IET Intelligent Transport Systems最新文献

Online car-hailing, with its advantages of convenience and efficiency, has quickly become popular among tourists, playing a crucial role in the accessibility of scenic spots. Due to the particularities of tourist travel behaviour and the complexity of travel supply and demand around scenic spots, research on car-hailing tourists is relatively lacking at this stage. Based on multi-source data, this study couples the identifying of travel characteristics, by introducing the concept of service dependency degree, with a Bayesian optimization–long short-term memory–convolutional neural network (BO-LSTM-CNN) method to conduct multi-task online car-hailing demand forecasting. The evaluation of the dependency degree primarily encompasses the establishment of evaluation indices and the application of the entropy weight method and natural breakpoint method. The BO-LSTM-CNN model utilizes Bayesian optimization for hyperparameter tuning, LSTM for temporal variable processing, and CNN for the fusion of multi-source information related to weather, space, and online car-hailing attributes. Extracting online car-hailing tourist travel orders based on spatial–temporal constraints, the proposed methods are applied to 72 scenic spots in Beijing, China. According to their dependency degree, Beijing's scenic spots are categorized into three levels of dependency on online car-hailing services, from high to low. The outstanding forecasting efficacy of the proposed model for various scenic spots is verified through comparison tests with several benchmark models. Consequent to these findings, mobility service improvement strategies are specifically proposed for each class of scenic spots, which can provide valuable insights for the relevant tourism traffic management personnel.

Train delays can significantly impact the punctuality and service quality of high-speed trains, which also play a crucial role in affecting dispatchers with their decision-making. In this study, a data-driven train delay prediction framework was proposed and strengthened by considering the impact of dispatching commands and the mechanisms of train delay propagation using XGBoost. Four metaheuristic algorithms were utilized to fine-tune its hyperparameters. A vast dataset comprising 1.9 million records spanning 38 months of train operation data was utilized for feature extraction and model training. The model's accuracy was evaluated using three statistical metrics, and a comparison of the four tuning frameworks was performed. To emphasize the model's interpretability and its practical guidance for train rescheduling, the relationship of dispatching commands, delay propagation and delay prediction was validated by combining the theory and practical results, and a SHAP (SHapley Additive exPlanations) analysis was used for a clearer model explanation. The results revealed that distinct XGBoost-Metaheuristic models exhibit unique effects in different criteria, yet they all demonstrated high accuracy and low prediction errors, thereby revealing the potential of using machine learning for train delay prediction, which is valuable for decision-making and rescheduling.

This paper uses the Entropy Weight Method-Grey Relational Analysis (EWM-GRA) and Covariance Base Structural Equations Model (CB-SEM) to study the relationships between driving habits and efficiency. EWM-GRA ranks 12 indicators in terms of their relevance of lane-changing and driving efficiency. Based on this, a CB-SEM-based framework to describe the relevance between driving habits and lane-changing is established, focusing on the effects of lane-changing and car-following behaviour. To validate the established framework, NGSIM trajectory data is used as measurement variables to describe latent variables. Several hypotheses about the relationships between the latent variables in this framework are proposed, and they are verified using trajectory data. The results show that driving habits have a direct impact on efficiency, and this impact becomes more significant when associated with lane-change behaviour.

The vehicle platoon lane changing is significant for alleviating road congestion and diminishing transportation energy consumption. However, the lane changing strategy for a group of vehicles is still a great challenge in this field. This paper investigates the vehicle platoon lane changing problems, in which the safety and efficiency in the lane changing procedure are both taken into consideration. Since the safety of the platoon lane changing would be affected by the lane changing gap and the length of the platoon, a novel platoon lane changing strategy is proposed by using the deep Q-network. In detail, the proposed platoon lane changing strategy contains two layers, where the first one is a decision layer and the other one is the verification layer. In the decision layer, the deep Q-network is employed to improve the lane changing efficiency. Then, the verification layer is presented to enhance the platoon lane changing safety. In final, some typical platoon lane changing scenarios are provided in an existing ramp containing a vehicle platoon and some random vehicles. The related numerical simulations are conducted to validate the feasibility and effectiveness of the proposed approaches.

The green hub-and-spoke airline network (GHSAN) is emerging as a dominant feature due to its excellent economic and environmental-friendly capabilities. However, environmental GHSAN designs still have some concerns, including single pollutant-domain oversimplification and lack of comprehensive network-level operation impacts. This paper proposes a multi-emission-driven efficient network design approach for GHSAN, utilizing a system, green, and user threefold optimization methodology. The approach includes a multi-objective optimization model and a two-layer solving method. The multi-objective optimization aims at minimizing multiple emissions, including carbon dioxide, carbonic oxide hydrocarbon, and nitric oxide, while also considering transportation system costs and journey user costs. A two-layer optimization algorithm is adopted to address different scales of optimization. Real-world results demonstrate that the proposed method mitigates environmental impact and user costs and increases overall airline density in airline networks. The proposed method can have a 16.29% reduction in green-fold (10 nodes) and a 12.06% decrease in user costs for the user-fold (10 nodes). As the number of nodes (15, 25, 50 nodes) and hubs (3, 4, 5, 6, 7 hubs) increase, the genetic algorithm (GA) proves to be more efficient and suitable in large-scale GHSAN. This work is further significant for the long-term and sustainable development of the future air transport industry.

In contrast to the wheel-track trains, where the motor characteristics are considered a constant value, the motor characteristics of the medium-speed maglev (MSM) trains are the dependent variable of the position. This article studies the integration of traction power control and train control for the MSM to minimize energy consumption. First, an innovative integrated energy-efficient optimization model for MSM train control is constructed, considering the characteristics of the linear motors. Then, a multi-level dynamic programming (DP) approach, which includes the train operation simulation with the linear motor, is developed to solve the optimization problem. Furthermore, a speed-up strategy for the DP approach is proposed by a pre-calculated target train speed band (TTSB). The results of numerical experiments show that the DP approach yields a more practical train speed profile. In contrast, the DP approach with the TTSB strategy can achieve a better trade-off between solution accuracy and computational efficiency.

With the development of deep learning technology, the problem of data-driven trajectory prediction and intention recognition has been widely studied. However, the pedestrian trajectory prediction and intention recognition methods based solely on data-driven have weak data description ability and black-box characteristics, which cannot reason about pedestrian crossing intention and predict pedestrian crossing trajectory as humans do. To address the above problems, the authors proposed a data and knowledge-driven pedestrian intention estimation and trajectory prediction method by imitating human cognitive mechanisms. In the pedestrian intention inference process, the authors adopted the knowledge-driven method. As a first step, the authors built a knowledge graph of pedestrian crossing scenes, and then paired it with a Bayesian network to estimate pedestrian crossing intentions. In the pedestrian trajectory prediction process, the authors used a data-driven approach, combining pedestrian crossing trajectory features and knowledge-based pedestrian intentions. Experiments show that all evaluation metrics of pedestrian trajectory prediction were improved after adding pedestrian intentions obtained by knowledge-driven.

Accurately predicting traffic flow is crucial for intelligent transportation systems (ITS). In recent years, many deep learning-based prediction models have been widely applied in traffic flow prediction, and various spatio-temporal networks have been proposed. However, most of the existing models follow a general technical route to extract the spatio-temporal features, which lack the capacity of extracting the important historical information with the high spatial and temporal correlations dynamically and deeply. How to develop a well-performance traffic flow prediction model for a complex transportation network is still facing some challenges. In this paper, a hybrid dynamic spatio-temporal network (HD-Net) for traffic flow prediction is proposed. In HD-Net, the authors first extract the dynamic spatio-temporal features using dynamic graph convolution and bidirectional gate recurrent uni (BiGRU). Subsequently, the authors extract the important features with high spatial and temporal correlations from the obtained dynamic spatio-temporal features using an auto-correlation mechanism from a local perspective, and self-attention mechanism from a global perspective, respectively. Extensive experiments have been conducted on two real-world traffic datasets. The experimental results demonstrate that the proposed HD-Net outperforms the baselines in the field of capturing the dynamic and important spatio-temporal features with high correlations.

Contrastive learning is an increasingly important research direction and has attracted considerable attention in the field of computer vision. It can greatly improve the representativeness of image features through data augmentation, unsupervised learning, and pre-trained models. However, in the field of traffic flow forecasting, most graph-based models focus on the construct of spatial–temporal relationships between road segments and ignore the use of temporal data augmentation and pre-trained models, which can improve the representation ability of the forecasting model. Therefore, in this work, contrastive learning are used to expand the distribution of sequence samples and improve the quality and generalization of forecasting models. Based on this, a novel forecasting model called contrastive learning based on multi graph convolution network (CLMGCN) is proposed, which is combined with four components: multi graph convolution network, which learns the spatial–temporal feature of the input traffic data; temporal data augmentation, which obtains the augmentation data of the input traffic data; contrastive learning, which achieves the pre-training phase and improve the quality of output feature of multi graph convolution network; output block, which utilizes the enhanced output feature of multi graph convolution network for predicting the future traffic data. Finally, by the experimental results of four public traffic flow datasets, it can be shown that CLMGCN achieves higher traffic forecasting accuracy with lower model complexity.

Compared with the traditional travel mode, the increasingly mature autonomous driving and shared travel technologies can lead to a higher driving utilization rate and lower car parc; however, at the same time, they will bring unknown impacts to the trans-energy system. As a new generation of vehicles, the behavior of electric vehicles will also be affected. This paper describes and models the behavior of electric vehicle group in the autonomous driving-shared travel mode in detail, and uses the multi-agent technology to establish a large-scale electric vehicle group simulation model. This model fully considers the constraints of traffic network and charging station and the influence of fuel vehicles, which can simulate the actual scene well and be used to study the charging behavior of electric vehicle group. Finally, the simulation model is used to obtain the charging load curve of the electric vehicle group under the new travel mode and analyze the influence of travel upgrade and network topology on charging load.