IET Intelligent Transport Systems最新文献

Given that the overall coverage deployment method fails to meet information needs in important areas, there are redundancies and deficiencies in the information provided. To enhance communication stability for roadside units (RSUs), improve information coverage at critical intersections and optimize algorithm efficiency. Here, a method for deploying RSUs is proposed that aims to optimize revenue in road network subareas. The road network is divided into several subareas based on critical intersections, node similarity, road segment correlations, and characteristics of RSU information transmission. Then, a roadway accessibility algorithm is developed that accounts for channel fading. Considering the robustness of wire network deployment, an improved traveling salesman problem (TSP) problem is proposed that includes candidate locations and constructs a model for optimal RSU deployment that maximizes consolidated revenue. Finally, using the Sioux Falls network as an example, the RSU deployment strategy is evaluated for the overall network and the road network after being subdivided. The results indicate that subdividing the road network improves the efficiency of the optimization solution, the information coverage of critical intersections increases by 1.8 times. The deployment optimization scheme of RSUs is directly influenced by various parameters such as bandwidth capacity and cost coefficient. When deploying RSUs in road network subareas, variations in total demand have minimal impact on RSU deployment, ensuring a stable deployment scheme.

In order to further improve the accuracy of short-term traffic flow prediction on designated sections of highways, a combined prediction model is designed in this paper to predict the traffic flow on designated sections of highways. Firstly, for the shortcomings of artificial rabbits optimization (ARO) algorithm, sine cosine ARO (SARO) is proposed by incorporating sine cosine algorithm (SCA) idea into ARO, and introducing the non-linear sinusoidal learning factor. Secondly, three mobile inverted bottleneck convolution (MBConv) modules are utilized to form the MB3 module, and with BiGRU are utilized to form the MB3-BiGRU combined prediction model. Finally, the MB3-BiGRU model is optimized by SARO to achieve short-term prediction of traffic flow. The analysis results show that using the United Kingdom highway dataset as the data source, the SARO-MB3-BiGRU presented in this paper reduces the root mean squared error (RMSE) by 32.58%, the mean absolute error (MAE) by 30.25%, and the decision coefficient (R²) reaches 0.96729, as compared to BiGRU. Compared with other common models and algorithms, the SARO has good solving capabilities and versatility, and the SARO-MB3-BiGRU model has been greatly improved in terms of prediction accuracy and generalization ability, which has better prediction ability and engineering reference value.

Vehicle-to-everything communication systems play a crucial role in enhancing road safety and traffic efficiency through vehicle and roadside infrastructure interactions. To provide robust defences against external threats in secure and trustworthy information exchange, these systems utilise public key infrastructure to authenticate vehicle-to-everything participant identities with digital certificates and security credential management systems to administer these certificates and encryption keys. However, even with these defences, vulnerabilities persist, particularly from vehicles with legitimate certificates that may malfunction or be exploited for malicious purposes. To address these issues, this paper introduces a misbehaviour detection (MBD) system, notable for its combined use of local and global MBD algorithms. This system is specifically designed to combat both conventional and novel threats, including slander attacks, in which vehicles with legitimate certificates may be falsely accused, and sophisticated attacks targeting the global MBD system itself. The efficacy of our MBD system was rigorously validated at K-City, the leading autonomous vehicle technology testing facility in Korea, demonstrating its ability to identify and counter internal misbehaviours precisely.

Special vehicles (SVs) are vehicles which conduct tasks such as the maintenance of urban roads and are typically characterized by travelling at a lower speed at a constant rate of speed within the same lane. In order to reduce the influence of SVs, guidance zone is designed and provides traffic guidance suggestions (TGS) for human-driven vehicles (HVs) helping drivers for better decision between car-following (CF) and lane-changing (LC). To verify the effectiveness of TGS, an improved Dogit-agent-based model is established to simulate the captive and not captive choice of CF and LC for different driver types under TGS, and build the rules for mixed traffic flow of SV and HVs. Finally, a numerical simulation with a three-lane system is conducted to analyze the traffic efficiency through a set of indicators, and the results show that the TGS can reduce the influence of SVs on traffic flow in a specific occupancy rates range, increase the cross-section traffic volume by about 5%. The TGS also can increase the average speed of HVs in the lane behind SV by about 5% to 30%, and increase traffic density to 200% on the underutilized lane in the raw space in front of the SV.

Individual mobility is driven by activities and thus restricted geographically, especially for trip destination prediction in public transport. Existing statistical learning based models focus on extracting mobility regularity in predicting an individual's mobility. However, they are limited in modeling varied spatial mobility patterns driven by the same activity (e.g. an individual may travel to different locations for shopping). The paper proposes a deep learning model with activity, geographic and sequential (DeepAGS) information in predicting an individual's next trip destination in public transport. DeepAGS models the semantic features of activity and geography by using word embedding and graph convolutional network. An adaptive neural fusion gate mechanism is proposed to dynamically fuse the mobility activity and geographical information given the current trip information. Besides, DeepAGS uses the gated recurrent unit to capture the temporal mobility regularity. The approach is validated by using a real-world smartcard dataset in urban railway systems and comparing with state-of-the-art models. The results show that the proposed model outperforms its peers in terms of accuracy and robustness by effectively integrating the activity and geographical information relevant to a trip context. Also, we illustrate and verify the working mechanism of the DeepAGS model using the synthetic data constructed using real-world data. The DeepAGS model captures both the activity and geographic information of hidden mobility activities and thus could be potentially applicable to other mobility prediction tasks, such as bus trip destinations and individual GPS locations.

This article studies cooperative adaptive cruise control (CACC) for vehicle platoons with consideration of the unknown nonlinear vehicle dynamics that are normally ignored in the literature. A unified data-driven CACC design is proposed for platoons of pure automated vehicles (AVs) or of mixed AVs and human-driven vehicles (HVs). The CACC leverages online-collected sufficient data samples of vehicle accelerations, spacing, and relative velocities. The data-driven control design is formulated as a semidefinite program that can be solved efficiently using off-the-shelf solvers. Efficacy of the proposed CACC are demonstrated on a platoon of pure AVs and mixed platoons with different penetration rates of HVs using a representative aggressive driving profile. Advantage of the proposed design is also shown through a comparison with the classic adaptive cruise control (ACC) method.

Vehicle lane-changing behaviour is often regarded as transient traffic behaviour while ignoring behavioural characteristics of the lane-changing process. A combined prediction model based on wavelet transform (WT) and dual-channel neural network (DCNN) is proposed to explore the selection behaviour of lane-changing distance by taking lane-changing behaviour in an urban inter-tunnel weaving section. Firstly, the extracted lane-changing data are analysed for correlation and noise reduction, and the main factors affecting lane-changing distance are taken as input variables of the model. The trajectory data of the inter-tunnel weaving section of the “Jiuhuashan-Xi'anmen” tunnel in Nanjing, China, are used to improve the prediction of vehicle lane-changing distance by training the model. The results show that the proposed WT-DCNN model has high prediction performance when compared with existing artificial neural network (ANN), DCNN and wavelet neural network (WNN) models. The characterization and study of the typical lane-changing behaviour in the weaving section can lay the theoretical foundation for the development of an urban inter-tunnel weaving section management scheme.

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.