IET Intelligent Transport Systems最新文献

Practical applications of graph neural networks (GNNs) in transportation are still a niche field. There exists a significant overlap between the potential of GNNs and the issues in strategic transport modelling. However, it is not clear whether GNN surrogates can overcome (some of) the prevalent issues. Investigation of such a surrogate will show their advantages and the disadvantages, especially throwing light on their potential to replace complex transport modelling approaches in the future, such as the agent-based models. In this direction, as a pioneer work, this paper studies the plausibility of developing a GNN surrogate for the classical four-step approach, one of the established strategic transport modelling approaches. A formal definition of the surrogate is presented, and an augmented data generation procedure is introduced. The network of the Greater Munich metropolitan region is used for the necessary data generation. The experimental results show that GNNs have the potential to act as transport planning surrogates and the deeper GNNs perform better than their shallow counterparts. Nevertheless, as expected, they suffer performance degradation with an increase in network size. Future research should dive deeper into formulating new GNN approaches, which are able to generalize to arbitrary large networks.

With the development of connected automated vehicles (CAVs), preview and large-scale road profile information detected by different vehicles become available for speed planning and active suspension control of CAVs to enhance ride comfort. Existing methods are not well adapted to rough pavements of different districts, where the distributions of road roughness are significantly different because of the traffic volume, maintenance, weather, etc. This study proposes a comfortable driving framework by coordinating speed planning and suspension control with knowledge transfer. Based on existing speed planning approaches, a deep reinforcement learning (DRL) algorithm is designed to learn comfortable suspension control strategies with preview road and speed information. Fine-tuning and lateral connection are adopted to transfer the learned knowledge for adaptability in different districts. DRL-based suspension control models are trained and transferred using real-world rough pavement data in districts of Shanghai, China. The experimental results show that the proposed control method increases vertical comfort by 41.10% on rough pavements, compared to model predictive control. The proposed framework is proven to be applicable to stochastic rough pavements for CAVs.

The ability to understand the underlying fundamentals of traffic flow behaviour facilitates improved planning and decision-making for road operators. This paper presents an overview of the various models which can be used to describe the interaction between the different parameters governing traffic flows. 5-years of measured data from Ireland's M50 motorway are used to demonstrate the application of traffic flow theory using real data, and a detailed investigation of factors affecting the fundamental traffic behaviour is presented. The road capacity is shown to be impacted by different traffic behaviour during morning and evening-peak periods, during dry vs. wet weather conditions and between lanes on the approach to junctions. It is demonstrated that the mean vehicle length is an important factor to consider when using traffic flow models. A novel 3-dimensional fundamental diagram model linking mean vehicle speed, mean vehicle length, and density is introduced which enhances capacity estimation and illustrates the importance of considering vehicle length when using the fundamental diagram to interpret traffic flows and estimate the capacity of the motorway.

There is a significant lack of comprehensive research that systematically examines public perceptions of liability (related to cyber risks), consumer data, and how these factors influence the adoption of automated vehicles (AVs). To fill this knowledge gap, the authors' research used a survey of 2062 adults across Australia, New Zealand, the UK, and the US to develop a scale for Liability, Data concerns, Data sharing and Patching and updates. This analytical approach employed various statistical methods to analyze the data (summarizing, finding patterns, measuring relationships). The results indicate that 70% of respondents express concerns about AV liability based on cyber risks, highlighting a significant level of liability anxiety. Individuals with high liability concerns also exhibit heightened concerns about AV data, are less comfortable sharing AV data, and display lower intent to adopt AVs. Conversely, individuals comfortable with data sharing are more willing to engage in patching and express a greater intent to adopt AVs. Interestingly, individuals with AV data concerns do not exhibit a negative correlation with their intent to adopt AVs. Additionally, those willing for patches also show a stronger intent to adopt AVs, challenging the notion that software updates hinder AV adoption.

This study aims to introduce a new strategy for anticipating the behaviour of human-driven vehicles (HDVs) and designing trajectories for connected and automated vehicles (CAVs) at signalized intersections under mixed traffic scenarios. To tackle the challenge of unreliable HDV trajectory predictions stemming from driving unpredictability, a recursive framework is developed. This framework integrates real-time tracking data from both traffic detectors and CAVs, continuously updating HDV predictions. The proposed approach employs the updated predictions to formulate optimal control problems recursively to optimize or adjust CAV trajectories, enhancing travel and energy efficiency. Besides, the recomputing of CAV trajectories will only be conducted when the variation in predictions rises to a certain threshold, balancing efficiency and computing consumption, inspired and modified based on MPC methods. The application of the Pontryagin maximum principle aids in finding solutions efficiently by transforming necessary conditions into a system of equations and consolidating elementary unconstrained and constrained arcs. Numerical simulations were carried out to evaluate the performance of the proposed recursive framework, revealing its superiority over the one-time approach, particularly in isolated intersections with high traffic demands. Additionally, the recursive framework exhibited more robust and effective enhancements throughout the road network.

This study examines road user opinions regarding small modular autonomous electric vehicles, focusing on the differences between the elderly and non-elderly populations in Norway. The data allowed for a comparison between 193 respondents under 65 years old and 208 respondents over 65 years old. The results highlighted significant differences between the two groups about the vehicles, their usability, and the likeliness of using them as public transport if implemented in the future. Traffic safety and personal security were found to be decisive aspects, for respondents over 65 years old being more worried about safety and security than their counterparts. Trust that the authorities will ensure the safe implementation of such vehicles in the current transportation system was also significantly different between the two groups, with the younger generations having more trust in the authorities than the older group. The results shed light on road user opinions about a small modular transport mode, particularly on those over 65 years old, indicating a need for research efforts to better identify how this new form of public transport should be implemented in the future to improve the mobility of all travellers and meet the needs of the seniors.

Digital twin (DT) has attracted much attention from the transportation community over the past 6 years. Combining the DT with intelligent transportation system (ITS) forms a digital twin intelligent transportation system (DT-ITS), which stands as one of the most effective solutions for addressing current complex traffic problems. Due to the rapid advancements in this field and a lack of recent literature reviews, this paper first reviews relevant literature on DT-ITS architecture design, to comprehend its core structure, methods, potential services and stakeholders, and implementation challenges, and then discusses DT-ITS core considerations, aiming to provide a general configuration model of DT-ITS for future development. Second, this paper focuses on reviewing the existing progress of DT-ITS services within the 32 categories of ITS services, adopting the service-centred point of view, to explore the potential DT-ITS services, proposed delivery methods, challenges, and opportunities for various stakeholders. Third, key enabling technologies supporting DT-ITS are reviewed and discussed, such as data fusion, cooperative perception, multi-access edge computing (MEC) (including computing offloading and service caching), federated learning, edge-cloud collaboration, secure and efficient communication (including Blockchain [BC], 5G), virtual modelling, and eXtended reality (XR). Finally, the paper identifies development trends and provides recommendations for future advancements.

In recent years, Vehicular Ad-hoc Network (VANET) has become an essential component of intelligent transportation systems that, along with the previous systems such as traffic condition, accident alert, automatic parking, and cruise control, use the communication of vehicle to vehicle and vehicle to the roadside unit to facilitate road transportation. Several challenges hinder efforts to improve traffic conditions and reduce traffic fatalities through VANET. A critical challenge is achieving highly accurate and reliable vehicle localization within the VANET. Additionally, the frequent unavailability of Global Positioning System (GPS), particularly in tunnels and parking lots, presents another significant obstacle. Traditional methods like Dead Reckoning offer low accuracy and reliability due to accumulating errors. Similarly, GPS positioning, map matching with mobile phone location services, and other existing solutions struggle with accuracy and economic feasibility. In this article, two Kalman filter approaches are used based on signal statistical information and the other learning-based networks, including traditional neural network, deep neural network and LSTM (long short-term memory) to locate the car. The prediction error of car position with root mean square measures. The squared error and distance prediction error are evaluated. It is shown that in terms of prediction time and processing time of vehicle localization, all the vehicle localization methods are efficient in terms of response time for localization, and Kalman filter methods, traditional neural network and deep neural network are faster than LSTM method. Also, in terms of localization error, Kalman filter works better than learning-based methods, and in learning-based methods, both deep neural network and LSTM methods perform better than traditional neural network in terms of localization error.

While existing literature has focused on modelling pedestrian movement on platforms, there is a lack of understanding of passengers' perceptions, motivations, and influential factors that shape their on-board behaviours and choices. This study developed a conceptual framework to assess passengers' post-boarding behaviours and perceptions, specifically focusing on their actions and choices inside the train carriages. The conceptual framework was tested through survey data of 429 passengers in Melbourne, Australia. The result shows that door access is the most influential factor when passengers choose where to stand or sit on board, followed by comfort, safety, privacy, and random factors. Furthermore, the study explores the relationship between the post-boarding behaviour variables and travellers’ personal and trip characteristic variables. The analysis shows that carrying large items has a more significant effect on many post-boarding behaviour variables. Gender, age group, travel frequency, waiting time, and carrying small items also play significant roles. However, variables such as travel time and frequency of group travel have lesser effects. These novel findings offer valuable insights, laying the groundwork for future modelling activities. Moreover, the understanding derived from passenger perceptions can guide transport agencies and operators in shaping strategies to improve onboard services.

The fast and safe formation tracking problem for multiple autonomous underwater vehicle (AUV) systems (MAUVS) with asymmetric output constraints and actuator faults is studied in this article. Actuator faults are composed of loss of effectiveness and time-varying unknown bias faults, an adaptive fixed-time fault-tolerant controller (AFFTC) is designed by employing fixed-time stable theory and fuzzy logic systems. Under the designed control algorithm, the MAUVS can be practically fixed-time stable, the tracking errors among the follower AUVs and the virtual leader AUV can converge to a small area near the origin within the fixed time, and the settling time is unaffected by the initial state of the system. To enhance the safety of MAUVS, a novel asymmetric barrier function is utilized to constrain the trajectory tracking errors within the prescribed range. Finally, the simulation results demonstrate the effectiveness of the proposed control algorithm.