Journal of Advanced Transportation最新文献

This is the first study to evaluate the effectiveness of the Federal Highway Administration (FHWA) roadside safety rating system in predicting Road Departure (RD) crashes on rural roads. The research employs a two-step framework: first, a computer vision model was used to extract detailed information on clear zones, rigid obstacles, side slopes, and safety barriers from roadway images. Next, the extracted data was integrated with crash records for statistical analysis. The FHWA safety rating system, which combines these features, shows a significant correlation with rural RD crash frequency and severe injury rates, as confirmed by Spearman correlation coefficients. Furthermore, using the negative binomial regression model, the safety rating emerged as the strongest predictor of rural RD crashes and their severity compared to individual roadside features, underscoring its value in assessing crash risk. With its seven categories, the FHWA safety rating system provides a more comprehensive predictor of rural RD crash risk, making it an essential tool for identifying high-risk locations and prioritizing safety interventions.

The integration of urban and rural transit networks is a prerequisite for the integration of urban and rural transportation systems. With the promotion of rural revitalization and new urbanization, the existing transit network operated separately in urban and rural areas is insufficient in meeting the travel demands of urban and rural residents. It is necessary to plan the urban and rural transit network rationally and to enhance the overall system performance of the urban and rural transit network. This paper proposes a biobjective model to optimize the integrated urban–rural transit network. The model minimizes both passengers’ and bus operators’ costs by optimizing the bus routes and frequencies simultaneously. Furthermore, we propose a subregional operations model and explore a performance comparison between the integrated and subregional optimization approaches. The genetic algorithm is developed to solve the proposed models. Finally, we conduct numerical experiments to identify the efficacy of the proposed models and algorithms. The results indicate that the integrated operation of the urban–rural transit network has more optimization space than the subregional operation, and can effectively reduce the number of transfers. Furthermore, under integrated operations, changes in operating costs have a more pronounced impact on total passenger travel time. When the demand is within a particular range, the integrated operation generates a shorter total passenger travel time than the subregional operation for the exact operating cost. In addition, the Pareto-optimal solution generated under varying interregional demands provides a trade-off between the total passenger travel time and the operating costs of the bus operator.

This study proposes a label-setting shortest-path algorithm with timetable and label constraints to address the path-planning problem in multimodal urban agglomeration transportation networks. The proposed algorithm addresses the limitations of traditional shortest-path methods, which are often challenged by complex conditions, including transfer constraints, timetable dependencies, and restrictions on the number of transfers. By analyzing the characteristics of urban agglomeration trip chains, this study constructed a multimodal network model incorporating six transportation modes: walking, buses, intercity coaches, metro, intercity railways, and private vehicles. A deterministic finite automaton was introduced to constrain feasible mode sequences, ensuring that path planning aligns with real-world travel patterns. The algorithm incorporates time-window constraints to simulate the effects of static timetables in scheduled transportation modes, such as railways and coaches. By improving the label-correcting algorithm via topological sorting and applying state-dominance rules to reduce redundant computations, it achieves optimal path planning under multiple constraints. Case studies demonstrate that the algorithm effectively balances transfer frequency and travel cost, reducing total cost by approximately 5% while ensuring feasibility, thereby validating the synergistic advantages of multimodal transportation networks. Therefore, the proposed algorithm can theoretically support multimodal traffic selection behavior or mixed traffic network modeling.

In loitering activity scenarios, vessels frequently execute course changes within localized maritime spaces, often exhibiting extreme turning maneuvers that generate ultralong, dense, and highly nonlinear spatiotemporal trajectories. Traditional prediction models demonstrate limitations in processing dynamically changing trajectory features, leading to insufficient prediction accuracy under such loitering conditions. To address this challenge, this study proposes a GL-LoiterDNet, a hybrid deep learning–based vessel trajectory prediction model. The model incorporates multidimensional trajectory characterization features including speed fluctuations, navigational positions, and course differentials. It integrates 1D convolutional neural networks (1D-CNN), bidirectional gated recurrent units (BiGRU), and bidirectional long short-term memory (BiLSTM) networks to capture both localized abrupt variations and long-term evolutionary patterns in loitering trajectories, thereby mitigating feature degradation phenomena. Experimental validation using trajectory data from vessels in Sagami Bay, Japan, demonstrates that the GL-LoiterDNet model outperforms 14 baseline models in prediction accuracy and robustness. The model exhibits rolling multistep trajectory prediction capability for loitering scenarios, achieving an average positioning error below 0.7 km within 10-min prediction windows. This research can provide reliable theoretical and data-driven support for continuous vessel positioning and monitoring in complex maritime operation scenarios.

Public health crises profoundly impact work activities and commuting behaviors. The multiple waves of COVID-19 outbreaks across cities worldwide have demonstrated that people voluntarily or involuntarily adapt their work patterns, such as shifting to remote work, and adjust their commuting choices. This study investigates changes in commuting behaviors among urban residents during three pandemic wave stages: prewave, outbreak, and postwave, focusing on work patterns, commuting frequency, and key influencing factors. A retrospective longitudinal survey was conducted in Shanghai after the first wave of COVID-19 outbreak to collect information on respondents’ work, commuting, pandemic-related, and sociodemographic characteristics. Descriptive analysis and statistical tests revealed a 35% increase in telecommuting and a 50% decrease in commuting trips during the outbreak, with near-normal levels postwave. Multinomial logit models identified key factors influencing commuting frequency, such as telecommuting, weekly working hours, and direct commutes to workplaces. Telecommuting increased commuting frequency prewave, decreased it during the outbreak, and continued to reduce it postwave. Work intensity consistently increased commuting frequency, with the most significant impact prewave and the least during the outbreak. The findings provide insights for policymakers to better understand and enhance strategies in response to unforeseen public events, including potential future pandemics like Disease X.

Anticipating traffic patterns is a vital component in efficiently managing traffic within smart cities. This proactive approach contributes to alleviating traffic congestion, promoting environmental preservation, reducing commute times, and strengthening overall safety measures. Numerous studies have verified that external factors, such as weather conditions, exert an influence on traffic patterns. Therefore, utilizing these factors as supplementary variables can improve traffic prediction accuracy. This paper presents a novel prediction model called the multi-input sequential multihead attention (MI-SMHA) model. This model integrates weather condition information into traffic prediction tasks, aiming to enhance prediction accuracy and computational efficiency. It utilizes advanced techniques from sequential modeling and attention mechanisms, specifically tailored to handle traffic and weather data such as temperature, wind speed, precipitation, visibility, and humidity. This integration aims to leverage the complementary nature of weather conditions in forecasting traffic patterns, yet it remains flexible enough to be generalized to support a wide range of multivariate time series prediction tasks. Data from real-life traffic detectors are utilized to perform experiments and comparisons with two baseline models and three state-of-the-art models to validate and assess the efficiency of the proposed model. The MI-SMHA model was efficient and reliable in forecasting future traffic flow, significantly reducing errors compared to the other models.

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.