behavior results in a 20.14% improvement. Furthermore, we compare the proposed strategy with the current road infrastructure control, which only improves the average speed performance by 11.6%. This indicates that controlling CAVs has the potential to replace the deployment of the traditional road infrastructure, thereby optimizing the social and economic benefits. This article can provide insightful guidance for the future policy formulation in the transportation authorities, wherein the emergence of CAVs needs to be effectively regulated based on the altruism, thus fostering the establishment and development of a safe and efficient mixed traffic ecosystem.

With the proliferation of connected and automated vehicles (CAVs), densely distributed edge computing nodes have emerged on roadways. Consequently, leveraging CAVs as a mobile computing platform can integrate idle vehicle resources to provide computational services for ubiquitous Internet of Things (IoT) devices. Numerous studies have investigated task offloading strategy in the systems with full CAVs penetration. It is expected that the coexistence of CAVs and human-driven vehicles (HDVs) in mixed traffic systems will continue for a considerable period. However, due to the impact of HDVs on communication performance, the task offloading model designed for the systems with full CAVs penetration are no longer applicable in mixed traffic systems. We explore task offloading schemes using CAVs as a mobile computing platform in mixed traffic systems to address this issue. Specifically, we first model the communication model in mixed traffic systems, taking into account the influence of HDVs on link interference, the alteration of path loss due to the impact of HDVs on routing, and the additional sensing tasks arising from the inability of HDVs and CAVs to communicate. Subsequently, considering that delay and energy consumption are crucial factors affecting the performance of CAVs as a mobile computing platform, we formulate the task offloading scheme as an optimization problem. Additionally, we employ a distributed offloading based on deep learning (DODL) algorithm to obtain approximately optimal offloading decisions. Simulation results demonstrate the effectiveness of the proposed model in mixed traffic systems. By employing the DODL algorithm, the CAVs as a mobile computing platform can achieve enhanced performance in terms of convergence, thereby advancing the development of autonomous driving in mixed traffic systems.

Connected and Automated Vehicles (CAVs) are characterized by diverse communication attributes, embodying the trajectory of future automotive progress. Meanwhile, the transportation system will be in a mixed stage of CAVs and Human-Driven Vehicles (HDVs) for a long time. The study of communication capacity and strategies for mixed traffic systems is of great significance for the popularization of CAVs and the deployment of communication infrastructures. However, current research mainly focuses on the communication capacity analysis in the scenario with full penetration of CAVs, while the influence caused by HDVs on Vehicle-to-Vehicle (V2V) communications and the capacity analysis of connected vehicles in mixed traffic systems need further understanding. To address this issue, this paper considers the shadow fading caused by HDVs on wireless communication links and analyzes the communication capacity in mixed traffic systems. Specifically, we first synthesize the V2V and Vehicle-to-Infrastructure (V2I) communication modes to propose an analytical framework for vehicular network communication capacity in mixed traffic. Then, a predictive communication strategy is also provided that caches the required content at infrastructure in advance according to predicted vehicle trajectories to improve the capacity of vehicular networks in mixed traffic. Furthermore, the derived capacity analysis theorems reveal the communication capacity of mixed traffic is closely related to the CAV penetration rate, the vehicle arrival rate, and the infrastructure deployment interval. Simulation results prove the effectiveness of the proposed framework, and the proposed predictive communication strategy can increase the mixed traffic communication capacity compared to existing communication strategies. The theoretical results herein can guide the implementation of vehicular network applications and the design of communication strategies in mixed traffic systems.

Unmanned aerial vehicle (UAV) relay networks with flexible and controllable characteristics are expected to complement the capacity of the gNB. This paper studies the multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) in a multi-UAV relay network, where the strict latency requirement of URLLC can be achieved by the preemptive multiplexing of eMBB resources. However, this may affect eMBB reliability due to the transmission interruptions. Moreover, given the limited energy resources of UAVs, there is an inherent tradeoff among reliability, delay, spectral efficiency, and energy efficiency. To address these challenges, this paper develops a hierarchical UAV-assisted eMBB/URLLC multiplexing scheduling framework. For the eMBB scheduler, we first utilize multiple UAVs to assist the gNB in relaying eMBB traffic and formulate the eMBB resource allocation problem as an optimization problem. Then, we propose a decomposition-relaxation-optimization algorithm to maximize eMBB data rates while considering the personalized fairness of resource allocation and UAV power consumption. For the URLLC scheduler, we further consider the multiplexing of eMBB/URLLC traffic based on the optimization of eMBB resources. To reduce the performance fluctuations of eMBB, we propose a novel cross-slot strategy to schedule URLLC within two time slots rather than one time slot as in existing works. With this strategy, a deep reinforcement learning-based algorithm is proposed to obtain the optimal strategy for the preemption of URLLC on eMBB. Simulation results show that the proposed algorithms outperform the benchmark schemes in terms of convergence rate, eMBB reliability, personalized resource fairness, UAV consumption, and URLLC satisfaction.

Air-ground Integrated Mobility (AIM), as a disruptive mode of travel, has the tremendous potential to alleviate ground traffic congestion issues substantially. However, the primary challenge in achieving this leapfrog development lies in ensuring driving safety. Receiving collision warnings in time within a limited distance can significantly reduce collision risks, which is crucial for ensuring driving safety in AIM. However, due to challenges in aerial network coverage, ensuring the communication quality of aerial Personal Aerial Vehicles (PAVs) remains difficult, thereby affecting the effective transmission of messages. Furthermore, the integration of ground Connected and Automated Vehicles (CAVs) with aerial PAVs in AIM results in significant differences in user resource requirements. Given the complexity of the AIM environment and the high mobility of PAVs, it is challenging to rapidly and accurately capture user communication quality. Therefore, addressing the differential resource requirements of users in this environment is particularly challenging. To this end, we propose a personalized resource allocation strategy assisted by a Channel Knowledge Map (CKM) in AIM. This strategy aims to meet the personalized resource requirements of users while maintaining the maximum Perception Response Time (PRT), thereby ensuring driving safety. Specifically, the CKM in AIM is constructed to obtain channel states through environment-aware communication. Next, a 3D collision warning system is designed to analyze rigorously the maximum PRT of vehicles under different motion states in avoiding collisions. On this basis, with the help of CKM, the channel knowledge of the user’s location is obtained to quantify the communication and computing resources required by each user to maintain the maximum PRT. Finally, we establish the PRT-driven resource optimization problem and employ Deep Reinforcement Learning (DRL) to seek the optimal resource allocation strategy. Simulation results indicate that the proposed method effectively enhances safety and resource utilization in AIM under resource constraints and uneven distribution.

In vehicular edge computing networks, the real-time, on-demand scheduling of scarce network resources for environmental perception, task offloading, computation, and feedback is vital. However, these coupled processes make resource allocation challenging. Moreover, existing real-time channel measurement techniques in complex vehicular topologies present load, accuracy, and customization difficulties. To address these issues, this paper proposes an on-demand environmental perception and resource allocation strategy. Specifically, with the introduction of a channel knowledge base, we first analyze the coupling relationship between environmental perception, communication, and computation. A model is then proposed for task offloading to schedule the granularity of environment perception, communication resources, and computational resources dynamically. Subsequently, the resource allocation problem is formulated as an optimization problem, aiming to minimize system processing delay and maximize resource utilization while ensuring perception accuracy. To address this, a two-phase optimization-assisted deep reinforcement learning (DRL) algorithm is proposed. The initial phase uses convex optimization to approximate a solution. The second phase proposes a DRL-based algorithm to intelligently schedule dynamic network resources, with the first phase’s solution guiding the initial exploration space to enhance DRL training efficiency. Extensive simulation experiments verify the effectiveness of our proposal.

Preemptive scheduling efficiently addresses the coexistence of enhanced Mobile Broad Band (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC). While URLLC puncturing influences eMBB performance, further investigation is necessary to study the trade-offs between stability, delay, and efficiency. However, existing studies overlook the imbalance in eMBB/URLLC load distribution and personalized fluctuations in eMBB performance, leading to sub-optimal results. To tackle this, we propose an unmanned aerial vehicle (UAV) relay-assisted eMBB/URLLC multiplexing framework. Specifically, considering the utilization of UAVs for connecting separated next-generation Node Bs (gNBs) and the individual subject experience of services, we first formulate the multiplexing problem as an optimization problem. The objective is to maximize eMBB throughput and minimize personalized fluctuations in eMBB performance and UAV consumption, subject to URLLC constraints. Then, the challenging problem is decomposed into the eMBB problem and the URLLC problem. For the former, we further decompose it into three sub-problems and solve them using optimization methods. For the latter, we propose a deep reinforcement learning-based algorithm to obtain an optimal strategy for relaying and puncturing URLLC into eMBB intelligently. Simulation results demonstrate that our proposals outperform benchmark schemes regarding eMBB throughput, UAV consumption, eMBB performance fluctuation, URLLC satisfaction, and learning efficiency.

As the number of connected and autonomous vehicles (CAVs) on road networks continues to increase, mixed transportation scenarios where CAVs and human-driven vehicles (HDVs) coexist are becoming more common. Establishing dedicated lanes (DLs) for CAVs is crucial for managing mixed traffic and improving road capacity. In this paper, we provide a theoretical analysis of the relationship between the market penetration rate (MPR) of CAVs and road capacity in both single-lane scenarios and multiple-lane scenarios with DLs. We derive a critical MPR for CAVs, at which they can be seamlessly accommodated within the DLs. Our numerical results show that CAVs should be prioritized to enter DLs first to optimize road capacity in mixed traffic. We also derive and validate the road capacity in multiple-lane scenarios and provide an optimal strategy for setting up DLs under varying MPRs to maximize road capacity. Overall, our study provides valuable insights into the significance of DLs for CAVs in mixed traffic and offers guidance on their implementation to improve road capacity.

Urban air mobility (UAM) provides a new solution to relieve urban transportation pressure by expanding transportation resources of near-ground space. The vigorous development of emerging technologies such as artificial intelligence, intelligent transportation, and sixth-generation (6G) communication technologies have greatly promoted the progress of UAM. However, UAM also increases traffic safety hazards while introducing vertical dimension transportation resources. Traditional collision avoidance is not suitable for three-dimensional (3-D) air-ground integrated mobility scenario, which considers safety hazards in vertical dimensions as well as the resource supply and demand conflict due to the combined effect of directional antenna angle and limited communication distance. Therefore, a safety-oriented on-demand resource allocation strategy for air-ground integrated mobility is proposed. Specifically, we first model the 3-D safety distance model in the air-ground integrated mobility scenario and construct its quantitative relationship with communication and computing resources. Secondly, a 3-D safety distance optimization model is proposed with joint consideration of safety-oriented resource requirements and resource distribution, which can allocate resources in the scenario. Furthermore, a 3-D safety distance optimization algorithm based on deep reinforcement learning (DRL) is designed for solving the optimization model, which implements a safety-oriented resource allocation. Simulation results show that the proposed safety control strategy can effectively improve the safety of air-ground integrated mobility and alleviate the contradiction between the supply and demand of resources.

To investigate the diversified applications in vehicular networks, artificial intelligence, intelligent edge computing, and vehicular networks are combined. By offloading computation tasks to devices close to vehicles, Vehicular Edge Computing (VEC) has emerged as a new computing paradigm to tackle the problem. Most existing VEC methods simply slice the application into subtasks for offloading purposes without considering the dependencies between subtasks. In practice, the dependency information is critical to the efficiency of offloading strategies. If a subtask requires the computation result of another subtask, the latter has to be processed before the former is finished. In this paper, we propose a deep reinforcement learning based offloading strategy for multi-vehicle collaboration VEC, with task dependency taken into account. With the proposed strategy, we formulate the offloading problem as an Markov Decision Process (MDP) and use the Sequence-to-Sequence (S2S) neural network to represent the policy/value function of the MDP. Furthermore, we train the S2S neural network to obtain the appropriate offloading policy using the Proximal Policy Optimization (PPO) technique. Our simulation results indicate that, by considering task dependencies during offloading, the proposed strategy outperforms existing methods in effectively reducing task offloading latencies.

The fifth-generation network (5G) has made great progress. With the continuous development of communication technology, by analyzing the characteristics of 5G scenarios, the sixth-generation network (6G) technology combined with multiple scenarios provides effective solutions for the implementation of emerging services with stringent requirements. It is worth noting that the vigorous development of emerging services has been weakened due to the limited resources provided by a single scenario, cross-scenario technologies are urgently needed to enable emerging services in the 6G stage. However, most of the existing work only focuses on a single scenario, which leads to emerging services with complex requirements still difficult to achieve in practice. Therefore, we propose an efficient representation and scheduling framework to achieve the unification of cross-scenario resources, aiming to solve the problem of resource scheduling in cross-scenario. In the above framework, first of all, considering the strict resource requirements of emerging services, we establish a unified resource representation model based on the Time-Expanded Graph (TEG). Secondly, to maximize resource utilization, based on the representation model, a cross-scenario resource scheduling model is proposed. Then, considering the complexity of solving the scheduling model, a resource utilization maximization strategy is presented through the primal decomposition. Simulation results show that the unified framework can effectively improve resource allocation efficiency in complex 6G scenarios.

The ever-growing Internet of Things (IoT) provides a powerful means for complex and changeable environmental monitoring. Directional sensor networks (DSNs), as a typical architecture of IoT, can efficiently facilitate various digital and intelligent IoT applications. In the DSNs, due to the asymmetry in coverage focus and diversity in detection angle of the directional IoT sensors, how to enhance the coverage performance with the limited sensors becomes a new challenge. To this end, we develop a novel sensor redeployment scheme based on the minimum exposure path (MEP) to optimize the coverage performance of the DSNs. Specifically, we first propose a minimum exposure path searching algorithm based on the particle swarm optimization (MEP-PSO) algorithm with the target of obtaining the MEP in the DSNs. With this algorithm, the traditional MEP problem can be analyzed and simplified by conducting the grid discretization and building the weighted undirected graph. Then, an MEP-based coverage optimization (MEP-CO) algorithm is proposed to determine the optimal deployment locations and the dispatch sensors so that the IoT sensors can be dynamically redeployed to achieve the coverage optimization. After that, we derive the formula for the coverage upper bound (CUB) and develop a CUB algorithm to provide a benchmark for evaluating the effectiveness of different coverage optimization algorithms. Simulation results demonstrate that the proposed coverage optimization scheme can significantly promote the minimum exposure value (MEV) and coverage ratio of the monitoring area compared with the existing algorithms.

The dissemination of traffic incident information (TII) will greatly help to decrease fuel consumption and congestion under future Internet of Vehicles (IoV) environments. Compared with the current semitargeted dissemination strategies of TII that focus on communication performance, we propose a complete targeted-dissemination strategy by jointly considering the impact of the dissemination on the route planning of connected vehicles and the communication performance of information dissemination in the IoV environment. This strategy further alleviates the considerable challenges caused by the increasing number of vehicles and limited radio resources in dissemination, while reducing the additional fuel consumption caused by excessive and meaningless dissemination by selectively distributing traffic information to connected vehicles. Specifically, the proposed strategy consists of a radio resource allocation strategy guaranteeing communication quality and a traffic-influencing targeted dissemination strategy selecting the targets. Simulation results validate the effectiveness of the proposed strategy in ensuring communication performance, reducing total cost, and decreasing the carbon dioxide emission rate.

With the development of autonomous driving technology, it is foreseeable that connected and automated vehicles (CAVs) will be fully popularized in people’s lives. During this process, transportation systems are expected to evolve into the era of mixed autonomy, where CAVs and human-driven vehicles (HDVs) coexist in road networks and share available road resources. To materialize the much-anticipated potential of CAVs, a thorough understanding of CAVs’ effects on transportation systems is indispensable. On the one hand, attributing to advanced sensing, communication, and computation capabilities, CAVs provide opportunities to enhance mixed traffic safety, improve energy savings and suppress shockwave spread. On the other hand, due to advantages in large-scale information and cloud-computing resources, CAVs have the ability to occupy more road resources compared with HDVs, resulting in a reduction in the travel efficiency of HDVs, and even of the entire transportation systems. In this article, by clarifying the key differences between HDVs and CAVs, we comprehensively review the potential impacts of CAVs when they are appearing on road networks coexisting with HDVs. It can be regarded as the first-of-its-kind paper that systematically overviews the impacts of CAVs in the era of mixed autonomy on both positive and negative emotions. Specifically, the main focuses of this article are: 1) what are the key differences between CAVs and HDVs? 2) what are the positive impacts of CAVs’ appearance on mixed traffic systems? 3) will the introduction of CAVs cause some negative effects simultaneously? and 4) what kinds of strategies should be employed to relieve these negative effects? Hopefully, this article can not only call for an objective attitude toward the introduction of CAVs, but also provide foresighted advice to address possible challenges during the popularization of CAVs, so as to create a cooperative, safe, and efficient mixed traffic ecosystem.

Traffic incident management in metropolitan areas is crucial for the recovery of road systems from accidents as well as the mobility and safety of the community. With the continuous improvement in computation and communication technologies, connected and automated vehicles (CAVs) exhibit the potential to relieve incident-induced traffic degradation. To understand the benefits of CAVs on traffic incidents, this paper models the impacts of CAVs with joint consideration of microscopic CAV driving behaviors and macroscopic traffic assignment in mixed traffic environment comprising both CAVs and human-driven vehicles (HDVs). Firstly, a generic traffic assignment model with mixed traffic is proposed to analyze the mixed traffic process from the macroscopic perspective. Then, we incorporate the traffic assignment model with bottleneck delays and incident effects from the microscopic perspective, to model the dynamic road system with incident effects in mixed traffic environment. Furthermore, cooperating with the mixed traffic assignment model, dynamic signal control policies are presented according to different incident severities, and the conditions for equilibrium existence, uniqueness and stability of the road system are derived. The analytical results indicate that road system stability with incident effects is closely related to the incident severity, signal control policy as well as penetration rate and spatial distribution of CAVs. Finally, simulation results are conducted to demonstrate the effectiveness of our proposed incident management policy in improving the recovery rate and system stability of road networks.

The occurrence of traffic accidents in cities is often accompanied by property losses, environmental pollution, casualties, and congestion. Predicting the spatio-temporal range of accident-induced congestion can mitigate the negative effects by taking appropriate measures to respond to traffic accidents in a timely manner. Unlike most existing traffic accident spatial-temporal prediction strategies that depend on existing traffic models, this paper proposes a model-free method by using the macroscopic road network images, which relieves the restriction of precise modeling of traffic dynamics and the detailed traffic data. Specifically, we first design a digital twin road network to observe the traffic operation from a macro perspective. Then, after designing the structure of the Convolutional LSTM (Conv-LSTM) cell, we stack multiple Conv-LSTM layers to form an encoding-decoding structure to predict spatio-temporal congestion caused by accidents in urban road networks. Finally, the simulation results indicate that the proposed method improves the prediction accuracy compared with the model-based method and the LSTM network model. The proposed strategy provides a new approach to predict the spatio-temporal congestion caused by accidents from a macroscopic perspective.

Autonomous driving has attracted huge research interest from both academia and industry. As one of the key components for the safe driving of autonomous vehicles, lane detection allows vehicles to correctly locate itself in the lane and follow the traffic rules. Unlike traditional detection methods that rely on the extraction of professional and hand-designed features, this paper proposes a robust lane detection method by mining semantic information via the deep learning model LaneNet, which can cope with more complex road scenes, and relieve the restriction of deep learning models that detect a fixed number of lanes. Specifically, we first utilize the LaneNet to segment lane pixels from the road scene. Then we distinguish different lane instances by using a clustering loss function based on the distance vector of lane pixels. Finally, we verify our method on two datasets, Tusimple and CULane. The results show that the detection accuracy of Tusimple is up to 94.3%, CULane’s normal level is 90.4% and other more complex levels can reach up to 70%. Furthermore, by comparing with existing approaches, simulation results confirm the robustness of the proposed method in lane detection. In addition, we combine lane detection with driving decision based on intelligent driving simulation platform PanoSim5, which illustrates the effectiveness of our proposed lane detection method for autonomous driving.

Random access systems are potential for Internet of Things in the future wireless communication network for its operational simplicity. Irregular repetition slotted ALOHA (IRSA) system is one of the high-efficiency random access systems. In this paper, performance analysis of the irregular repetition slotted ALOHA systems with short-packet, i.e. finite-blocklength, transmission for the quasi-static Rayleigh fading channel is given. A cumulative distribution function of signal-to-interference power ratio (SIR) is derived and thus a closed-form expression of an average packet error probability (PEP) at the SIR with short packets for the Rayleigh fading channel is given. The closed-form expression makes it possible to optimize the degree distributions at a specific blocklength in the sense that the systems give the maximum system load.

Identifying the root cause of congestion and taking appropriate strategies to improve traffic network performance are important goals of Advanced Traffic Management Systems (ATMS). On many occasions, the causes of congestion are not necessarily attributable to road infrastructures themselves. Instead, signal control strategies at intersections are very often the major contributors of congestion. In lieu of this, in this paper, a root cause identification method is developed with consideration of the impact from both road infrastructure and traffic signal control. Firstly, we differentiate congestion effects between road segments and intersections to attribute the causes of congestion to road infrastructure and signal control respectively. Then, we construct causal congestion trees to model congestion propagation and quantify congestion costs for each road segment and intersection in the whole road network. A Markov model is utilized to capture congestion spatio-temporal correlation among multiple road segments and intersections simultaneously, with which the most critical root cause can be located. Furthermore, a gradient boosting decision tree based method is presented to predict the root cause of congestion according to traffic flows, signal control strategies and road topology in traffic networks. Finally, simulations based on Simulation of Urban Mobility (SUMO) validate the effectiveness of our proposed method in identifying and predicting the congestion root cause. Experiments are further conducted using inductive loop detector data to identify the root cause for the road network of Taipei.

Traffic incidents are recognized as a key contributor to non-recurrent congestion, which causes many negative effects in economy, environment, health and lifestyle. In this article, we investigate an incident management policy considering both signal control and route choice, which presents a real-time systematic effort to provide a rapid recovery from an incident and mitigate incident-related congestion according to different incident effects. Firstly, we introduce a route choice method on a multiple-route urban road network with consideration of bottleneck delays. Then, we analyze the route travel costs under incident effects and give the equilibrium existence condition after the occurrence of an incident. Furthermore, combining with the route choice method, a novel traffic signal control policy is proposed and the condition for equilibrium existence is given with the consideration of dynamic signal control and route choice simultaneously. Sufficient conditions for the dynamic road system to be stable are also derived and validated by using Lyapunov stability theorem. The analytical results indicate that opposite signal control policies should be applied in road networks under different incident circumstances and the proposed control policy can achieve the improved recovery rate and system stability than existing control policies in terms of dynamic incident effects in road networks. Finally, numerical results have been conducted to demonstrate the effectiveness of our proposed incident control policy and confirm the conditions for road system stability when different incident circumstances had been identified.

By driving vehicles in constant spacing, platooning is a promising way to enable a safer and faster mobility with higher lane capacity and energy efficiency. On one hand, recent advances in vehicular communication technologies improve the usefulness of platooning. On the other hand, the string instability in platooning is easily created by the unavoidable communication delays. In this paper, we focus on improving the performance of intraplatoon communications by considering the impact of non-line-of-sight (NLOS), which is typically isolated in most of present works. To do that, we first evaluate the impact of NLOS due to the vehicles as obstacles on signal attenuation among platoon members by developing an analytical mode based on the knife-edge model. To obtain the optimal communication performance, an power control problem is formulated by considering NLOS and multi-user interference. By resorting to the graph neural network (GNN), which can achieve an excellent performance in learning dynamic graph characteristics, an efficient power control policy is developed after modeling the inter-vehicle communication links in the platoon as a fully connected interference graph. Extensive simulations finally validate the performance of our method.

The travel delay caused by incidents severely reduces the efficiency of traffic. This symptom has been relieved with the development of advanced communication technologies. However, the emergency traffic information (ETI) is meaningless for vehicles which do not traverse the incident segment. Unlike most existing studies concentrating on the network performance during the dissemination of ETI to all vehicles, this paper proposes a joint traffic-communication optimization strategy (JTCS) to reduce the extra cost caused by unnecessary communication, which minimizes the total communication and traffic cost by transmitting the ETI to the worthy vehicles who need the ETI. Specifically, we capture the optimal targeted ETI transmission strategy with combination of radio resource allocation strategy (RRAS) and traffic-influencing transmission strategy (TTS), which can be converted into a bi-level optimization problem. The lower-level problem minimizes the total cost by Lagrangian method and obtains the optimal RRAS when the TTS is given. The upper-level problem develops the optimal TTS based on the optimal RRAS obtained in the lower-level problem. Simulation results using SUMO and MATLAB indicate that JTCS can achieve minimal total cost of ETI transmission and vehicle rerouting by comparing with existing approaches.

Identifying the root cause in urban road networks and ranking the influential factors can benefit traffic management for improving traffic condition. Traditional congestion identification studies paid attention to identify traffic bottlenecks, namely the most vulnerable points in a road network, without consideration of root causes that leading to the congestion. In this paper, we propose a gradient boosting decision trees (GBDTs) based method to identify the root cause of road network congestion and rank the influential factors using different types of explanatory variables. Based on Sioux Falls network, different signal control strategies at intersections and number of lanes on road segments under different traffic flows are conducted as samples using Simulation of Urban Mobility (SUMO) to train and test the GBDT model. Simulation results indicate that the GBDT model can achieve superior performance in average travel speed prediction and identify the root causes of congestion by prioritizing the relative importance of influential factors, such as lane numbers and signal control strategies, compared with other algorithms.

In this paper, we investigate a hybrid dynamical system which incorporates flow swap process, green-time proportion swap process, and flow divergence for a general network with multiple Origin-Destination (OD) pairs and multiple routes, where flow swap process is specified in which traffic swaps from more costly to less costly input links, green-time proportion swap process is specified in which green time at each intersection swaps from less pressurized stages to more pressurized stages, flow may diverge at each intersection from one OD pair to other OD pairs. Unlike the dynamical system model, where bottleneck delays need to be intentionally constructed to yield the equilibrium flow vector and green-time proportion vector, we propose a novel control policy to fill the gap by only adjusting the green-time proportion vector. We derive a sufficient condition for the existence of equilibrium of the dynamical system under the mild constraints that 1) the travel cost function and stage pressure function should be continuous functions and 2) the flow and green-time proportion swap processes project all flow and green-time proportion vectors on the boundary of the feasible region onto itself. We derive the condition of unique equilibrium for fixed green-time proportion vector and show that with varying green-time proportion vector, the set of equilibria is a compact, non-convex set, and with the same partial derivative of travel cost function with respect to the flow and green-time proportion vectors. Finally, we prove the stability of the proposed dynamical system by using Lyapunov stability analysis.

Real-time and accurate prediction about current and future traffic conditions is one of the effective ways to alleviate traffic problems. However, missing data problem is inevitable for various reasons when obtaining real-time traffic flow information. Incomplete traffic information may seriously affect the prediction accuracy. To address this problem, in this paper, we propose a method that predicts the traffic flow in real time under missing data. Considering the spatio-temporal characteristics of traffic flows and the spatial location of road segments, we first evaluate the importance of traffic flows using a spatio-temporal correlation function to analyze the correlations of traffic flows. Then, we present a PPCA-based minimum data imputation optimization (P-MDIO) algorithm to reduce computation time of data imputation. Finally, we utilize the complete traffic data and relevant road segments sequences to predict real-time traffic flows. The experimental data are obtained from the real-time traffic data collected by loop detectors in Taipei, Taiwan. Our experimental results show the performance and validity of the proposed approach, particularly in large-scale prediction.

Traffic congestion has seriously caused various problems in society, economy and environment, especially in urban areas. A traffic bottleneck is always seen as the root cause of congestion which frequently deduces the congestion emergence, queues formation and congestion propagation. However, bottlenecks are caused by many complicated factors and vary with spatial and temporal environment which are difficult to be defined and identified in urban areas. In this paper, we first propose a novel definition of bottlenecks in urban area based on the congestion propagation costs and the congestion weights of road segments. Then according to the definition, we present an urban bottleneck identification method using causal congestion trees and causal congestion graphs to identify some bottlenecks. This paper implements some experiments based on the urban inductive loop detector data. According to our proposed method, we identify several bottleneck groups around the urban area. Furthermore, we also improve the road capacity of identified bottlenecks and compare the congestion level and congestion propagation range before and after the improvement to verify the identified bottlenecks.

Electric Vehicles (EVs) is a promising transportation to alleviate traffic congestion and pollution problems and its route planning can save the limited energy. However, designing a reliable route planning strategy to achieve optimal route remains a challenging problem, especially when various objectives of both energy consumption and time cost are taken into consideration. Therefore, based on the traffic signal control technologies in urban areas and an EV energy consumption model, we formulate EV route planning problem as a multi-objective optimization problem and propose a Green wave band-based Multi-objective Ant Colony Optimization (G-MACO) algorithm to solve it. Moreover, relying on the field measurement data, the graphical model of urban road network containing relevant weights is also analyzed. Finally, simulation results show that the proposed algorithm can achieve a trade-off between energy consumption and time cost to realize the multi-objective optimization route planning.

Wireless Body Area Networks (WBAN) are becoming increasingly important for health care with the development of health consciousness and health detection requirements. A lot of researches have been devoted to the progress of WBAN, for example, the improvement of protocols designed for WBAN, the optimization of parameters and the system performance evaluation. However, the main premise of all the directions above is to provide an efficient and reliable simulation platform, since the existing platforms cannot respond to accord with the reality with high performance. To work this issue out, we construct the WBAN simulation platform on Network Simulation 3 (NS-3) which specifies in good expandability and resources saving and agrees with real networks in many aspects comparing with the other simulators. In this paper, we propose our implementation of WBAN module based on IEEE 802.15.6 standard. Our simulation platform consists of Medium Access Control (MAC) and Physical (PHY) layer of IEEE 802.15.6. Then in order to make the simulation more objective, we design a proper simulation scenario according to the practical application and analyze the simulation results. Finally, we compare the throughput saturation threshold on NS-2 and NS-3 with the analysis results respectively to indicate the superiorities of NS-3 and verify the validity and effectiveness of our WBAN module.