The cross-line operation (CO) of trains in urban rail transit is an effective method to efficiently satisfy transfer passenger travel demand as well as relieve the pressure of transfer stations. The primary problem of CO is designing train services to satisfy travel demand with an uneven spatial distribution of passengers. This study constructs a nonlinear integer programming model with a novel train operation scheme, i.e., virtual coupling (VC) technology, which allows the coupling/decoupling of trains on different lines at both ends of each operation zone. This scheme makes the train capacity equitably distributed in each operation zone, thereby balancing train capacity utilization over the whole CO system. Regarding the nonlinear characteristics of the proposed model, an adaptive simulated annealing genetic algorithm (ASA-GA) was designed to quickly generate high-quality solutions. Based on real-world data from the Beijing Changping Line and Line 13, the effectiveness of the proposed model and algorithm were verified. The computation results show that in comparison to a single grouping train composition scheme without CO, a VC scheme with CO would reduce operation costs by 46.8%, with 80.6% savings of train capacity equity. Furthermore, the average passenger residence time would be reduced by 25.9%.

Network attacks on the Internet of Vehicles (IoV) can lead to catastrophic consequences such as traffic congestion, incorrect routing, and even accidents. Existing rule-based countermeasures are effective only in specific scenarios, while machine learning-based methods suffer from suboptimal performance due to data quality issues. Furthermore, the isolation between in-vehicle networks (IVN) and external vehicle networks (EVN) prevents independent intrusion detection systems (IDS) from detecting continuous attack behaviors. To address these issues, we propose a mechanism that characterizes attack behaviors solely based on network connection information. This mechanism downplays the specifics of the traffic itself, allowing for the integration of IVN and EVN through structure, thus breaking the internal-external boundary and providing relatively stable logical structure information. This approach also offers opportunities to address the scarcity of labeled data. Based on this characterization mechanism, we develop an intrusion detection system – One$^{2}$, utilizing a multi-attribute heterogeneous graph transformer to achieve accurate multi-classification of various types of attacks. To assess the compatibility of the proposed IDS for IVN and EVN, extensive experiments were conducted using six real-world datasets that accurately depict IVN and EVN. The results show that One$^{2}$ improves average accuracy by 7.14% and F1 score by 6.97% compared to state-of-the-art methods. The source code of this work is available at: https://github.com/LouHerGetUp/One2