The progression of low-carbon aviation policies and the maturation of electric vertical take-off and landing (eVTOL) technology have engendered considerable prospects for the advancement of short-haul intercity and intra-city transportation systems. To harness the potential of eVTOL travel in ameliorating transportation carbon emissions and alleviating ground transportation congestion, the judicious selection of optimal eVTOL stop sites emerges as a pivotal consideration. This study delineates a framework for the delineation of intra-city and short-distance inter-city eVTOL site selection predicated on comprehensive analysis of ground transportation system interconnections. The initial phase of the framework entails the identification of potential optimal take-off and landing sites through a multi-faceted assessment of factors encompassing vehicular and passenger traffic flows, regional economic dynamics, travel behavioral patterns, and prevailing eVTOL flight regulations across heterogeneous ground transportation networks. Employing an enhanced iteration of the K-means algorithm, this phase undertakes the clustering of optimal takeoff and landing locations, thereby discerning their spatial distribution to effectively alleviate ground traffic congestion while aligning with eVTOL vertical port requirements and airspace regulatory mandates. The second phase involves the establishment of a demand gravity model to validate the optimal take-off and landing coordinate sites of eVTOL and further assess a service index indicative of traffic flow optimization. The case shows that six optimal eVTOL take-off and landing locations have been discerned by our model within the Beijing-Tianjin-Xiong'an (Hebei) region. These locations are anticipated to yield a cumulative service index of 75,465 instances, thereby efficaciously mitigating travel pressure on ground transportation infrastructure.

Complex and diverse multi-attribute flight data can provide data-driven opportunities for airport flight delay prediction. However, it is a challenge to effectively and efficiently process multi-attribute flight data. This paper proposes a hybrid dynamic spatial-temporal long short-term memory network (LSTM) with 3D-directional multi-attribute features (3DF-DSCL) for departure flight delay prediction. The model is based on a 3D convolutional neural network (3D-CNN), graph convolutional network (GCN) and long short-term memory networks (LSTM) model. Firstly, the dataset divides the state and environment of departure flight delay into three situations, including the dynamic operation link, which integrates the trajectory system of aircraft movement in the terminal area, the network congestion link caused by aircraft multi-area movement in the air and ground, and other delay factors determined by the airport take-off and landing requirements. Multi-attribute data are divided into time series, spatial-temporal network and dynamic moving trajectory grid input variables. Among them, the spatial network and dynamic moving trajectory grid data are the inputs of GCN and 3D CNN models, which aim to extract spatial-temporal features. The time series input variables are fed into LSTM. These features are then integrated and fed into LSTM for flight delay prediction, where the flight delay of airport outbound flights is taken as the output variable. The case study shows that the proposed method can significantly improve the accuracy of flight prediction delay. The Mean Absolute Error (MAE) can reach 0.26, which is a 14.47% reduction compared with 2D CNN+GCN+LSTM.

As the value and importance of urban air mobility (UAM) are being recognized, there is growing attention towards UAM. To ensure that urban air traffic can serve passengers to the greatest extent while ensuring safety and generating revenue, there is an urgent need for a transportation scheduling plan based on safety considerations. The region of Beijing–Tianjin–Hebei was selected as the case study in this research. A real-time demand transportation scheduling model for a single day was constructed, with the total service population and total cost as objective functions, and safety intervals, eVTOL performance, and passenger maximum waiting time as constraints. A Joint Optimization of Cost and Scheduling Particle Swarm Optimization (JOCS-PSO) algorithm was utilized to obtain the optimal solution. The optimal solution obtained in this study can serve 138,610,575 passengers during eVTOLs’ entire lifecycle (15 years) with a total cost of CNY 368.57 hundred million, with the cost of CNY 265.9 per passenger. Although it is higher than the driving cost, it saves 1–1.5 h and thus has high cost effectiveness during rush hours.

As subsidies for renewable energy are progressively reduced worldwide, electric vehicle charging stations (EVCSs) powered by renewable energy must adopt market-driven approaches to stay competitive. The unpredictable nature of renewable energy production poses major challenges for strategic planning. To tackle the uncertainties stemming from forecast inaccuracies of renewable energy, this study introduces a peer-to-peer (P2P) energy trading strategy based on game theory for solar-hydrogen-battery storage electric vehicle charging stations (SHS-EVCSs). Firstly, the incorporation of prediction errors in renewable energy forecasts within four SHS-EVCSs enhances the resilience and efficiency of energy management. Secondly, employing game theory’s optimization principles, this work presents a day-ahead P2P interactive energy trading model specifically designed for mitigating the variability issues associated with renewable energy sources. Thirdly, the model is converted into a mixed integer linear programming (MILP) problem through dual theory, allowing for resolution via CPLEX optimization techniques. Case study results demonstrate that the method not only increases SHS-EVCS revenue by up to 24.6% through P2P transactions but also helps manage operational and maintenance expenses, contributing to the growth of the renewable energy sector.