IET Intelligent Transport Systems最新文献

Conflicting driving behaviours between automated vehicles and manually driven vehicles may compromise driving safety. The aim of this study is to analyse the safety of mixed traffic on urban roads. The driving simulation tests were conducted using a multi-agent driving simulator, which allows real-time synchronization of multiple simulators. These data were further processed to derive the driving behaviour parameters of manually driven vehicles in VISSIM traffic simulations. Driving safety evaluation indicators included conflict-related indicators, as well as individual safety indicators. The safety evaluation indicators were normalized through min–max normalization, and the risk scores were summed to evaluate the urban roads. The analysis revealed that driving safety was poor at unsignalized intersections with a market penetration rate of 10% and 50% and at signalized intersections with traffic islands and a market penetration rate of 100%, where conflicts arise from the deceleration of leading vehicles and lane changes. This finding is about the driving behaviour of automated vehicles, which maintain a greater distance from the leading vehicle than manually driven vehicles, resulting in poorer driving safety due to lane changes rather than deceleration. Using the findings of this study, criteria for assessing the safety of mixed traffic situations in existing road infrastructures can be established.

Accurate demand forecasting has become increasingly necessary in the burgeoning field of free-floating micro-mobility systems. However, for model training, the service area must be divided into specific areal units, which often involves grid-based methods. Although these methods are feasible and provide a uniform area division, they are highly susceptible to the Modifiable Areal Unit Problem (MAUP), which is a critical issue in spatial data analysis. Although MAUP can adversely affect predictive model learning, studies addressing this issue are scarce. Therefore, a novel base areal unit generation algorithm is proposed that employs a clustering approach to enhance the prediction accuracy in free-floating micro-mobility system demand. The method identifies suitable base areal units by merging smaller ones while considering the similarities in temporal usage patterns and distances between different areas, mitigating the impact of MAUP during model learning. The approach was evaluated using shared e-scooter data from two cities, Kansas City and Minneapolis, and it was compared to the traditional grid method. The findings indicate that the proposed framework generally improves prediction performance within the newly defined areal units.

Autonomous driving is an exciting research field that has received growing attention in recent years. One of the most challenging and safety-critical driving situations is highway on-ramp merging. Most decision-making strategies that perform highway on-ramp merging are designed, firstly, to reduce the risk of crashes and improve the safety metrics. However, even with the development of such advanced driving systems, human drivers will still be involved in road traffic. Human drivers have various driving styles and different reactions to other traffic participants on the highway on-ramp. Understanding driver behaviors is essential for designing safe and efficient real-world driving strategies. Therefore, this paper provides a unique systematic review of existing techniques for modelling driver behaviors at highway on-ramps, which are critical locations for traffic safety and efficiency. The novelty of this review is that it proposes a new classification of current state-of-the art techniques. Each category of techniques involves a unique paradigm. For each category of approaches, fundamental concepts are examined together with their challenges and limitations, and an overview on practical implementation. Furthermore, and based on the classification and chronological order, current research trend is identified, i.e. “data-driven approaches”. Some future research avenues and disparities are also discussed.

As urbanization and transportation demands continue to increase, electric buses play an important role in sustainable urban development thanks to their advantages of emission reduction, noise and pollution reduction. However, electric buses still face some challenges, in which, range anxiety is one of the main factors limiting its popularization. To solve this problem, an accurate estimation method for the driving range of electric buses based on atomic orbital search (AOS) algorithm and back propagation neural network (BPNN) was used, in which a long-term bus operation dataset under different driving conditions is utilized to train BPNN, and then weight and bias are taken as the first generation provided for AOS approach to find a more appropriate parameter combination. Simulation and experimental analysis show that the algorithm introduced in this paper has higher prediction accuracy and efficiency compared to the traditional machine learning algorithms, that compared with BPNN, AOSBP reduced MAE, RMSE and MAPE by 85.6%, 50.9% and 64.6%, respectively, which effectively relieves range anxiety, and ensures the normal operation of the electric bus fleet.

The deployment of autonomous vehicles (AVs) in complex urban environments faces numerous challenges, especially at intersections where they coexist with human-driven vehicles (HVs), resulting in increased safety risks. In response, this study proposes an improved control strategy based on the Proximal Policy Optimization (PPO) algorithm, specifically designed for hybrid intersections, known as MSA-PPO. First, the Self-Attention Mechanism (SAM) is introduced into the algorithmic framework to quickly identify the surrounding vehicles with a greater impact on the ego vehicle from different perspectives, accelerating data processing and improving decision quality. Second, an invalid action masking mechanism is adopted to reduce the action space, ensuring actions are only selected from feasible sets, thereby enhancing decision efficiency. Finally, comparative and ablation experiments in hybrid intersection simulation environments of varying complexity are conducted to validate the algorithm's effectiveness. The results show that the improved algorithm converges faster, achieves higher decision accuracy, and demonstrates the highest speed levels during driving compared to other baseline algorithms.

Understanding the behaviour of traffic participants within the geo-spatial context of road/intersection topology is a vital prerequisite for any smart ITS application. This article presents a video-based traffic analysis and anomaly detection system covering the complete data processing pipeline, including sensor data acquisition, analysis, and digital twin reconstruction. The system solves the challenge of geo-spatial mapping of captured visual data onto the road/intersection topology by semantic analysis of aerial data. Additionally, the automated camera calibration component enables instant camera pose estimation to map traffic agents onto the road/intersection surface accurately. A novel aspect is approaching the anomaly detection problem by AI analysis of both the spatio-temporal visual clues and the geo-spatial trajectories for all type of traffic participants, such as pedestrians, bicyclists, and vehicles. This enables recognition of anomalies related to either traffic-rule violations, for example, jaywalking, improper turns, zig-zag driving, unlawful stops, or behavioural anomalies: littering, accidents, falling, vandalism, violence, infrastructure collapse etc. The method achieves leading anomaly detection results on benchmark datasets World Cup 2014, UCF-Crime, XD-Violence, and ShanghaiTech. All the obtained results are streamed and rendered in real-time by the developed TGX digital twin visualizer. The complete system has been deployed and validated on the roads of Helmond town in The Netherlands.

Common difficulties across industries are discovered in data management, where handling the volume, variety, and quality of data is crucial for informed decisions in uncertain environments. In this context, rail management must navigate complex decision-making to ensure safety, service continuity, and cost-effectiveness. The 2020 Stonehaven derailment is an example of the increasing vulnerability of rail infrastructure to environmental factors and systemic failures. It emphasizes the need for resilient systems, proficient at preventative maintenance and adaptable to escalating challenges. These matters further accentuate the need for context-dependent strategies that bridge theoretical insights and practical applications. This scoping review explores strategies for decision-making under uncertainty across sectors such as civil infrastructure, agriculture, water management, and emergency response. It unfolds a selection of procedures addressing the impacts of extreme weather and other unexpected disruptions. It also sets a foundation for future research to support rail infrastructure adaptation to climate change by advocating the use of cybernetic principles and artificial intelligence (AI) to enhance decision-making processes. Cybernetics enables collaborative human-AI methods, improving adaptability and resilience. However, balancing and incorporating diverse stakeholder viewpoints into decision chains remains difficult. While promising, substantial research and system improvements are needed to fully harness the potential of AI.

Bus rapid transit (BRT) system is a cost-effective way to provide public transportation service. However, it faces some challenges such as reduced labour productivity and increasing fuel costs. One solution is introducing automated vehicles (AV) to reduce operational expenses. However, there are still limitations on completely replacing human drivers even in limited operational design domains (ODD). Furthermore, AVs often suffer from poor driving stability in some roadways, such as abrupt changes in road geometry. To enhance the driving safety of AV-based BRT services, this study develops a new connected and automated bus (CAB) system using a cloud-based traffic management centre with cooperative intelligent transportation systems. The proposed system introduces risk-based maximum speed advisory system (RMSAS), which controls the maximum advisory speed of CAB to reduce its driving risk. This research evaluates the performance of RMSAS by comparing it to other driving modes, such as human-driven vehicles and conventional AVs, based on real-world field operational tests. The result shows that the proposed system outperforms other driving modes in terms of driving risks, particularly in some road geometry-related ODDs. Hence, this research concludes that the proposed system can be applied to the AV-based BRT service for uprating its safety performance.

This paper presents a lateral control scheme based on event-triggered model predictive control for trajectory tracking of autonomous vehicles. Firstly, the augmentation system is constructed based on the known road curvature information, and the model predictive controller is utilized to obtain the optimal control sequence. Then, an event-triggered mechanism is introduced to improve the real-time performance of the control system. The strategy targets to reduce the computational complexity and solving frequency of the optimization problem. In addition, a contraction constraint is structured using the backstepping control strategy to ensure the stability of the control system. Finally, experiments are conducted through the CarSim/Simulink joint simulation platform, and compared with the traditional model predictive control, the method proposed in this paper has better tracking accuracy and improves the real-time performance of the control system.

The use of unmanned aerial vehicles (UAVs) for smart and speedy logistics is still relatively nascent compared to traditional delivery methods. However, it is witnessing sporadic and steady growth due to booming demands, technological advancement, and regulatory support. The intelligence and integrity of UAV systems depend largely on the underlying cognitive and cybersecurity models, which serve as both eyes and brains to perceive and respond to the myriad of scenarios around them. Smart mobility and intelligent logistic ecosystems (SMiLE) are complex and advanced technological networks which are exposed to several issues. The incorporation of UAVs for priority logistics, thereby extending the coverage and capacity of SMiLE, further heightens these vulnerabilities and questions its security, safety, and sustainability. This review scrutinizes the significant security disruptions, smartness dynamics, and sundry developments for the sustainable deployment of UAVs as an aerial logistics-based vehicle. Using the PRISMA-SPIDER methodology, 157 articles were selected for quantitative analysis and 20 review articles for qualitative evaluation. Security and safety issues in UAVs cut across all the layers of logistics operations: components, communication, network architecture, navigation, supply chain etc. Expanding the capacity of SMiLE using UAV demands an intentional and incremental convergence-based integration of an agile explainable artificial framework for reliable and safety-conscious smart mobility, a scalable and tamperproof blockchain for multi-factor authentication, and a zero trust cybersecurity paradigm for inclusive enterprise-based authorization.