IET Intelligent Transport Systems最新文献

The anomaly detection and predictive replacement of the degradation decay state coefficient (D_esc) of ship power system (SPS) are crucial for ensuring their operational safety and maintenance efficiency. This study introduces the YC3Model, a model based on a dynamic triple sliding window mechanism, and Gaussian process regression) to address this challenge. It combines the temporal variation characteristics of the decay state coefficient's original data, first-order, and second-order differential data in both normal and abnormal trend intervals. The model calculates three local statistical measures within each sliding window and employs the Z-score method for anomaly detection. The combination of three sliding windows reduces false positives and negatives, enhancing the precision of anomaly detection. For detected anomalies, Gaussian process regression is used for prediction and replacement, providing confidence intervals to increase the reliability of the predicted values. Experimental results demonstrate that the YC3Model exhibits superior anomaly detection accuracy and adaptability in the degradation process of SPS, surpassing traditional methods across a range of evaluation metrics. This confirms the potential of YC3Model in health monitoring and predictive maintenance of SPS, offering reliable data input for the intelligent operation and maintenance (IO&M) of SPS.

High-definition maps are essential for autonomous vehicle navigation, but indoor parking lots remain poorly mapped due to high costs. To address this, a crowdsourcing model gathers data from consumer-grade sensors in mass-produced vehicles to create semantic maps. Indoor parking lots lack GNSS signals, and most of them do not have high-definition maps or navigation maps as references, making it difficult to ensure the accuracy of the final mapping results. Additionally, the semantic information of indoor parking lots is relatively limited, and the geometric features are overly similar, which significantly impacts the accuracy of point cloud registration. Therefore, this article proposes a crowdsourcing-based approach, where vehicles generate local semantic maps at the client end and upload them to the cloud. Leveraging the scene characteristics of indoor parking lots, the cloud optimizes and fits a large amount of crowdsourced data to obtain a high-precision base map without prior information. Enhanced ICP point cloud registration merges subsequent maps with the base. Additionally, parking space occupancy information is provided. This map can furnish the necessary information for Autonomous Valet Parking (AVP) tasks. Evaluation on the BEVIS dataset shows a root mean square error of 0.482446 m for vehicle localization on the cloud-based map.

The study of vessel trajectories (VTs) holds significant benefits for marine route management and resource development. VT segmentation serves as a foundation for extracting vessel motion primitives and enables analysis of vessel manoeuvring habits and behavioural intentions. However, existing methods relying on predefined behaviour patterns face high labelling costs, which hinder accurate pattern recognition. This paper proposes a self-supervised vessel trajectory segmentation method (SS-VTS), which segments VTs based on their inherent spatio-temporal semantics. SS-VTS adaptively divides VTs into cells of optimal size. Then, it extracts split points on different semantic levels from the multi-dimensional feature sequence of the VTs using self-supervised learning. Finally, spatio-temporal distance fusion module is performed on split points to determine change points and obtain VT segments with multiple semantics. Experiments on a real automatic identification system datasets show that SS-VTS achieves state-of-the-art segmentation results compared to seven baseline methods.

There is continuous research into driving cycles (DCs) as researchers across the globe seek to define driving characteristics, energy consumption, and emissions in a local context. For decades, data collection for the development of DCs has been conducted in three ways: chase car, instrumented vehicle, or a combination of both. Many studies have moved on to cheap and easily available global positioning system (GPS) technology, while others record vehicle data directly through the on-board diagnostics (OBD) port. However, there are major limitations to GPS data collection such as frequent inaccuracies and loss of coverage in urban environments. For this reason, both OBD and GPS vehicle speed data have been collected. Then, the recorded data has been analysed to capture any differences in sampling rates and dropping data. Finally, basic DCs were created from smoothed GPS and OBD data and compared. DCs were developed with a microtrip-based method, and a relative error term was used to compare candidate DCs to the collected data. DCs were compared based on kinematic characteristic parameters that are most used in the field. The results of this study could be used to assess the validity of GPS-based DCs compared to OBD cycles using low-cost devices.

A two-step framework that integrates real-time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO-v7 object detector to construct accurate traffic volume data. In the second step, an ARIMA–LSTM time series model is applied to forecast future traffic volumes. Experimental results show that YOLO-v7 achieved a vehicle detection accuracy of over 93.30%, ensuring high precision in traffic volume data construction. The ARIMA–LSTM model demonstrated superior performance in traffic volume prediction, with a mean squared error of 87.97, root mean squared error of 10,388.57, and mean absolute error of 101.39. YOLO-v7's detection speed of 7.8 ms per frame further validates the feasibility of real-time data construction. The findings indicate that the combination of YOLO-v7 for vehicle detection and ARIMA–LSTM for traffic prediction is highly effective, offering a significant reduction in training time compared to more complex deep learning models while maintaining high prediction accuracy. This research presents a unified solution for traffic data collection and prediction, enhancing transportation infrastructure planning and optimizing traffic flow. Future work will focus on extending the prediction intervals and further refining the models to improve performance.

Steady degradation (SD) prediction is crucial for the intelligent operation and maintenance of ship power system (SPS). Addressing the challenge of predicting the SD process, this study introduces the YC2Model, a system-level predictive method that integrates encoding time slice data to images (ETSD2I) with a convolutional neural network and Transformer. Incorporating the Transformer, in particular, enables the YC2Model to predict the SD state of SPS over extended periods more effectively. Compared to baseline models, YC2Model demonstrates superior performance on key performance indicators, including the highest coefficient of determination ($�R^2$) of 0.960717, and the lowest symmetric mean absolute percentage error of 0.015500, mean square error of 0.707211 × 10⁻⁴, root mean square error of 0.008410, and mean absolute error of 0.006519, proving its superior predictive accuracy. The correlation between model performance variations and degradation mechanisms is validated through statistical analysis of the YC2Model's performance in different stages of the SD process. During the SD process, YC2Model exhibits high predictive accuracy, an ability to capture changes in degradation mechanisms and robust adaptability to degradation trends. This model can provide precise and reliable SD state predictions for the intelligent operation and maintenance of SPS.

Remote driving, a well-matured technology in various industries, is relatively new to the railway sector but appears to be a promising solution for achieving advanced automation, especially for conventional trains. The shift from traditional in-cab driving to automated train operation, especially remote operations is a complex and ongoing process, with laboratory and field tests being conducted to examine its viability. This transition presents numerous areas that require further investigation and development. This study delves into these unexplored areas, examining various metrics that could be pivotal during the introduction of railway remote driving. The research adopts a mixed-method approach, employing a triangulation technique in data collection to address the research question on performance indicators for railway remote driving. Through an extensive literature review, benchmarking, and expert surveys, the study pinpoints several performance indicators crucial for assessing the operational effectiveness of remote railway operations. The developed indicators were validated using the two-round Delphi method, with 9 out of 13 being deemed essential by the panel of experts. The list of these indicators is the key finding in the study. They are: latency, data transfer rate, cybersecurity measures, video quality and camera stability, perception, system integration, permanent connection check, driver vitality check, and organizational aspects. The study contributes to filling the existing research gap and serve as a cockpit or instrumental panel in the implementation of remote operations, thus facilitating the transition towards more automated and remotely operated systems.

Customized bus services typically focus on single-trip requests, which often struggle to accommodate the growing needs for varied multiple trips in urban daily travel. This paper addresses the customized bus routing problem for passengers with multiple trips. A bi-objective mathematical model is established for maximizing the operational profit and minimizing the travel costs by considering the characteristics of the multi-trip requests and time-dependent travel time. Besides, a novel profit objective function is proposed considering the service's completion status and the starting price. Since the proposed mixed integer linear programming model is an NP-hard problem, a non-dominated sorting genetic algorithm II-based method is proposed to handle different sizes of instances. Finally, the instances with multi-trip requests are carried out to test the accuracy of the model and the effectiveness of our method compared with Gurobi and the local search-based multi-objective algorithm approach.

Max-weight (or max-pressure) is a popular traffic signal control algorithm that has been demonstrated to be capable of optimising network-level throughput. It is based on queue size measurements in the roads approaching an intersection. However, the inability of typical sensors to accurately measure the queue size results in noisy queue measurements, which may affect the controller's performance. A possible solution is to utilise the noisy max-weight algorithm to achieve a throughput optimal solution; however, its application may lead to decreased controller performance. This article investigates two variants of max-weight controllers, namely, acyclic and cyclic max-weight (with and without noisy queue information) in simulated scenarios, by examining their impact on the throughput and environment. A detailed study of multiple pollutants, fuel consumption, and traffic conditions, which are proxied by a total social cost function, is presented to show the pros and cons of each controller. Simulation experiments, conducted for the Kamppi area in central Helsinki, Finland, show that the acyclic max-weight controller outperforms a fixed time controller, particularly in avoiding congestion and reducing emissions in the network, while the cyclic max-weight controller gives the best performance to accommodate maximum vehicles flowing in the network. The complementary positive characteristics motivated the authors to propose a new controller, herein called the hybrid max-weight, which integrates the characteristics of both acyclic and cyclic max-weight algorithms for providing better traffic load and performance through switching.

Emergency Medical Services (EMS) are vital for providing timely out-of-hospital care during medical emergencies. This research aims to optimize ambulance services by strategically allocating resources to minimize response time. A modified Whale Optimization Algorithm (mWOA) is introduced to achieve this goal, focusing on providing 24 × 7 services to every patient in need. The, conducted in Southern Delhi, India, considers the uncertain and stochastic nature of demand and traffic. The results demonstrate a 14.6% improvement in average EMS-based response time, highlighting the effectiveness of the mWOA algorithm in enhancing ambulance allocation strategies. The results obtained using different algorithms are compared with those obtained using mWOA. The experiment outcomes demonstrate that the mWOA has higher efficiency and superiority than alternative algorithms regarding convergence rate and stability.