IET Intelligent Transport Systems最新文献

At present, the mainstream studies on route selection optimization at the railway station rarely considered the overall punctuality of the operation plans and the seizing route resource between shunting operation and train running, which can endanger the running safety and reduce the efficiency at the station. Therefore, this paper proposes an optimization method for the route selection under the integration of dispatching and control at the railway station. Firstly, the station-type data structure, the route occupation conflict, and the operation task order were defined. Then, a 0-1 programming model was constructed to minimize the total delay time and shorten the total travel time of all operations. Finally, a two-stage solution algorithm based on depth-first search algorithm and genetic algorithm was designed, and two actual cases of a technical station in China were designed. The instance verification results show that the algorithm can find the satisfactory route scheme in 250 iterations; different delay factors and travel coefficients will get different route schemes, which can provide decision support for dispatchers and operators to select routes. Through comparative analysis of algorithms, it is found that the two-stage algorithm has higher solving efficiency than the individual depth-first search algorithm and individual genetic algorithm.

Driving while inattentive or fatigued significantly contributes to traffic accidents and puts road users at a significantly higher risk of collision. The rise in road accidents due to driver inattention resulting from distractive objects, for example, mobile phones, drinking, or tiredness, requires intelligent traffic monitoring systems to promote road safety. However, outdated detection technologies cannot handle the poor accuracy and the lack of real-time processing possibility especially when combined with the variations of driving environment. This paper introduces “ME-YOLOv8” which operates driver`s distraction and fatigue through a modified version of YOLOv8, which includes modules multi-head self-attention (MHSA) and efficient channel attention (ECA) modules applied, where the goal of MHSA is to improve the sensitivity of global features and the ECA attentions focus on critical features. Additionally, a dataset was created containing 3660 images covering multiple distracted and drowsy driver scenarios. The results reflect the enhanced detection capabilities of ME-YOLOv8 and demonstrate its effectiveness in real-time scenarios. This study demonstrates a significant advancement in the application of AI to public safety and highlights the critical role that state-of-the-art deep learning algorithms play in lowering the risks associated with distracted and tired driving.

Continuous-flow intersections (CFI), also known as displaced left-turn (DLT) intersections, aim to improve the efficiency and safety of traffic junctions. A CFI introduces additional cross-over intersections upstream of the main intersection to split the left-turn flow from the through movement before it arrives at the main intersection which decreases the number of conflict points between left-turn and through movements. This study develops and examine a two-step optimization model for CFI traffic signal control design and demonstrates its performance across more than 300 different travel demand scenarios. The proposed model is compared against a state-of-practice CFI signal control model as a benchmark. Microsimulation results suggest that the proposed model reduces average delay by 17% and average queue length by 32% for a full CFI compared with the benchmark signal control model.

The co-phase traction power supply system (TPSS) with hybrid energy storage system (HESS) and photovoltaic (PV) is proposed to eliminate the neutral section and improve the regenerative braking energy (RBE) utilization. Although the integration of HESS and PV facilitates the energy saving and cost reduction of the co-phase TPSS, the high cost and configuration of HESS should be considered, which is the key to affect the optimal operation strategy of co-phase TPSS. Here, the optimal operation strategy of co-phase TPSS with HESS and PV is proposed to design the HESS configuration, recycle RBE and improve power quality. The proposed model aims to minimize the total system cost, including HESS investment cost, electricity cost and operation and maintenance cost. Moreover, the proposed model is formulated as a mixed integer linear programming by employing linearization approaches. Finally, case studies verify that the 29.2% cost reduction rate is achieved and the three-phase voltage unbalance meets the standard requirements.

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.