Optimizing timetables within urban rail transit networks remains a fundamental challenge, particularly when aiming to simultaneously improve energy efficiency and transfer performance. Specifically, transfer synchronization is crucial for shaping the passenger experience and overall network reliability. This study develops a biobjective model to enhance energy efficiency and transfer synchronization in URT networks. To address the computational complexity of this problem, we design a solution framework based on Lagrangian relaxation that ensures both tractability and methodological rigor. A real world case study on the large scale Xi'an network with 94 stations demonstrates the effectiveness of the proposed framework. Computational results demonstrate that the approach consistently delivers robust solutions, achieving a 28.1% reduction in energy consumption and a 23% reduction in transfer waiting time compared to the baseline timetable. Ultimately, the proposed framework provides operators with a quantitative tool to balance energy conservation and passenger service quality in complex networks.
Connected and Automated Vehicles (CAVs) platooning technology has great potential to increase road capacity, relieve traffic congestion, and improve road safety. However, the open-access VANETs vehicular network is vulnerable to malicious cyber attackers. To address this problem, a synthesis CAVs platooning control system has been developed in this paper, which integrates the Cybersecurity Attack-Defense and Anomaly-Data-Detection functions. This platooning system is composed of three subsystems, the sensors and communication subsystem, the cyber-attack detection and defense subsystem, and the decision-making subsystem. To validate the performance of the developed synthesis CAVs platooning system, a life-like simulation testing platform is developed in this study. Then, a series of numerical simulation experiments are conducted to test the performance of the developed synthesis CAVs platooning system. The experimental results reveal that the developed synthesis CAVs platooning control system can not only effectively and swiftly identify cyberattacks, but also significantly mitigate the impacts of malicious cyberattacks.
This study assesses the long-term evolution of transport hubs in the U.S.-centred aviation network operated by U.S. carriers from a sustainable hub perspective. By constructing flow-weighted and carbon-weighted networks from 1971 to 2024, we evaluate how historically formed hub structures perform across three interconnected dimensions: carbon efficiency, network efficiency and structural resilience. We find that (1) the U.S.-centred aviation network has not followed a linear or stable pathway toward improved hub sustainability, but is instead characterised by persistent trade-offs among carbon efficiency, network efficiency, and structural resilience; (2) hub-and-spoke structures have strengthened network efficiency, but have also concentrated carbon burdens at key nodes, revealing an embedded tension between connectivity and emissions; and (3) spatial heterogeneity and scenario analysis show that no single intervention improves all three dimensions, calling for coordinated technological, structural, and redundancy-based measures. These results provide insights for low-carbon spatial planning and resilience-oriented governance in aviation systems.
This study proposes a novel service platform in which operators hire privately owned electric autonomous vehicles to provide mobility services to passengers and charging services to these vehicles. The model maximises operator profits by integrating endogenous optimisation of the hired e-SAV numbers, a private-vehicle sharing mechanism, and nonlinear battery charging and discharging. To solve the problem, we tailor an Adaptive Variable Neighbourhood Search (Adaptive VNS) algorithm that integrates problem-specific operators, a charging-station insertion procedure, and a Service Start Time Optimisation (SSTO) procedure. We compare the Adaptive VNS algorithm with GUROBI, a traditional VNS algorithm, and an ALNS baseline on both small and large instances. The proposed algorithm produces competitive solutions in a much shorter time. Sensitivity analysis reveals that the effectiveness of operational strategies is scale-dependent: homogeneous fleets perform better under the tested vehicle-composition setting, while the benefits of mixed charging requirements vary with order scale.
Connected vehicles (CVs) are pivotal in transition to autonomous driving. To improve CVs car-following stability while reducing energy consumption and emissions, this paper proposes a dual-lane car-following model that systematically incorporates CV drivers' compliance with driver assistance systems and cyberattacks intensity. Through stability analysis and Hopf bifurcation analysis, this paper systematically investigates the stability conditions and stability intervals of key parameters influencing CVs performance, thereby establishing a foundation for further optimization of energy consumption. Then simulation involving 100 vehicles on circular road show that incorporation of CVs improves the anti-interference performance of heterogeneous traffic flows and reduces the amplitude of congestion fluctuations. Furthermore, energy consumption analysis based on simulation reveals that our model effectively reduces energy consumption and emissions, with both metrics decreasing as CV penetration increases. When CV penetration reaches 100%, the maximum average reductions in energy consumption, CO2 and NOx emissions reach 29.57%, 45.41%, and 66.72% respectively.
