IET Intelligent Transport Systems最新文献

Urban traffic speed prediction with high precision is the unremitting pursuit of intelligent transportation systems. The fundamental challenges of traffic speed prediction lie in the accurate modelling of the complex temporal and spatial correlations of transportation systems. Among all the methods, the hybrid “GNN + RNN” models have achieved state-of-the-art results. However, these methods still cannot address the following two challenges. First, in addition to the topology of road networks, the traffic speed could be affected by a variety of other factors, such as road functionality and weather. Second, in addition to predicting traffic speed, it is necessary to diagnose the causes of the prediction results. In this paper, we propose a multi-graph attentive network (MGAN), to predict and diagnose urban traffic speed. We create GNN model by using multiple graphs to encode the factors affecting them from various aspects. And we design a hierarchical attention mechanism to organize and pinpoint the fine-grained effects of different affecting factors for diagnosing the prediction results. The experimental results demonstrate that MGAN achieves state-of-the-art prediction performance on two real-world datasets, outperforming the strongest baseline by at least $��5.94�\%�$ across three prediction horizons, and is able to intuitively diagnose the prediction results.

Accurate platform-based passenger flow prediction based on deep learning technology becomes crucial for efficient operation and management; in particular, the prediction integrating external weather factors, temporal dependencies and spatial features is desired but has not been addressed. This paper is different from the previous station-based passenger flow prediction, but reconstructs data recorded by the Automatic Fare Collection System (AFCS) for platform-based prediction. Existing deep learning techniques often struggle with issues such as high computational cost in traffic flow prediction. To address these issues, a novel passenger flow prediction model is proposed that integrates convolutional neural networks (CNN) with bi-directional long short-term memory networks (BILSTM) and an attention mechanism (CNN-BILSTM-Attention). The proposed model takes preprocessed numerical weather features, temporal and spatial features as input. The CNN extracts spatial patterns from passenger flow data, the BILSTM captures temporal dependencies and the attention mechanism dynamically adjusts the importance weights of features at different time slots. By integrating these components, the model effectively captures spatiotemporal patterns while accounting for weather impacts. Experimental results demonstrate that the proposed approach outputs an efficient and accurate prediction.

The energy consumption of the subway has attracted much attention. Applying bidirectional converter substations (BCS) and researching energy-efficient train control (EETC) strategies can effectively reduce the energy consumption of the subway system. This paper analyzes the coupling model of power supply-train operation with rectifier substations (RS) and BCS. To minimize the energy consumption of substations, an optimal control problem model of EETC is established, and a multi-stage dynamic programming algorithm with state space reduction is designed to solve the train energy-saving speed profile. The EETC results of different line conditions with RS and BCS are presented. The results indicate that the EETC changes with the type of substations, where trains with BCS adopt regenerative braking conditions matched with the inverter turn-on voltage to feed back energy. The relationship between train running time and energy consumption is analyzed, showing that EETC with BCS has superior energy-saving effects and operational efficiency to EETC with RS. Results demonstrate the effectiveness and energy-saving effects of the optimization methods presented in this paper.

This paper introduces a dynamic optimisation system that enhances the management of train delays within automatic train operation (ATO) systems, utilising an innovative integration of shrinking-horizon model predictive control (SH-MPC) with genetic algorithms (GA). This research focuses on optimising train trajectories to efficiently handle various delay scenarios, from temporary speed restrictions to significant halts, ensuring both energy efficiency and punctuality. The proposed SH-MPC addresses diverse delay situations in real time, while the integration with GA overcomes the limitations of long horizon forecasting. The simulation of multiple trains on a real route demonstrates the robustness of the proposed system in adhering to scheduled timetables while reducing energy consumption.

This paper investigates the integration of rate-splitting multiple access (RSMA) into cellular vehicle-to-everything (C-V2X) networks to enhance resource allocation and interference management in decentralized, ad-hoc vehicular communication environments. C-V2X facilitates communication among vehicles, infrastructure, and pedestrians, and traditionally relies on orthogonal frequency division multiple access (OFDMA). However, OFDMA's rigidity limits its effectiveness under dynamic interference and imperfect channel state information (CSI) conditions typical of vehicular networks. RSMA, which blends features of spatial division multiple access (SDMA) and non-orthogonal multiple access (NOMA), provides a more adaptive framework by splitting messages into common and private parts, thereby improving spectral efficiency and interference handling. To assess RSMA's applicability, the LTEV2Vsim simulator was extended to include RSMA functionality, incorporating features such as reputation-based grouping, group-wise resource synchronization, and simplified beamforming. A dynamic grouping algorithm selects high-reputation vehicles as transmission leaders to form multi-vehicle groups of varying sizes for RSMA-based transmission. For interference modeling, self-interference is excluded from SINR calculations, and beamforming-based inter-vehicle interference is approximated. Simulation results reveal that RSMA outperforms OFDMA in terms of spectral efficiency and adaptability, particularly under conditions of incomplete CSI and varying interference. The findings confirm RSMA's suitability for complex and fast-changing vehicular environments, indicating its potential as a robust multiple access scheme for future C-V2X deployments.

The prediction of dynamic origin-destination (OD) data is critical for facilitating real-time traffic management across traffic networks. Despite numerous efforts to integrate the temporal and spatial characteristics of OD data to capture the nonlinearity and high dynamics of traffic flow, prior studies usually rely on link-level or region-level data for model construction. The temporal relationships among origin traffic flow, destination traffic flow, and OD flow remain insufficiently understood. To address this gap, we propose a novel definition of dynamic OD data using real-world OD datasets. Our framework can incorporate different temporal distributions for each OD pair. Additionally, the framework ensures flow conservation from either the origin or the destination perspective. The performance of the proposed framework is validated through numerical studies using real-world electronic toll collection (ETC) gantry data. A multi-task long short-term memory (LSTM) model predicts OD flows, and both the predictions and the resulting destination traffic distributions are statistically indistinguishable from the observed values. Furthermore, this approach enables the prediction of arrival volumes before trip completion, offering valuable insights for real-time traffic management.

Accurate traffic speed prediction is important in modern society for its effectiveness in route navigation, estimated time of arrival calculations and other practical applications. As the road network is complicated, traffic speed exhibits high-order correlations among regions, namely many-to-many spatial correlations, while also displaying long-term temporal dependencies. However, existing studies have not effectively modelled these characteristics. In this context, this study proposes a self-attention enhanced hypergraph convolution network (SE-HCN) for accurate speed prediction. The proposed SE-HCN consists of four modules. Specifically, we design a relation extraction module, which can obtain the similarity of road sections from geographical information and clustering. Subsequently, the model contains a spatial correlation hypergraph convolutional module and a long-term temporal dependencies transformer module to capture spatio-temporal features comprehensively. Two public real-world datasets - PeMSBAY and PeMSD7-M - were tested to validate the model's performance, and the result demonstrates that our approach achieved state-of-the-art performance.

Significant differences in cellular tower density across cities pose a major challenge for identifying intercity travel origin-destination (OD) pairs from mobile phone signalling data. Many existing OD identification algorithms apply uniform parameters across cities, which can undermine detection accuracy in heterogeneous networks, and their performance remains underexplored under varied tower density conditions. To address this gap, we conducted a field experiment collecting mobile signalling data from intercity trips in two metropolitan regions with different tower densities, while recording GPS trajectories and travel diaries as ground truth. We compared the unsupervised spatiotemporal clustering method (ST-DBSCAN) and the supervised deep learning model (Bi-LSTM) for OD identification. Furthermore, we introduced a novel genetic algorithm adaptive parameter selection mechanism to enhance performance under different density conditions by dynamically adjusting ST-DBSCAN's clustering radius, time threshold and minimum cluster size, as well as tuning Bi-LSTM's input features and time window length. Results show that this adaptive approach significantly improved OD identification accuracy, with optimised ST-DBSCAN achieving 84% accuracy and Bi-LSTM 91%. These findings highlight the importance of adaptive algorithm calibration and offer theoretical insights and practical guidance for more reliable intercity travel modelling in metropolitan areas with heterogeneous cellular infrastructure.

To increase the loading rate of bulk carriers and reduce the cost of loading and transportation, it is necessary to rationally match bulk orders with vessel resources. Decision-making on order-ship matching is difficult due to the need to consider issues such as order splitting, bulk cargo variety switching, and liner routes. This paper aims to optimise the ship loading rate, loading variety switching cost, and transportation cost by constructing a three-objective order-ship matching optimisation model. Addressing the problems of poor solution set diversity and search ability in the traditional NSGA-II algorithm for large-scale problems, this paper proposes using the First Fit algorithm as an initialisation method to reduce the solution space. Additionally, an adaptive greedy evolution operator is designed to improve the searchability of the NSGA-II algorithm. Finally, an aggregate producer is used as an example to verify the feasibility of the matching algorithm. Experimental results show that the algorithm achieves an average ship loading rate of over 93% for the matching scheme and reduces costs in solving the ship waybill scheme when the problem size is large.

The accepted gap—the time or distance a driver deems sufficient to enter or cross an intersection—is a key indicator of traffic risk, particularly at uncontrolled three-legged intersections. Smaller accepted gaps are linked to higher risk due to an increased chance of vehicle conflicts. This study investigates the relationship between accepted gaps and risk and proposes a method to quantify the level of risk and severity (LORS) to guide targeted safety interventions. Data on vehicle speed, accepted gap and critical gap were collected from six rural intersections in India. Using a binary logit regression model and clustering techniques, the LORS was estimated and validated against actual accident data, yielding a predictive accuracy of up to 83%.

The significance of this study lies in its novel data-driven approach to safety assessment using parameters easily measured in the field. Designed for heterogeneous traffic conditions, the method provides traffic engineers and planners with a practical tool to assess intersection safety, recommend specific remedial measures and prioritise interventions based on risk and severity levels. With potential for automation and scalability, this research contributes to the development of safer road systems, particularly in low-resource settings where conventional crash data is limited or unavailable.