Journal of Advanced Transportation最新文献

This study investigates key factors influencing the immersive experience of passengers in autonomous vehicles (AVs) and proposes a novel theoretical model. In this model, four core dimensions were identified: (1) Emotional and Sensory Experience, (2) Interaction and Engagement Experience, (3) Trust and Safety Experience and (4) Dispositional Experience. It is found that Emotional and Sensory factors, such as lighting and sound, primarily affect passenger comfort and mood. In contrast, the Interaction and Engagement factors that focus on human–machine interaction (HMI) and AR/VR devices enhance passengers’ engagement. As for trust and safety factors, passengers’ confidence towards AVs is addressed through clear communication during driving processes. Dispositional factors, including technology acceptance and personalisation, contribute to passengers’ overall satisfaction in AVs. In addition, external factors such as intelligent transportation systems (ITSs), intelligent connected vehicles (ICVs) and smart city infrastructure further impact passengers’ experiences in safety and efficiency. The study highlights several emerging research areas requiring further investigation, such as multisensory feedback, dynamic personalisation and cultural inclusivity differences in AV experience. The proposed theoretical model serves as a foundation for future work aimed at enabling the design of AV systems that are more attentive and accommodating to passengers by sourcing control both domestically and externally, ultimately enhancing the passengers’ experience.

Safety anomalies are the early warning and precursors to major accidents. Preventing such incidents requires robust accident models to identify and mitigate risk factors. This study enhances the system hazard identification, prediction, and prevention (SHIPP) model to develop a safety barrier–based accident analysis framework tailored to high-speed train operations. The proposed model employs a fault tree to represent the causal relationships among various safety barriers and an event tree to depict the progression from safe operation to catastrophic outcomes. To validate the approach, the study collects a total of 60 cases of operational safety accidents and reconstructs the accident process comprehensively. The causal relationships among contributing factors are visualized clearly, providing a foundation and technical support for accident process analysis and the formulation of preventive measures.

High-speed railway (HSR) operations heavily rely on visual inputs, yet there is a notable gap in examining how HSR drivers adjust their eye movements in response to different lighting conditions, despite the pivotal role visual cues play in such environments. This investigation employed a Tobii Nano eye-tracker to capture the visual behaviors of HSR drivers during simulated driving exercises. It centered on 4 areas of interest (AOIs): the front window, prompt area, dashboard, and speed dial. By using Train Sim World 3, we created 3 scenes (open section, short tunnel, and long tunnel) and utilized 4 key metrics (average pupil diameter, APD; time to first fixation, TFF; duration of first fixation, DFF; fixation duration, FD) to evaluate the variations in visual attention patterns of HSR drivers. The results reveal a significant relationship between these indicators and driving scenes. Drivers in tunnel settings tend to have a longer time to form fixations, reflected by longer TFF, duration of first fixation (DFF), and FD. Pupil dilation is most pronounced in tunnels with weaker light (long tunnels), while stronger light (short tunnels) leads to increased TFF, DFF, and FD. At the outset of the driving task, the front window and speed dial are the earliest fixated (earlier TFF). Throughout the driving, speed dial continues to be a central fixation, manifested by extended DFF and FD. Gaining insight into HSR drivers’ visual behaviors is essential for enhancing driving safety.

With the continuous growth of high-speed railway passenger transportation demand, how to improve the capacity has become an urgent problem to be solved. The signal system based on moving block can effectively improve the utilization of line capacity. From the perspective of signal system, this paper studies the line capacity benefits brought by CTCS-3 combined with moving block. First, in response to the challenges of implementing moving block under CTCS-4 based on existing technologies and considering the need for line interconnection, this paper proposes a CTCS-3 solution that combined moving block. Secondly, this paper proposes a multiagent-based high-speed railway network train tracking simulation modeling method and establishes infrastructure and train simulation models under two signal system scenarios: CTCS-3 and CTCS-3 combined with moving block. Finally, this paper selects the Beijing-Shanghai High-Speed Railway as a research case and verifies the railway capacity indicators. The results show that the application of CTCS-3 combined with moving block is expected to further tap the transportation capacity potential of the existing high-speed railway network.

Confronted with the disturbances arising from various risk events, it is crucial to accurately measure the severity of risks in the dispatching section for efficient train operation and transportation management of a high-speed railway (HSR). This paper proposes a risk mapping method for daily HSR disturbances based on a self-formulated operation loss model, aiming to assist in identifying the spatiotemporal transportation bottlenecks and mitigating the propagation of risks. The calculation models for operation loss under risk disturbances are first established, with a focus on the instantaneous operation loss (IOL) of affected trains and the cumulative operation loss (COL) of the dispatching section, giving specific considerations on delay status, train importance, and operation scheme. Based on the delay characteristics observed in various risk scenarios, the variation curves of IOL for affected trains and dispatching sections are categorized into triangular and trapezoidal patterns. Combining the historical data statistics, the spatiotemporal risk distribution matrix is then established by occurrence probability calculation, event probability decomposition, and grid operation loss calculation, using well-designed algorithms. Meanwhile, the importance of risk scenario features is analyzed through LightGBM classification to identify key attributes. To validate the feasibility of the proposed approach, a case study has been conducted on weekday risk disturbances in a dispatching section administrated by the Shanghai Railway Bureau. The results demonstrate that this approach can accurately depict the distribution of risk severity by considering both operation losses and decomposed probabilities, where the average COL of station risks ranges from 0.14 to 0.64, while the average COL of section risks ranges from 0.09 to 0.49. Furthermore, the attributes contributing to the risk severity can be effectively extracted for various scenarios, such as the primary delay, risk position, and train speed heterogeneity. Finally, a discussion on the generalizability and challenges of applying this method provides further verification and detailed explanations for HSR risk mapping.

This study examined underground roads to evaluate the effects of traffic congestion prevention strategies. A specific framework, called the traffic congestion judgment criteria and process (TJCAP), was developed for underground road application. Using this framework, the study analyzed congestion relief effects by applying traffic strategies commonly used on surface roads. A real underground road in Seoul was used as a testbed. Microscopic traffic simulation was conducted using the VISSIM to create a realistic simulation network. The model was calibrated using observed traffic volume and speed data, both on the underground and adjacent surface roads. This approach enabled the analysis of traffic strategies aimed at reducing congestion. Results showed that the effectiveness of the strategies depends on the type of surface road (interrupted or uninterrupted flow) and its traffic conditions. In particular, the strategies were effective when the connected surface road had a level of service (LOS) of D or better.

Traffic flow forecasting, as a crucial component of intelligent transportation systems (ITS), enables the prediction of future traffic conditions based on historical traffic data, thereby optimizing travel strategies and achieving the goal of reducing traffic congestion. Considering the limited nature of specific road network spatial structures, specific road network datasets often overlook the influence of surrounding networks on the network itself, motivating the need for a framework that captures boundary interactions. This paper introduces the bidirectional spatial–temporal expanded graph convolutional model (Bi-STEGCM) to traffic flow forecasting. This addresses the limitations of conventional models, particularly in capturing spatial features and managing missing or anomalous data. The Bi-STEGCM reconstructs and aggregates traffic data while preserving the temporal dynamics of traffic flow. This offers a more nuanced representation of the spatiotemporal dynamics within road networks. The model utilizes causal convolution for temporal feature extraction and an auto-regressive moving average (ARMA) filter for spatial feature extraction. It integrates these with bidirectional graph convolution to aggregate spatial features across various layers. Validation using real-world traffic datasets PEMS03, PEMS04, PEMS07, and PEMS08 demonstrates that the Bi-STEGCM outperforms state-of-the-art models, including spatial–temporal synchronous graph convolutional networks (STSGCN) and spatial–temporal fusion graph neural networks (STFGNN), across three key evaluation metrics. Notably, the Bi-STEGCM requires significantly fewer parameters and less training time than its counterparts, rendering it a more efficient and effective solution for traffic flow forecasting tasks.

Urban air mobility (UAM) helps to revolutionize intra- and intercity transportation systems and fosters a more sustainable future. Prior research has primarily concentrated on consumers’ adoption of UAM from the perspective of technology acceptance and diffusion, overlooking the crucial dimension of innovation resistance. This study addresses this oversight by integrating the stimulus–organism–response (SOR) framework with the innovation resistance theory (IRT). Specifically, it employs personal innovativeness and environmental awareness as moderating variables and negative attitude as a mediation factor. An online survey in 2024 in China, and 695 valid responses were used to test the proposed hypotheses. The results indicate that usage barriers, value concerns, risk perceptions, and traditional norms are significantly and positively correlated with negative attitudes, ultimately leading to a diminished intention to adopt UAM. Notably, personal innovativeness and environmental awareness mitigate the impact of risk perceptions and traditional norms on these negative effects. The findings of this study contribute to the understanding of consumer resistance toward UAM and provide valuable insights for scholars and marketers in devising strategies to overcome these barriers and facilitate the adoption of UAM systems.

High-speed railway systems face increasing operational challenges due to rising passenger demand and complex infrastructure constraints. However, traditional space-time network models for train timetabling may lack detailed representation of real-world incompatibility constraints, limiting their practical applicability. This study proposes an extended space-time network that explicitly incorporates train headway constraints through enhanced incompatibility modelling. The model classifies section arcs into eight operation-specific types based on train movements at adjacent stations, enabling precise representation of distinct headway constraints. To address the limitations in existing arc incompatibility descriptions, two novel concepts are introduced: N-incompatible arc sets and pairwise N-incompatible arc sets. A 0-1 integer programming formulation is developed to maximize train timetabling profits while strictly enforcing all headway and station capacity constraints. For large-scale problems, a Lagrangian relaxation algorithm with model reformulation techniques is proposed to efficiently solve real-world instances. Computational experiments on the Beijing–Shanghai high-speed railway line demonstrate the model’s ability to generate conflict-free timetables within acceptable computation time. This work enhances the conceptual framework of incompatibility modelling and bridges the gap between theoretical models and practical timetable generation by explicitly capturing heterogeneous train operations and intricate incompatibility relationships.

In this paper, a multiaircraft path planning method framework for autonomous operation and distributed decision-making was proposed. The core content of this framework consists of two parts: single-aircraft path planning and multiaircraft path coordination. The path planning process includes airspace operational situation assessment, initial path generation based on operational situation, path optimization, and smoothing. A joint path planning algorithm of artificial potential field (APF) and particle swarm optimization is designed to overcome the inherent defects of the APF method and optimize the path to make it more resistant to disturbance. In the process of multiaircraft route coordination, a mixed strategy game model is constructed to promote the fair allocation of airspace resources among aircraft. The mathematical properties of the mixed strategy Nash equilibrium solution for this problem are presented. Finally, a simulation scenario is constructed based on the actual sector structure (ZSSSAR01) and running data to verify the effectiveness of the proposed method. The simulation results show that with the increasing proportion of aircraft operating in the autonomous mode, the length of the planned path increases first and then decreases, the airspace operation situation is gradually balanced in the spatial distribution, and the robustness of the planned path is gradually enhanced. The average path length of aircraft increases only by 9.15%, but the peak air traffic complexity can be reduced by 34.77%, and the number of highly utilized grids in airspace can be increased by 22.55%. And, the anti-disturbance capability of this path is significantly improved. It proves that the multiaircraft distributed route planning method proposed in this paper has a good application prospect in future air traffic management.