Traffic assignment is an essential component of the traditional four-step transportation planning methodology and significantly contributes to the prediction of traffic flow distribution and optimization of traffic planning. Existing algorithms for solving the user equilibrium traffic assignment problem typically rely on equal intervals and random sampling strategies to divide a set of origin-destination (OD) pairs. However, these sampling strategies fail to address the path overlap issue among OD pairs and often depend on sensitivity analyses to partition the OD set, hindering the efficiency of task parallelism. To address this challenge, the OD grouping problem was formulated as a vertex-coloring problem, which was translated into an integer linear programming (ILP) model. The largest degree first algorithm was proposed to solve the OD grouping problem, enabling the identification of OD pairs within each block with minimal path overlap. Thereafter, the results of the OD grouping based on vertex coloring were incorporated into the parallel block coordinate descent (PBCD) method, increasing the number of OD subproblems within each block and enhancing the parallel computation. An adaptive algorithm is further proposed to address the OD-based restricted subproblem depending on the number of paths for a given OD pair. The proposed method is evaluated based on various large-scale transportation networks and compared with existing algorithms, demonstrating its effectiveness in reducing path overlap within blocks and improving the efficiency of solving traffic assignment problems in large-scale networks.

Dynamic optimization strategies for large-scale network signal control typically require real-time traffic state to deal with variable demand patterns, yet a decentralized control strategy to control the network through the combination of intersection-level control is mostly adopted, which limits the solution to local optimum to some degree. On the other hand, though centralized fixed-time controller aims at global optimum, its essence of an open-loop one-shot control depends more on prediction accuracy of traffic state, meanwhile facing a dilemma between computation efficiency and solution quality. To fill this gap, this study proposed a closed-loop online updating control strategy for large-scale network, which keeps updating the parameters of a Cycle-based Adaptive Traffic Light Control (CATLC) model through learning-based turning ratio prediction. A model predictive control strategy is further used to develop a multi-cycle extended control model considering the demand distribution of traffic progression in a larger temporal-spatial scale. Simulation evaluation on a 56-intersection network in Singapore showed that the proposed method outperformed five baselines covering fixed-time control, actuated control and adaptive control methods. Specifically, an improvement of 19% in total waiting time over the Sydney Coordinated Adaptive Traffic System (SCATS) scheme is obtained, while an superiority of 22% in average number of stops over a Deep Q Network-(DQN)-based method is obtained. The proposed GRU-based turning ratio predictor obtained an accuracy of about 84%, outperforming existing turning ratio predictors. Sensitivity analysis was also conducted regarding the control step size and the solution algorithm parameters, and results showed that a control step of 4 cycles was optimal to realize global optimum in the case study, while the solution quality is more sensitive to population number than iteration number. With satisfactory solution quality and computation cost shown in case study, the method shows great prospect in real-time traffic control for large-scale roadway networks in practice.

This paper presents a closed-loop framework for conflict-free routing and scheduling of multi-aircraft in Terminal Manoeuvring Areas (TMA), aimed at reducing congestion and enhancing landing efficiency. Leveraging data-driven arrival inputs (either historical or predicted), we formulate a mixed-integer optimization model for real-time control, incorporating an extended TMA network spanning a 50-nautical-mile radius around Changi Airport. The model enforces safety separation, speed adjustments, and holding time constraints while maximizing runway throughput. A rolling-horizon Model Predictive Control (MPC) strategy enables closed-loop integration with a traffic simulator, dynamically updating commands based on real-time system states and predictions. Computational efficiency is validated across diverse traffic scenarios, demonstrating a 7-fold reduction in computation time during peak congestion compared to onetime optimization, using Singapore ADS-B dataset. Monte Carlo simulations under travel time disturbances further confirm the framework's robustness. Results highlight the approach's operational resilience and computational scalability, offering actionable decision support for Air Traffic Controller Officers (ATCOs) through real-time optimization and adaptive replanning.

The increasing frequency of extreme events presents significant challenges to the security and reliability of electric-hydrogen energy systems, where traditional optimal sizing methods often fail to address supply–demand imbalance risks under typical conditions. To bridge this gap, in the present study, a comprehensive multi-objective optimization framework that integrates extreme scenario generation with preference-information decision-making is proposed. First, an integrated generative adversarial network-based method is developed to synthesize diverse scenarios while preserving long-duration temporal dependencies and accurately capturing wind-solar-load correlations. A two-layer multi-objective optimization model is subsequently established to coordinate short- and long-duration energy storage dispatch across both normal and extreme scenarios, thereby increasing the economic viability and reliability of the system. Furthermore, an angle preference-based multi-objective particle swarm optimization algorithm is introduced to incorporate the preferences of decision-makers and yield flexible and tailored optimal sizing solutions. The evaluation results demonstrate that the proposed integrated GAN scenario generation method reduced the training losses by 60.97% in comparison with other traditional methods. Additionally, the overall optimization costs for the extreme and normal scenarios were 1.08% and 12.5% lower, respectively, than those for the same scenarios without preference optimization. Finally, the effectiveness and scalability of the proposed methodology were validated within an extended electric-thermal-hydrogen system on historical extreme high-temperature scenario data.

Estimated time of arrival (ETA) for airborne aircraft in real-time is crucial for arrival management in aviation, particularly for runway sequencing. Given the rapidly changing airspace context, the ETA prediction efficiency is as important as its accuracy in a real-time arrival aircraft management system. In this study, we utilize a feature tokenization-based Transformer model to efficiently predict aircraft ETA. Feature tokenization projects raw inputs to latent spaces, while the multi-head self-attention mechanism in the Transformer captures important aspects of the projections, alleviating the need for complex feature engineering. Moreover, the Transformer's parallel computation capability allows it to handle ETA requests at a high frequency, i.e., 1HZ, which is essential for a real-time arrival management system. The model inputs include raw data, such as aircraft latitude, longitude, ground speed, theta degree for the airport, day and hour from track data, the weather context, and aircraft wake turbulence category. With a data sampling rate of 1HZ, the ETA prediction is updated every second. We apply the proposed aircraft ETA prediction approach to Singapore Changi Airport (ICAO Code: WSSS) using one-month Automatic Dependent Surveillance-Broadcast (ADS-B) data from October 1 to October 31, 2022. In the experimental evaluation, the ETA modeling covers all aircraft within a range of 10NM to 300NM from WSSS. The results show that our proposed method method outperforms the commonly used boosting tree based model, improving accuracy by 7% compared to XGBoost, while requiring only 39% of its computing time. Experimental results also indicate that, with 40 aircraft in the airspace at a given timestamp, the ETA inference time is only 51.7 microseconds, making it promising for real-time arrival management systems.

The integration of distributed photovoltaic (PV) has become a prominent characteristic of distribution network (DN). However, achieving 100% PV self-consumption poses challenges due to intermittent generation and varying load demands. To address these challenges, this paper proposes an innovative joint planning method for PV, stationary energy storage system (SESS), and mobile energy storage system (MESS) using the consensus-based alternating directional multiplier method (ADMM). First, to account for multiple scenarios with real-world variations, a two-stage clustering method with clustering by fast search and finding of density peaks and k-means is employed. Second, a joint planning model for PV, SESS, and MESS is developed to minimize the total cost of DN, including installation, operation, and maintenance costs. The model optimizes the PV capacity, SESS capacity, and MESS capacity, as well as their locations. Third, a distributed planning framework is proposed using consensus-based ADMM, which decomposes the problem into a PV planning subproblem under the worst-case scenario and energy storage system planning subproblems under multiple scenarios. Finally, the proposed method is demonstrated on an IEEE-33 node system, and the results show the method effectively enhances the access capacity of PV by 11.80% and leads to a reduction in the total cost.

Aircraft trajectory prediction aims to estimate the future movements of aircraft in a scene, which is a crucial step for intelligent air traffic management such as capacity estimation and conflict detection. Current approaches primarily rely on inputting absolute locations, which improves the prediction accuracy but limits the model’s generalization ability to unseen environments. To bridge the gap, we propose to alternatively learn aircraft’s intentions from a repository of historical trajectories. Based on the observation that aircraft traveling through the same airspace may exhibit comparable behaviors, we utilize a location-adaptive threshold to identify nearby neighbors for a given query aircraft within the repository. The retrieved candidates are next filtered based on contextual information, such as landing time and landing direction, to eliminate less relevant components. The resulting set of nearby candidates are referred to as the local history, which emphasizes the modeling of aircraft’s local behavior. Moreover, an attention-based local history encoder is presented to aggregate information from all nearby candidates to generate a latent feature for capturing the aircraft’s intention. This latent feature is robust to normalized input trajectories, relative to the current location of the target aircraft, thus improving the model’s generalization capability to unseen areas. Our proposed intention modeling method is model-agnostic, which can be leveraged as an additional condition by any trajectory prediction model for improved robustness and accuracy. For evaluation, we integrate the intention modeling component into our previous diffusion-based aircraft trajectory prediction framework. We conduct experiments on two real-world aircraft trajectory datasets in both towered and non-towered terminal airspace. The experimental results show that our method captures various maneuvering patterns effectively, outperforming existing methods by a large margin in terms of both ADE and FDE.

Aircraft landing time (ALT) prediction is crucial for air traffic management, especially for arrival aircraft sequencing on the runway. In this study, a trajectory image-based deep learning method is proposed to predict ALTs for the aircraft entering the research airspace that covers the Terminal Maneuvering Area (TMA). Specifically, the trajectories of all airborne arrival aircraft within the temporal capture window are used to generate an image with the target aircraft trajectory labeled as red and all background aircraft trajectory labeled as blue. The trajectory images contain various information, including the aircraft position, speed, heading, relative distances, and arrival traffic flows. It enables us to use state-of-the-art deep convolution neural networks for ALT modeling. We also use real-time runway usage obtained from the trajectory data and the external information such as aircraft types and weather conditions as additional inputs. Moreover, a convolution neural network (CNN) based module is designed for automatic holding-related featurizing, which takes the trajectory images, the leading aircraft holding status, and their time and speed gap at the research airspace boundary as its inputs. Its output is further fed into the final end-to-end ALT prediction. The proposed ALT prediction approach is applied to Singapore Changi Airport (ICAO Code: WSSS) using one-month Automatic Dependent Surveillance-Broadcast (ADS-B) data from November 1 to November 30, 2022. Experimental results show that by integrating the holding featurization, we can reduce the mean absolute error (MAE) from 82.23 seconds to 43.96 seconds, and achieve an average accuracy of 96.1\%, with 79.4\% of the predictions errors being less than 60 seconds.

In this paper, we proposed a conflict-free routing strategy combined with scheduling for Terminal Manoeuvring Area (TMA) multi-aircraft to guarantee a safe separation. By incorporating Standard Terminal Arrival Routes (STARs) as route constraints, a mixed-logic model is designed to maximize the runway throughput while ensuring minimum separation between aircraft and avoiding overtaking on each STARs segment. Control techniques such as speed recommendation and holding operations are employed to the model to address potential conflicts. Three different algorithms are developed to solve the model: branch and bound with mixed-integer linear programming, multi-agent pathfinding with constraint programming, and meta-heuristics with evolutionary neighborhood search. These algorithms are tested on multiple cases of varying scales. Finally, we demonstrate the advantages of the proposed three algorithms by simulating realistic scenarios and comparing the results with Singapore ADS-B (Automatic dependent Surveillance-Broadcast) historical dataset. In one hour testing, results show that our method could reduce the last aircraft landing time nearly 10 minutes and save more than 80 minutes for total flight travel times for all aircraft, as well as non-vectoring flight trajectories, which indicates its potential to be used as an auxiliary decision-making tool for Air Traffic Controllers (ATCOs).

Aircraft trajectory prediction aims to estimate the future movements of the aircraft, which is a crucial step for air traffic management such as capacity estimation and conflict detection. In this paper, we present a context-aware trajectory prediction method, which generates the future movements based on both the aircraft's past status and the contextual information such as the pilot and controller intent and the environmental conditions. The proposed framework consists of 1) a Trajectory Encoder that captures the history behaviors and the social interactions of the aircraft, 2) a Context Encoder that extracts latent features from contextual information, and 3) a Transformer-based Decoder that generates future trajectories based on a diffusion model. Specifically, we model the trajectory prediction as the reverse diffusion process where we first gradually add noise to the ground-truth trajectory and then train a neural network to learn the reverse of this diffusion process conditioned on the output of the trajectory encoder and the context encoder. We conduct experiments on real-world aircraft trajectories collected at Singapore Changi Airport in December 2019, which correspond to one-week ADS-B data before the start of the COVID-19 pandemic. The experimental results show that our proposed approach outperforms existing methods by a significant margin.

The increasing demand for air travel combined with uncertainties has put additional strain on airport infrastructure and ground handling resources. To improve the efficiency of airport operations, in this paper, We firstly perform an in-depth analysis of the A-SMGCS dataset for Singapore Changi Airport, and then propose taxiing routing solutions for both landing and departure aircraft with involved departure uncertainty, formulating the problem as a mixed-integer quadratic programming (MIQP) problem. The proposed model considers the waypoint- based conflict checking, as well as incorporates anti-overtaking constraints, and head-on constraints exclusive for bidirected graph, aiming to minimize the taxiing time as well as the gap with the scheduled in-block time for landing aircraft and scheduled take-off time for departure aircraft. The presence of departure uncertainty prompts us to build a stochastic model, where constraints with stochastic variables are converted into corresponding chance constraints under designated confidence levels. Incorporating stochastic factors enhances the resilience and dependability of our solution. To evaluate the efficiency of our proposed method, we have elaborately investigated the computational complexity under various test scales, and analyzed how changes in uncertainty and confidence levels impact routing and scheduling solutions on a simplified Singapore Changi Airport network, which could provide significant reference for other work.

The degree of coupling between information and energy is increased, leading to a cyber-energy system. The amount of information carried by communication networks is expanding, and if a cyber system fails or operates abnormally, the defect may propagate across space to the entire system. In this paper, a routing optimization method of cyber-energy systems is proposed to optimize information operations and respond to cyber attacks. To account for the difference in information demand between electricity and gas, we describe the communication business and determine the business's importance using the fuzzy analytic hierarchy approach. The risk assessment model is improved by analyzing the structural vulnerability of the energy system based on complex network theory. To coordinate various conditions, the approach is split into preventative and emergency control modes. The Yen algorithm and the fuzzy membership function are coupled in the preventative control mode to simultaneously optimize load balancing and communication risk. In the event of cyber attacks, the emergency control mode is triggered, which employs emergency isolation to minimize fault propagation and to preserve system stability in a partially disconnected operating state. Finally, the method's effectiveness is illustrated through using IEEE 30 bus and gas 13 node coupling system.

Energy Storage System (ESS) has been utilized extensively to manage the uncertainty of renewable energy output and load demand. To utilize ESS more effectively, the concept of shared energy storage system (SESS) is proposed. This paper proposes an adjustable robust optimization model for microgrid cluster, taking into consideration the uncertain renewable energy and load. The robust adjustment parameter Γ is introduced to adjust the conservative degree of the model. According to the predicted WT and PV outputs, load demand and given Γ, the reserve capacity and pre-scheduling scheme of microgrid cluster are solved. By simulating the actual fluctuation of uncertain parameters and re-scheduling, the robustness and economy of pre-scheduling schemes under different Γ are verified. Simulation results demonstrate that the adjustable robust optimization model is superior to both the deterministic optimization model and the static robust optimization model. Furthermore, comparing the operating costs of each microgrid with ESS and the microgrid cluster with SESS, it is proved that SESS not only reduces the charging and discharging frequency of ESS, but also decreases the requirement for reserve capacity, thereby reducing the operation cost.

A novel two-stage sequential disaster recovery strategy for resilient cyber-physical distribution power systems (CPDPSs) is proposed with consideration of cyber-physical collaborative optimization. In the first stage, a mixed-integer linear programming (MILP) model is formulated based on the integration of a transportation network (TN), a cyber network (CN) and a physical network (PN) to achieve efficient load recovery. Additionally, a 'repair-event-triggered' mechanism is proposed to facilitate effective and collaborative optimization among physical maintenance crews (PMCs), cyber maintenance crews (CMCs), and a distribution network recovery model (DNRM). In the second stage, PMCs and CMCs are rescheduled to repair the remaining damaged components with the lowest time cost after all the power supplies are restored. The effectiveness and applicability of this proposed model are validated using the IEEE 33 bus system and the PG&E 69 bus system.

Due to the coupling characteristics of the physical system and the communication system, distribution network fault scenarios and post-disaster recovery procedures become more complicated in the aftermath of catastrophic natural disasters. Consideration of communication system restoration by unmanned aerial vehicle base station (UAV-BS) can effectively reduce distribution network outage duration. This paper proposes a post-disaster recovery strategy for distribution networks that takes UAV-based communication recovery into account. Consideration is given to the cooperation of multiple UAV-BSs in the recovery process using multi-agent reinforcement learning (MA-RL). The communication recovery procedure of multiple UAVs is initially converted into a Markov decision process (MDP). To actualize agent interaction, the distribution network reconfiguration model is constructed as a reinforcement learning environment that takes into account communication constraints. The problem is resolved by MA-RL, and the effectiveness of the proposed strategy is evaluated by IEEE 33-bus system.

Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With accurate EV station availability prediction, suitable charging behaviors can be scheduled in advance to relieve range anxiety. Many existing deep learning methods have been proposed to address this issue; however, due to the complex road network structure and complex external factors, such as points of interest (POIs) and weather effects, many commonly used algorithms can only extract the historical usage information and do not consider the comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatiotemporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both the external and internal spatiotemporal dependence of relevant transportation data. The external factors are modeled as dynamic attributes by the attributeaugmented encoder for training. The AST-GIN model was tested on the data collected in Dundee City, and the experimental results showed the effectiveness of our model considering external factors' influence on various horizon settings compared with other baselines.

Due to the frequent occurrence of extreme natural disasters, the distribution network is vulnerable to significant effect, resulting in partial power supply interruption and the formation of islands. Emergency power supply vehicles (EPSVs) can temporarily restore power supply to the islands during the outage, and repair crews (RCs) are sent to repair faulted lines. To accelerate fault recovery, a two-stage optimization recovery strategy for the distribution network is proposed in this paper. In the first stage, the distribution network reconfiguration model is established to maximize the critical load recovery. In the second stage, according to the reconfiguration result, EPSVs select the islands with the highest emergency degree of failure load to provide power supply. In the meantime, RCs pick the island which still has more load loss to repair the fault line after power supply restoration. Mobile emergency resources (MERs) are continuously sent depending on the results of a two-stage rolling optimization to promptly restore power supply to all loads. The dispatching routes of MPSs considering the dynamic traffic network models are optimized by using Floyd algorithm. The reliability and effectiveness of the proposed strategy are verified by simulation of the IEEE 33- node system.

Particle filter (PF) is an effective state estimation method for nonlinear unbalanced distribution systems with non-Gaussian noises. However, the computational inefficiency limits its real-time application in large-scale distribution systems. This article proposes an adaptive PF (APF) method by using randomized quasi-Monte Carlo (RQMC) sampling to enhance computational efficiency. First, an RQMC sampling method is introduced to enable uniform sampling based on the low-discrepancy Sobol’ sequence. This even sampling accelerates the convergence rate of numerical integrations. Second, the standard PF is extended to be an RQMC-PF estimator by using the RQMC sampling instead of the MC sampling. Benefiting from the higher convergence rate, fewer particles are required to obtain precise state estimates, thus notably improving computation efficiency. Third, to further make a tradeoff between estimation accuracy and computational efficiency, the number of particles used in the RQMC-PF is adaptively determined with the root mean square of the estimated state covariance, yielding an APF. Test results on unbalanced distribution systems with different scales demonstrate that the proposed method can efficiently provide accurate estimates as well as achieve good scalability.