IET Intelligent Transport Systems最新文献

To enhance the robustness and interpretability of road-accident prediction under sparse, noisy, and dynamically evolving conditions, this study proposes a Multimodal Grey-Markov and Adversarial Meta-Learning (MGMC-AML) framework. The model integrates grey relational analysis for adaptive modality weighting, dynamic Markov state modelling for temporal-spatial transition learning, and adversarial meta-optimisation for rapid recovery from perturbations. A reinforcement-enhanced dynamic partitioning mechanism is introduced to maintain structural consistency of road-network topology during fluctuating traffic states. Extensive experiments on multi-source traffic and weather datasets demonstrate that the proposed framework achieves superior predictive performance, with a Mean Absolute Error (MAE) of 2.34 incidents per day, a Segmentation Consistency Index (SCI) of 0.85, and a Modular Q value of 0.78, while maintaining an attack-recovery time of 162 s. These results surpass state-of-the-art baselines, including LSTM, GNN, and Transformer models, confirming both its precision and resilience under adversarial or uncertain sensing conditions. The proposed approach offers a unified and reproducible framework that bridges uncertainty modelling, adaptive fusion, and robust optimisation, providing a theoretical and practical foundation for resilient traffic-risk forecasting in intelligent-transportation systems.

Given that constant-parameter model predictive control (MPC)-based automatic emergency braking (AEB) systems are unable to simultaneously balance comfort, safety, and computational efficiency, this paper proposes an adaptive optimization strategy for MPC that adjusts weights and sampling times to address this issue. First, a three-level warning strategy based on a safety distance model is introduced. This strategy assesses the risk level during vehicle operation using an emergency coefficient and adaptively adjusts the sampling time. By analysing the correlation between relative distance, speed, and injury risk, weight adjustment rules based on injury risk are determined. Second, a fuzzy regulator is developed with the emergency coefficient, injury risk, and relative distance as inputs is developed to enable dynamic adjustment of weights and sampling time in response to operational conditions. Finally, an AEB control strategy is designed based on hierarchical control principles: the upper layer uses MPC to achieve multi-objective optimization, while the lower layer employs PID correction to track the desired acceleration. In the test scenarios, joint simulation experiments were conducted using CarSim and MATLAB/Simulink, and the results under four scenarios and operating conditions were analysed and compared. The results show that the proposed control strategy enhances comfort while ensuring AEB safety control, reducing the average braking distance deviation by 11.80% and the average acceleration deviation by 48.91%. These improvements are significant for enhancing AEB performance without hardware modifications.

Traditional methods of vehicle trajectory reconstruction heavily rely on the data of cross-sectional traffic detectors, but the effectiveness of existing methods is limited by insufficient information of the cross-sectional data. In response to this, this study proposes a novel trajectory reconstruction method based on long-range traffic detectors. It builds upon Newell's car-following model and its derived inverse following model. By taking driver heterogeneity into account through individualised calibration of the key parameter, namely the spatial shift, which is optimised by the whale optimisation algorithm, the accuracy of trajectory reconstruction is enhanced. Furthermore, to connect trajectories of the same vehicle in the same area that are reconstructed by adjacent long-range detectors, a particle filter-based trajectory fusion method is developed. It can fuse overlapped reconstructed trajectories and smoothly connect sectional trajectories into a complete and seamlessly connected one. The performance of the trajectory reconstruction method is evaluated on the NGSIM I-80 dataset, while the trajectory fusion method was tested on both the I-80 and the TRJD TS datasets. Results show that the reconstruction method generates complete vehicle trajectories across various traffic flow conditions, achieving an average of 28.65% reduction in mean absolute error compared to methods that do not account for driver heterogeneity. The mean absolute error of the fused trajectories was reduced by 49.23% and 59.69% on average for two datasets, respectively, compared to reconstructed trajectories using a single detector. The trajectory reconstruction accuracy of the proposed method also outperforms that of a deep convolutional neural network and an improved adaptive smoothing method.

In this study, we introduced a multi-task deep learning framework that concurrently forecasts traffic accident risk and severity by integrating convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) units, and a self-attention mechanism. Unlike conventional single-task approaches, our model leverages shared spatiotemporal representations to capture complex patterns in traffic data, thereby enhancing both predictive accuracy and generalizability. Evaluations on large-scale datasets from New York City and Chicago demonstrate that our approach achieves high accuracy (up to 92% for accident risk and 89% for severity) and remains robust across diverse urban contexts. Moreover, an enhanced SHAP-based interpretability module provides granular insights into the influence of both observable and latent factors, such as driver behaviour or road surface conditions, on prediction outcomes. The self-attention mechanism further mitigates unobserved heterogeneity by highlighting critical time steps and feature interactions. With competitive real-time performance and throughput, our framework offers a practical solution for dynamic traffic safety applications. Future work will focus on extending evaluations to broader urban settings and integrating latent variable models to better quantify unobserved influences, ultimately advancing the development of safer, more efficient transportation systems.

Accurate modelling of acceleration and deceleration decisions is vital for replicating car-following behaviour, as these govern longitudinal control, traffic stability, and safety. This study introduces ArrowNet81, a 200-layer novel convolutional neural network (CNN) architecture designed to model the asymmetric nature of these decisions using multivariate time-series data. The architecture leverages modular Ribs and Linkers to enhance depth while minimizing complexity. A perceived-risk variable derived from nominal time-to-collision (NomTTC) addresses the effect of vehicle heterogeneity in car following behaviour. Trajectories extracted from UAV video footage support neighbourhood vehicle identification via the queens move system (QMS), and a novel generalized data arrangement technique ensures compatibility with deep learning (DL) inputs. To prove its generalizability and superiority, the proposed architecture is trained, tested, and validated in three diverse traffic conditions against different statistical, machine learning (ML), and DL techniques. And the developed models using ArrowNet81 architecture outperform all of them. Future research may adapt ArrowNet81 for classification problems such as mode choice, accident severity, or spatial risk mapping, and integrate it into connected and autonomous vehicles (CAVs) to emulate human-like behaviours. The rib-based architecture also permits the development of lighter variants, facilitating real-time deployment in resource-constrained environments without compromising predictive performance.Accurate modelling of acceleration and deceleration decisions is vital for replicating car-following behaviour. ArrowNet81, a 200-layer novel CNN architecture designed to model the asymmetric nature of these decisions using multivariate time-series data. The rib-based architecture permits the development of lighter variants, facilitating real-time deployment in resource-constrained environments without compromising predictive performance.

Multi-bottleneck highway segments present significant challenges in traffic management due to the propagation of congestion waves between closely spaced bottlenecks. This study proposes a linear model predictive control (MPC)-based ramp metering strategy designed specifically for continuous multi-bottleneck corridors. The approach incorporates a macroscopic linear traffic flow model that discretizes the roadway into interconnected cells, allowing real-time prediction of traffic states on both mainline and ramp segments. The control problem is formulated as a constrained quadratic programming task aimed at minimizing vehicle accumulation and enhancing overall throughput. A key innovation of this strategy lies in its predictive, multi-input, multi-output architecture, which enables proactive, corridor-wide coordination of ramp inflows based on anticipated traffic conditions and inter-bottleneck interactions. To ensure real-time computational feasibility, a linearized cell transmission model is used and efficiently solved via the CPLEX optimizer. Simulation experiments demonstrate the effectiveness of the proposed method, with reductions in total travel time of 23.7%, 11.6% and 2.4% and corresponding reductions in total delay of 74.6%, 54.1% and 8.9%, compared to no-control, PI-ALINEA and regional MPC ramp metering strategies, respectively. These results highlight the strategy's superiority in improving system-wide traffic efficiency under complex congestion scenarios.

Since the integration of private vehicles into modern societies, traffic congestion has emerged as a critical and persistent problem. In recent years, the rise of micro-mobility, including all forms of light electric vehicles such as e-scooters and e-bikes, has introduced both innovative solutions and significant challenges. While many scholars argue that micro-mobility can help alleviate traffic-related issues, ongoing debates highlight potential drawbacks, including regulatory gaps, concerns over battery reliability, and the increased risk of severe injuries in accidents. Within this context, a systematic synthesis of the literature represents an important milestone for the field. This study systematically reviews academic research on electric scooters (e-scooters) published in journals indexed in SCI, SCI-Expanded, and SSCI, providing a comprehensive assessment of the existing body of literature. The review organizes the literature into eight thematic categories: demand forecasting, optimization, marketing, urban micro-mobility policies and user behaviour, health and safety, prior review studies, survey-based research, and other miscellaneous domains. From an initial pool of 1921 peer-reviewed articles, the application of objective filtering criteria produced a final dataset of 313 studies, each of which was rigorously examined and categorized accordingly. Using bibliometric techniques, the study analyses publication trends, the geographical distribution of research, collaboration networks among authors, and citation patterns within the e-scooter literature. The findings reveal that demand forecasting and optimization dominate in terms of publication volume, while health, safety, and legal/regulatory research remain comparatively underexplored. Bibliometric results further demonstrate a sharp increase in scholarly activity over the past 5 years, with the majority of studies concentrated in Europe, North America, and Asia. By identifying gaps in the existing literature and outlining priority areas for future research, this study provides both a synthesized knowledge base and practical insights for policymakers, urban planners, and industry stakeholders. The integrative perspective adopted here not only bridges disciplinary divides but also offers actionable recommendations for advancing e-scooter systems toward greater safety, inclusivity, and sustainability.

Traffic congestion is a major challenge in urban life, and traffic flow prediction, as a critical task in intelligent transportation management, can effectively alleviate congestion and enhance traffic control efficiency. Although deep learning models have achieved significant progress in traffic flow prediction, most existing studies focus on capturing dynamic spatiotemporal features, with insufficient attention to the pivotal role of historical data's periodic characteristics in forecasting. To address this, we proposed a Transformer-based Dynamic Graph Convolutional Network (T-DGCN), which significantly improves prediction accuracy by integrating historical periodic features and real-time dynamic variations. T-DGCN employs the self-attention mechanism of Transformer to capture temporal dependencies and combines dynamic graph convolutional networks to extract spatial features, thereby comprehensively modelling spatiotemporal dynamics. Experiments on the real highway datasets from California (PeMSD4 and PeMSD8) show that T-DGCN outperforms state-of-the-art models across multiple time horizons. On the PeMSD4 dataset, it reduces the MAE and RMSE by an average of 12.9% and 5.2% across the 15 min, 30 min, and 60 min prediction horizons, respectively. On the PeMSD8 dataset, it reduces the MAE and RMSE results by approximately 8.8% and 5.7% on average, respectively.