This paper addresses the impact of asymmetric jitter, which arises from air turbulence or mechanical vibrations, on the three dimensional (3D) attitude angles of aerial intelligent reflecting surface (AIRS) mounted on unmanned aerial vehicle (UAV). To characterize these effects, a physics-based channel model based on the spherical wavefront assumption (SWA) is established. To mitigate the resulting performance degradation, we propose a novel continuous reflection phase based on the planar wavefront assumption (PWA), leveraging the concept of the Zadoff-Chu sequence. This design integrates a conventional reflection phase component with a spatial-frequency-bandwidth-dependent term, effectively broadening the bandwidth of the passive beam. Using the proposed continuous reflection phase, we analyze the normalized array gain function and average received signal power under UAV jitter, deriving approximate expressions for these metrics using Fresnel functions. The analysis demonstrates that the proposed reflection phase can expand the beam bandwidth to cover the potential range of jitter angles. Furthermore, a discrete reflection phase is designed based on the continuous version. Numerical results confirm that the beam bandwidth remains stable as the severity of asymmetric jitter increases, illustrating the effectiveness of the designed phases in mitigating UAV platform instability. Additionally, the results indicate that the proposed reflection phase can effectively compensate for performance degradation caused by UAV jitter.

High-Density Parking (HDP) offers an alternative solution to enhance urban space utilization by increasing parking density through modifications to existing parking facilities. Current HDP dispatch strategies primarily rely on heuristic methods, the adaptability of which to diverse parking scenarios has not been adequately evaluated. 7 distinct parking demand patterns are identified with different entry/exit distribution from real-world parking data to create a comprehensive evaluation framework. A novel Dueling Deep Q-Network (Dueling DQN) approach is proposed, which effectively captures the complex spatial-temporal relationships in HDP environments through specialized state representation. Extensive experiments evaluated the adaptability of existing heuristic and the proposed approaches under diverse parking scenarios with different parking patterns, stack depths, supply-demand ratios, and departure time estimation errors. The Dueling DQN model performs superiorly and exhibits exceptional reusability, maintaining superior performance without retraining when deployed across different parking scenarios. This research provides practical insights for implementing efficient HDP dispatch strategies in real-world settings.

Traffic state estimation (TSE) lays the foundation for freeway traffic monitoring and control. This paper develops a novel freeway traffic state estimator based on physics-informed deep learning (PIDL), whereby the deep-learning neural network is guided with physical laws of traffic. The innovative features of this work are as follows. In the data-driven aspect, the representation capability of neural networks is enhanced by introducing a nonlinear expansion layer, an attention mechanism, and a point-weighting mechanism. In traffic flow modeling aspect, a neural network is designed for the adaptive identification of model parameters. Additionally, a ramp flow self-learning neural network is constructed in consideration of system observability. This study employs four macroscopic traffic flow models LWR, ARZ, JWZ and PW to design PIDL-based traffic state estimators. Field-data evaluations and comparative analyses are conducted with respect to an urban expressway of 17.5 km. The results show that the designed PIDL traffic state estimators are able to simultaneously achieve traffic state estimation, ramp flow estimation, and adaptive identification of model parameters. The PIDL estimators outperform a number of baseline estimators, and are least sensitive to the number of input sensors and missing data. It is also discovered that the effectiveness of PIDL hinges on the appropriate tuning of the imbedded physical model as well as accurate boundary condition inputs, and failure to meet these requirements may lead to performance degradation.

Reconfigurable intelligent surface (RIS) has the potential to shape the wireless channel into an intelligent programmable wireless propagation environment. RIS-assisted millimeter wave (mmWave) technology is considered as a potential technology for sixth generation (6G) wireless communications. In this article, an RIS-assisted mmWave system is considered, and the corresponding physics-based channel model under the parabolic wavefront assumption, which is a second-order approximation to the spherical wavefront assumption, is established. Based on the parabolic wavefront assumption, the approximate closed-form expression of the direction-dependent Rayleigh distance is derived, which is a supplement to the classical Rayleigh distance. Also, the RIS reflection phase, consisting of a conventional far-field reflection phase and an addition near-field reflection phase, is obtained. The far-field phase compensates the phase variations from the mismatch in the azimuth and elevation angles, and the near-field phase compensates the phase variations caused by the distance differences from the transmitter/receiver to different RIS unit cells. Based on the conventional far-field reflection phase and the designed reflection phase, the received signal power is explored, and the approximate expressions are also obtained by using the Fresnel functions, which are validated by using numerical results. In addition, the numerical results show that the mmWave channel model under parabolic wavefront assumption and the corresponding near-field reflection phases are necessary to explore the RIS-assisted mmWave communication systems.

Time-Sensitive Networking (TSN) employs shaping mechanisms such as Time-Aware Shaping (TAS) and Cyclic Queuing and Forwarding (CQF), which depend heavily on precise time synchronization and complex Gate Control Lists (GCL) configurations, limiting their effectiveness in large-scale mixed traffic networks like those in vehicular systems. In response, IEEE 802.1Qcr protocol introduces the Asynchronous Traffic Shaping (ATS) mechanism, based on Urgency-Based Schedulers (UBS), to asynchronously address diverse traffic needs and ensure low and predictable latency. Nonetheless, no traffic scheduling algorithm exists that can be directly applied to ATS shapers in generic large-scale traffic scenarios to solve for fixed end-to-end (E2E) delay constraints and the number of priority queues.In this paper, we propose an urgency-based fast flow scheduling algorithm (UBFS) to address the issue. UBFS leverages domain-specific optimizing strategies with a focus on traffic delay urgency inspired by greedy algorithm for priority allocation across hops and flows, complemented by preprocessing for scenario solvability and dynamic verification to ensure scheduling feasibility. We benchmark UBFS against the method with both scalability and solution quality in typical network topology and demonstrate that UBFS achieves more rapid scheduling within seconds across linear, ring, and star topologies. Notably, UBFS significantly outperforms the baseline algorithm in scheduling efficiency in mixed and large-scale traffic environments, scheduling a larger number of flows. UBFS also reduces time costs by 2-10 times in delay-sensitive environments and by more than 10 times in large-scale scenarios, effectively balancing time efficiency, performance and scalability, thereby enhancing its applicability in real-world industrial settings.

The connected automated vehicles (CAVs) are envisioned to be implemented most likely on electric vehicles, while traditional fuel-powered manually-driven vehicles (MVs) would probably still dominate the automobile market in the next decade. In this context, this paper addresses urban eco-driving of CAVs in mixed traffic and heterogeneous power conditions. The paper aims to develop a practical and deployable eco-driving strategy for CAVs in mixed traffic flow of CAVs and MVs under realistic and complex traffic conditions. Several typical eco-driving scenarios were studied in detail. In a nutshell, the eco-driving strategy for each CAV was determined by solving a typical two-point boundary value problem with minimum electric energy consumption in urban traffic conditions with small market penetration rates (MPRs) of CAVs. A rolling-horizon scheme was applied to implement the eco-driving strategy to handle uncertain/unpredictable disturbances of preceding MVs and the interference of junction queues to the eco-driving maneuvers of CAVs. The paper also studied how eco-driving for electrified CAVs would affect MVs’ fuel consumptions. Simulation studies were carried out on urban arterial roads of multiple signalized intersections in various scenarios of demand and MPR to verify the energy savings effect of the proposed eco-driving strategy. The results showed that via eco-driving electrified CAVs each had a potential of reducing energy consumption by 40%-61%, meanwhile leading to 5%-34% fuel savings on average for each following MV. Further issues concerning the energy saving mechanism of electrified CAVs, impacts of MVs cut-in from adjacent lanes, and passenger comfort were also examined.

The dynamic spatiotemporal characteristics of queues at urban intersections are crucial to traffic operation tasks such as signal performance measure and signal optimization. This paper addresses the high time-resolution estimation of queue profile at urban signalized intersection using Extended Kalman Filtering (EKF) with data of connected vehicles (CVs). The main features of this work are as follows: (i) a machine learning method was applied to construct a dynamic shockwave propagation model based on shockwave theory and historical data of CVs; (ii) a heuristic approach was proposed to measure the shockwave speed for use in EKF; (iii) an urban queue estimator was designed to combine the dynamic shockwave propagation model and real-time shockwave information via EKF to deliver second-by-second queue profile estimates. The queue estimator does not require any priori information about vehicle arrival patterns and the market penetration rate (MPR) of CVs. The performance and robustness of the queue estimator were evaluated using both simulation and real-world CV data. The results show that the method can provide satisfactory queue estimation results at various MPR levels of CVs, with the estimation error of 2.5 vehicles at the MPR of 5%, and of 0.5 vehicle at the MPR of 40%.

Capacity drop at congested freeway bottlenecks is well known with a lot of field observations. This paper studies coordinated ramp metering (RM) and mainstream traffic flow control (MTFC) as well as their integration (RM+MTFC) for freeway traffic, with particular attention to effects of capacity drop on the performance of traffic control measures. Via mathematical analysis and comprehensive simulation studies under an optimal control framework, the work has revealed a capacity-drop-related mechanism that MTFC and ramp metering are based on to take effects, and obtained a number of important conclusions: (1) applications of any control measure (RM, MTFC, or RM+MTFC) in freeways are justified by the existence of capacity drop in field; (2) an appropriate usage of the control measures can effectively prevent the activation of potential bottlenecks on freeways and hence avoid capacity drop; (3) any control measure is beneficial for a large majority of the driver population in a freeway network if it can manage to increase the accumulated total network exit flow; (4) it is a common misconception that ramp metering would simply transfer traffic loads from the freeway mainstream to on-ramps. The work has also highlighted the strengths, weaknesses, and applicability of ramp metering and MTFC.

This paper investigates variable speed limit (VSL) systems, trying to optimize the system designs when the variable message signs (VMSs) are movable. The optimization problem is formulated as a large mixed-integer nonlinear programming problem, whose decision variables include the number of VMSs to be deployed, the locations of the VMSs, and the speed limits posted on the VMSs. Two objectives are considered, one is to smooth the flow propagation, and the other is to minimize the environmental impact of freeway traffic. Moreover, a genetic algorithm is proposed to solve the complex problem. Numerical examples performed on a real freeway segment show that VSL can effectively achieve smooth flow and reduce the environmental impact of freeway traffic.