Search Results (253)

Ground-based LiDAR sensing offers a promising approach for delivering short-range landing feedback to aerial vehicles operating near vertiports and in GNSS-degraded environments. This work introduces a detection system capable of classifying aerial vehicles and estimating their 3D positions with sub-meter accuracy. Using a simulated Gazebo environment, multiple LiDAR sensors and five vehicle classes, ranging from hobbyist drones to air taxis, were modeled to evaluate detection performance. RGB-encoded point clouds were processed using a modified YOLOv6 neural network with Slicing-Aided Hyper Inference (SAHI) to preserve high-resolution object features. Classification accuracy and position error were analyzed using mean Average Precision (mAP) and Mean Absolute Error (MAE) across varied sensor parameters, vehicle sizes, and distances. Within 40 m, the system consistently achieved over 95% classification accuracy and average position errors below 0.5 m. Results support the viability of high-density LiDAR as a complementary method for precision landing guidance in advanced air mobility applications. Full article

For aircraft departure, which is a process of multi-resource coordination, strict time limitations, and complex condition constraints, the optimization of taxi-out time prediction is critical for enhancing airport surface operational efficiency, optimizing runway slot utilization, and reducing aircraft ground delay and fuel consumption. By combining aircraft taxi path and network traffic flow features, a refined airport road-level transport network model is constructed to accurately characterize the taxi path topology and node-edge attributes. On this basis, two new micro-features are introduced: estimated taxi time and the number of handovers. Experimental results show that after the introduction of the micro-features, the prediction accuracy of the taxi-out time prediction model within the error of 1 min increases from 49.29% to 54.41%, and the prediction accuracy within the error of 5 min reaches 99.42%. This method effectively addresses the limitations of traditional models that focus solely on the overall taxiing process while neglecting microscopic airfield network dynamics and time consumption during control handover procedures. The method can be integrated into the Airport Collaborative Decision Making (A-CDM) system to provide minute-level support for departure taxi-out time prediction, thereby providing a more precise and operationally aligned temporal benchmark for intelligent apron operations scheduling, aircraft sequencing optimization, and other collaborative decision making processes. Full article

Currently, the short-term accurate prediction of multi-region taxi pick-up demand often adopts methods that integrate graph neural networks with temporal modeling. However, most models focus solely on node features during the learning process, neglecting or simplifying edge features. This study adopts a hybrid prediction framework, Edge-GATv2-LSTM, which integrates an edge-aware attention-based graph neural network (Edge-GATv2) with a temporal modeling component (LSTM). The framework not only models spatial interactions among regions via GATv2 and temporal evolution via LSTM but also incorporates edge features into the attention computation structure, jointly representing them with node features. This enables the model to perceive both node attributes and the strength of inter-regional relationships during attention weight calculation. Experiments are conducted based on real-world taxi order data from Ningbo City, and the results demonstrate that the adopted Edge-GATv2-LSTM model exhibits favorable performance in terms of pick-up demand prediction accuracy. Specifically, the model achieves the lowest RMSE and MAE of 3.85 and 2.86, respectively, outperforming all baseline methods and confirming its effectiveness in capturing spatiotemporal demand patterns. This research can provide decision-making support for taxi drivers, platform operators, and traffic management departments—for example, by offering a reference basis for optimizing taxi pick-up route planning when vehicles are unoccupied. Full article

Urban multimodal travel trajectory prediction is a core challenge in Intelligent Transportation Systems (ITSs). It requires modeling both spatiotemporal dependencies and dynamic interactions among different travel modes such as taxi, bike-sharing, and buses. To address the limitations of existing methods in capturing these diverse trajectory characteristics, we propose a spatiotemporal multi-model ensemble framework, which is an ensemble model called GLEN (GCN and LSTM Ensemble Network). Firstly, the trajectory feature adaptive driven model selection mechanism classifies trajectories into dynamic travel and fixed-route scenarios. Secondly, we use a Graph Convolutional Network (GCN) to capture dynamic travel patterns and Long Short-Term Memory (LSTM) network to model fixed-route patterns. Subsequently the outputs of these models are dynamically weighted, integrated, and fused over a spatiotemporal grid to produce accurate forecasts of urban total traffic flow at multiple future time steps. Finally, experimental validation using Beijing’s Chaoyang district datasets demonstrates that our framework effectively captures spatiotemporal and interactive characteristics between multimodal travel trajectories and outperforms mainstream baselines, thereby offering robust support for urban traffic management and planning. Full article

With the rapid growth of urban transportation demand, traditional taxi systems face challenges such as supply–demand imbalances and low dispatch efficiency. These methods, which rely on static data and predefined strategies, struggle to adapt to dynamic traffic environments. To address these issues, this paper proposes a dual-layer Taxi Dispatch and Empty-Vehicle Repositioning (TDEVR) optimization framework based on Multi-Agent Reinforcement Learning (MARL). The framework separates the tasks of taxi matching and repositioning, enabling efficient coordination between the decision-making and execution layers. This design allows for the real-time integration of both global and local supply–demand information, ensuring adaptability to complex urban traffic conditions. A Multi-Agent Dual-Attention Reinforcement Learning (MADARL) algorithm is proposed to enhance decision-making and coordination, combining local and global attention mechanisms to improve local agents’ decision-making while optimizing global resource allocation. Experiments using a real-world New York City taxi dataset show that the TDEVR framework with MADARL leads to an average improvement of 20.63% in the Order Response Rate (ORR), a 15.29 increase in Platform Cumulative Revenue (PCR), and a 22.07 improvement in the Composite Index (CI). These results highlight the significant performance improvements achieved by the proposed framework in dynamic scenarios, demonstrating its ability to efficiently adapt to real-time fluctuations in supply and demand within urban traffic environments. Full article

To address the charging demand challenges brought about by the widespread adoption of electric vehicles, integrated photovoltaic–storage–charging stations (PSCSs) enhance energy utilization efficiency and economic viability by combining photovoltaic (PV) power generation with an energy storage system (ESS). This paper proposes a two-stage data-driven holistic optimization model for the siting and capacity allocation of charging stations. In the first stage, the location and number of charging piles are determined by analyzing the spatiotemporal distribution characteristics of charging demand using ST-DBSCAN and K-means clustering methods. In the second stage, charging load results from the first stage, photovoltaic generation forecast, and electricity price are jointly considered to minimize the operator’s total cost determined by the capacity of PV and ESS, which is solved by the genetic algorithm. To validate the model, we leverage large-scale GPS trajectory data from electric taxis in Shenzhen as a data-driven source of spatiotemporal charging demand. The research results indicate that the spatiotemporal distribution characteristics of different charging demands determine whether a charging station can become a PSCS and the optimal capacity of PV and battery within the station, rather than a fixed configuration. Stations with high demand volatility can achieve a balance between economic benefits and user satisfaction by appropriately lowering the peak instantaneous satisfaction rate (set between 70 and 80%). Full article

Hydrodynamic slamming loads during water landing are one of the main concerns for the structural design and wave resistance performance of large amphibious aircraft. However, current existing sensors are not used for full-scale hydrodynamic load flight tests on complex models due to their large size, fragility, intrusiveness, limited range, frequency response limitations, accuracy issues, and low sampling frequency. Fibre-optic sensors’ small size, immunity to electromagnetic interference, and reduced susceptibility to environmental disturbances have led to their progressive development in maritime and aeronautic fields. This research proposes a novel hydrodynamic profile encapsulation method using ultra-thin surface-mounted micro-electromechanical system (MEMS) fibre-optic Fabry–Pérot pressure sensors (total thickness of 1 mm). The proposed sensor exhibits an exceptional linear response and low-temperature sensitivity in hydrostatic calibration tests and shows superior response and detection accuracy in water-entry tests of wedge-shaped bodies. This work exhibits significant potential for the in situ monitoring of hydrodynamic loads during water landing, contributing to the research of large amphibious aircraft. Furthermore, this research demonstrates, for the first time, the proposed surface-mounted pressure sensor in conjunction with a high-speed acquisition system for the in situ monitoring of hydrodynamic pressure on the hull of a large amphibious prototype. Following flight tests, the sensors remained intact throughout multiple high-speed hydrodynamic taxiing events and 12 full water landings, successfully acquiring the complete dataset. The flight test results show that this proposed pressure sensor exhibits superior robustness in extreme environments compared to traditional invasive electrical sensors and can be used for full-scale hydrodynamic load flight tests. Full article

Motorbike taxis are widely used in Yaoundé and Douala, despite their association with heightened accident risks and relatively high fares. This research combines qualitative methods, including 38 semi-structured interviews and direct field observations, with a quantitative survey of 280 motorbike taxi passengers (customers). It employs a dynamic risk approach to analyse both the factors motivating individuals to choose motorbike taxis and the strategies adopted by drivers and passengers to mitigate and prevent accidents. The findings reveal that speed, cost-effectiveness, and the limited accessibility of certain neighbourhoods to other transport options are key factors driving regular motorbike taxi use. Moreover, strategies for managing accident risks include regulating passenger positions based on gender, perceived age, or physical stature; invoking deities for protection; and passengers’ verbal interactions with drivers to ensure safer behaviour. This research also explores how overloading, a collectively tolerated deviance, is managed to avoid or minimize the impact of accidents. By addressing both risk acceptance and prevention strategies, this study provides new insights into passengers’ social perceptions, which are often overlooked in motorbike taxi research. It expands the understanding of motorbike taxi use in urban Global South transport contexts, particularly in terms of users’ risk management behaviours. Full article

With the emergence of joint business operations involving electric vehicle taxis (EVTs) and charging/swapping stations (CSSTs), a unified decision-making method has become essential for an EVT to select both the driving path and the energy acquisition mode (EAM). The decision making is influenced by energy acquisition cost and potential operation profit. The energy acquisition cost is closely related to the driving time required to reach a CSST, and existing prediction methods for driving time ignore the spatial–temporal interactions of traffic flows on different roads and fail to account for traffic congestion differences across various sections of a road. Existing estimation methods for potential operation income ignore the distributions of taxi orders in different areas. To address these issues, a traffic flow prediction model is first proposed based on the long short-term memory–generative adversarial network (LSTM-GAN) deep learning algorithm. A refined driving time model is developed by segmenting a road into different sections. Then, an expected operation income model is developed considering the distributions of origins and destinations of taxi orders in different areas. Then, a decision-making method for path planning and the charging/swapping mode is proposed, aiming to maximize the total profit of EVTs. Finally, the effectiveness of the proposed decision-making method for EVTs is validated with a city’s traffic network. Full article

High battery swapping demand from electric taxis and drivers’ subjective station selection often leads to congestion and the uneven utilization of battery swapping stations (BSSs). Efficient vehicle guidance is essential for improving the operational performance of electric taxis. In this study, we have developed a vehicle-to-station guidance model that considers dynamic demand and diverse driver response-time preferences. We have proposed two decision-making strategies for BSS recommendations. The first is a real-time optimization method that uses a greedy algorithm to provide immediate guidance. The second is a delayed optimization framework that performs batch scheduling under high demand. It integrates a genetic algorithm with KD-tree search to handle dynamic demand insertion. A case study based on Beijing’s Fourth Ring Road network was conducted to evaluate the strategies under four driver preference scenarios. The results show clear differences in vehicle waiting times. A balanced consideration of travel distance, waiting time, and cost can effectively reduce delays for drivers and improve station utilization. This research provides a practical optimization approach for real-time vehicle guidance in battery swapping systems. Full article

With the rapid expansion of the aviation industry, traditional static scheduling methods have become inadequate to meet the increasingly complex demands of efficient airport operations. To enhance the operational efficiency of multi-runway airports, this paper introduced a two-stage dynamic departure scheduling method based on continuous Markov chains. The pushback rate control strategy was extended to multi-runway scenarios to identify the optimal taxiway queue threshold in stage I. In stage II, the pushback rate control strategy with a known queue threshold was introduced into a multi-objective optimization model, aiming to minimize flight delays and operational costs including pushback waiting times, taxi fuel consumption, and environmental impact. Then, continuous-time Markov chains (CTMC) were employed to track aircraft state transitions in the taxiway queue, and a nested whale optimization algorithm was proposed to optimize both the pushback sequence and runway resource allocation. Results indicate that the proposed method reduced the average taxiway queue time by 55.58%, with delay reductions of up to 73.06%, offering significant cost savings and environmental benefits while improving flight punctuality. This innovative approach highlights the potential for optimizing airport resource scheduling in complex and dynamic environments. Full article

In this paper, the local and global structure of positive solutions for a general predator–prey model in a multi-dimension with ratio-dependent predator influence and prey taxis is investigated. By analyzing the corresponding characteristic equation, we first obtain the local stability conditions of the positive equilibrium caused by prey taxis. Secondly, taking the prey-taxis coefficient as a bifurcation parameter, we obtain the local structure of the positive solution by resorting to an abstract bifurcation theorem, and then extend the local solution branch to a global one. Finally, the local stability of such bifurcating positive solutions is discussed by the method of the perturbation of simple eigenvalues and spectrum theory. The results indicate that attractive prey taxis can stabilize positive equilibrium and inhibits the emergence of spatial patterns, while repulsive prey taxis can lead to Turing instability and induces the emergence of spatial patterns. Full article

To enhance the accuracy and real-time capability of estimated in-block time (EIBT) predictions at airports, this study proposes a two-stage integrated prediction method. By extending the prediction time window for arrival times, this method systematically models and analyzes the integrated arrival time, thereby achieving precise EIBT predictions. This study divides the arrival process into the approach flight stage and the taxi-in stage, constructing predictive models for each and identifying key influencing factors. Additionally, copula entropy is employed to optimize feature selection. Based on operational data from Shanghai Pudong International Airport, a LightGBM-based prediction model was developed and validated across multiple datasets. The results demonstrate that the two-stage integrated forecasting method significantly outperforms single-stage modeling, with the best model achieving a prediction accuracy of 87.11% within a ±5 min error margin. Furthermore, this study validates the effectiveness of copula entropy in enhancing model prediction performance. This research provides theoretical support and practical references for improving the real-time predictive capabilities of airport collaborative decision-making systems, as well as a technical pathway for integrated air-surface management research at multi-runway airports. Full article

With the development of transportation networks, countless trajectory data are accumulated, and understanding human mobility from traffic data could be helpful for smart cities, urban computing, and urban planning. Extracting valuable insights from traffic data, such as taxi trajectories, can significantly improve residents’ daily lives. There are many studies on spatiotemporal data mining. As we know, arrival prediction or regional function detection encompasses important tasks for traffic management and urban planning. However, trajectory data are often mutilated because of personal privacy and hardware limitations, i.e., we usually can only obtain partial trajectory information. In this paper, we develop an embedding method to predict the next arrival using the origin–destination (O-D) pair trajectory information and point of interest (POI) data. Moreover, the embedding information contains region latent features; thus, we also detect the regional function in this paper. Finally, we conduct a comprehensive experimental study on a real-world trajectory dataset. The experimental results demonstrate the benefit of predicting arrivals, and the embedding vectors can detect the regional function in a city. Full article

With the rapid growth of the civil aviation industry, the aircraft towing and taxiing (ATT) system has gained significant attention in the literature. Due to its nonlinear and underactuated characteristics, the path tracking and control of the ATT system is a challenging issue. Therefore, in this paper, a robust adaptive cascade control structure is proposed for the path tracking of the ATT system. Firstly, the system description is established via kinematic and dynamics modeling, and the lumped system disturbance is derived for the controller design. Next, the robust adaptive cascade control structure consisting of the adaptive MPC and the CSMC or the RASMC-based dynamics controller is designed with the application of the extended Kalman filter. The system stability in the dynamics loop is verified through the Lyapunov method. The simulation results reveal that the combination of the adaptive MPC and the RASMC can provide a more accurate tracking performance and smoother trajectories under the given disturbances and uncertainties, while both ensuring passenger comfort and meeting the civil aviation regulations, indicating its potential in practical applications. Full article