Journal of Advanced Transportation最新文献

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.

Modeling passenger route choices is crucial for analyzing and predicting public transportation demand. One of the most popular methods is to use probabilistic route choice (PRC) models (also known as discrete choice models in general), which have broad applications in transportation, economics, politics, and other fields. However, its performance varies depending on the characteristics of the origin–destination (OD) trip data and should be examined carefully. This paper proposes a framework for validating the PRC model on its application to urban rail transit (URT) networks containing small-scale OD trip data. The concept of small-scale data is defined at first for each OD pair considering the desired confidence level and the variance of route choices. Then, a travel time range (TTR)-based method is put forward to deduce passengers’ actual route choices as a benchmark for verifying PRC models. The difference and regularity analysis between the actual route choices and the model predictions are also performed with a twofold comparison. A case study on the Nanchang metro in China shows that the actual daily passenger volumes on routes of small-scale OD pairs diverge remarkably from the estimations of the PRC model. The PRC model’s performance is further discussed when the small-scale OD trip data accumulate to a larger scale over multiple days (e.g., several months). This study reveals the inherent limitation of PRC models in estimating the travel behaviors of passengers in a small-scale population. Several practical implications are discussed to improve the route choice model and passenger flow analysis.

This study proposes an integrated framework that combines real-time simulation with offline optimization to evaluate and enhance the operational performance of demand-responsive transit (DRT) systems. Using the Kalamazoo Metro DRT as a case study, the Transportation Analysis and Mobility Optimization System (TAMOS) is employed to replicate dynamic booking behavior and vehicle dispatch logic. These real-time operations are benchmarked against a static capacitated vehicle routing problem with time windows (CVRPTW), solved using Google OR-Tools (v9.6) with the PARALLEL_CHEAPEST_INSERTION strategy to minimize fleet mileage while respecting vehicle capacity and time window constraints. Results show that the current fleet of 41 vehicles achieves a 74% service rate with an average pickup delay of 19.6 min. In contrast, the optimized CVRPTW solution fulfills 100% of trip requests with only 22 vehicles, assuming a relaxed pickup delay of 10 min. However, reducing the allowable delay to 5 min lowers trip feasibility to 65%, underscoring the operational sensitivity to temporal thresholds. The dual-model approach illustrates how integrating real-time simulation with optimization can quantify trade-offs between service quality and operational efficiency. Additionally, the study introduces several enhancements to the OR-Tools solver, including dynamic time windows, passenger-level detour constraints, and integration with the Google Maps API for real-world travel time matrices, improving model realism and decision relevance. The proposed framework is adaptable to various urban contexts and scalable across international settings, offering practical guidance for transit agencies in fleet sizing, delay tolerance, and service design under dynamic demand conditions.

Road network connectivity is an important indicator for measuring the operational efficiency and reliability of urban road networks, and it plays an important role in supporting traffic planning and management decisions. The implementation of traffic management measures, such as traffic bans and temporary traffic flow changes, will restrict access to some sections and lanes, reduce the passable paths in the road network, and thus affect the overall connectivity performance of the road network. Existing road network research results mostly evaluate the topological connectivity of the network at the physical level, and it is difficult to accurately portray the actual road network connectivity under traffic management conditions. To quantitatively evaluate the road network connectivity performance after the implementation of traffic management tools, this paper proposes a road network connectivity evaluation method based on strongly connected effective paths. Firstly, the node steering coefficients are used to describe the no-traffic constraints of turning lanes, and the connectivity evaluation indexes are constructed based on the number of strongly connected effective paths and the shortest paths of strongly connected paths. Secondly, combining the Floyd-Warshall algorithm and the depth-first search algorithm, we constructed a strong connectivity effective path search algorithm to adapt to the refined traffic management situation, and identified the key road sections that have the greatest impact on the connectivity of the road network by considering the maximum acceptable level of the path and the road access constraints. Finally, Sioux-Falls network and nine urban road networks with different layout patterns are selected for the case study and compared with traditional road network connectivity indicators. The case studies show that: (1) the connectivity of the square grid road network structure is superior, while the connectivity of the free-form road network is the lowest; (2) road access management measures reduce the overall road network connectivity, and the banning of traffic in critical sections has the most significant effect on connectivity. Accurately assessing the changes in road network connectivity performance under different traffic management measures provides a scientific basis for the development of road control strategies, which can effectively improve urban traffic fluency and residents’ travel efficiency.