One of the key reasons why the road capacity of urban roads in China often fails to meet the designed capacity is the mixture of heavy vehicles (slow-moving) and light vehicles (fast-moving). This paper presents a two-lane cellular automaton model suitable for simulating urban road traffic that includes intersections, based on the NaSch model. The model comprehensively takes into account multiple key factors, such as vehicle safety distance, speed differences between adjacent vehicles, the acceleration and deceleration performance of different types of vehicles, and driver reaction time, enabling it to more realistically reproduce the complex characteristics of mixed traffic flows on urban roads. The paper investigates and analyzes the influence of traffic flow density and the proportion of heavy vehicles on the moving bottleneck effect in urban roads from several aspects, including space–time evolution diagrams, traffic flow, average speed, and lane-changing rates. The results indicate that the model established in this paper successfully simulates the characteristics of mixed traffic flows on urban roads, and the simulation results align with actual traffic conditions, achieving the expected simulation effects. Before the traffic volume reaches saturation, the higher the proportion of heavy vehicles on urban roads, the stronger the moving bottleneck effect. This confirms the necessity of conducting research on the phenomenon of moving bottlenecks and the mechanisms of traffic impacts in urban roads, providing a scientific basis for formulating effective traffic dispersion measures and alleviating traffic congestion in the future. This research possesses significant practical meaning and value.

With the rapid development of intelligent transportation systems and information technology, the security of road traffic signal systems has increasingly attracted the attention of managers and researchers. This paper proposes a new method for detecting attacks on traffic signal systems based on game theory and Generative Adversarial Networks (GAN). First, a game theory model was used to analyze the strategic game between the attacker and the defender, revealing the diversity and complexity of potential attacks. A Bayesian game model was employed to calculate and analyze the attacker’s choice of position. Then, leveraging the advantages of GAN, an adversarial training framework was designed. This framework can effectively generate attack samples and enhance the robustness of the detection model. Using empirical research, we simulated the mapping of real traffic data, road network data, and network attack data into a simulation environment to validate the effectiveness of this method. In a comparative experiment, we contrasted the method proposed in this paper with the traditional Support Vector Machine (SVM) algorithm, demonstrating that the model presented here can achieve efficient detection and recognition across various attack scenarios, with significantly better recall and F1 scores compared to traditional methods. Finally, this paper also discusses the application prospects of this method and its potential value in future intelligent transportation systems.

Identifying road markings is a very important part of the vehicle environment sensing system and plays a crucial role in a vehicle’s correct understanding of a current traffic situation. However, road traffic markings are interfered with by a variety of factors, such as being obscured and the viewpoint of the vehicle sensors, resulting in large errors in the existing detection methods. In order to make the target detection task applicable to irregular objects or to detection tasks with higher accuracy requirements while reducing the waste of computational resources, this paper improves the accuracy of traffic marking segmentation detection by designing a multi-type traffic marking segmentation detection model based on image segmentation algorithms and designing a segmentation guidance matrix module based on a rank guidance matrix computation method. By constructing a comprehensive traffic marking detection model, a unified road traffic marking detection is achieved. Finally, the new traffic marking datasets ApolloScape-Precise and ApolloScape-Large are constructed based on the existing ApolloScape dataset, and experimental validation is carried out on these two datasets. The results show that the index MIoU (Mean Intersection over Union) of traffic marking segmentation detection reaches 61.44% and 70.15%, thus achieving a more perfect road traffic marking detection and right-of-way information perception and proving the effectiveness of the integrated traffic marking detection method designed in this paper.

Ensuring dynamic risk management for intelligent connected vehicles (ICVs) in complex urban environments is critical as autonomous driving technology advances. This study presents three key contributions: (1) a comprehensive risk indicator system, constructed using entropy-based weighting, extracts 13-dimensional data on abnormal behaviors (e.g., speed, acceleration, position) to enhance safety and efficiency; (2) a multidimensional risk quantification method, simulated under single-vehicle and platooning modes on a CARLA-SUMO co-simulation platform, achieved >98% accuracy; (3) a cloud takeover strategy for high-level autonomous vehicles, directly linking risk assessment to real-time control. Analysis of 56,117 risk data points shows a 32% reduction in safety risks during simulations. These contributions provide methodological innovations and substantial data support for ICV field testing.

Low-altitude UAV scheduling and planning has become a critical technological pillar in disaster response systems; however, systemic challenges in complex environments and under uncertain risk conditions remain insufficiently understood. Although substantial progress has been achieved in model formulation and algorithm design in recent years, scheduling and planning frameworks still lack a systematic representation of key risk factors, such as meteorological disturbances, terrain damage, and communication constraints, thereby undermining operational safety and decision reliability. This study conducts a systematic review of low-altitude UAV scheduling and planning research over the past decade, covering representative disaster scenarios including forest fires, large building fires, earthquakes, floods, major public health emergencies, and traffic accidents. By comparatively analyzing scheduling objectives and technical pathways across the pre-disaster, during-disaster, and post-disaster stages, this paper summarizes the dominant research paradigms and limitations of multi-UAV coordination, air–ground coordination, and risk reduction-oriented scheduling and planning. This review reveals that existing approaches generally lack explicit modeling of dynamic risks and uncertainties, highlighting an urgent need to incorporate risk-aware considerations and reliability analysis frameworks into scheduling and planning to enhance the overall robustness and decision credibility of UAV systems in disaster environments.