Search Results (78)

Efficient multi-hop prediction over large-scale spatio-temporal knowledge graphs of aircraft taxiing trajectories remains challenging, as existing methods focus either on static multi-hop relations or on accuracy improvement for spatio-temporal single-hop predictions, leading to computational inefficiency. This paper proposes a vector-index-supported multi-hop prediction method. First, a knowledge graph embedding technique that integrates spatio-temporal features maps the trajectory graph into a low-dimensional complex vector space. Then, a hierarchical query acceleration structure based on IndexIVFFlat is constructed. A clustering strategy guided by the distribution of trajectory data partitions the vector space into subspaces, and approximate nearest neighbor search within those subspaces rapidly prunes the candidate set to accelerate multi-hop retrieval. Experiments on real aircraft taxiing trajectory datasets and general benchmarks show that the proposed method substantially improves prediction efficiency while maintaining competitive accuracy. The results demonstrate that the vector index mechanism effectively balances accuracy and efficiency, and the efficiency has been improved by at least 56.65%. This work provides a key technical foundation for real-time analysis and intelligent prediction of large-scale aircraft taxiing trajectories. Full article

Accurate short-term traffic flow prediction in complex urban road networks is of great significance for capacity organization and dispatch optimization in intelligent transportation systems. Using publicly available historical taxi trip records released by the New York City Taxi and Limousine Commission from January to June 2016, this study develops a spatiotemporal fusion framework for short-term traffic flow prediction. To address the nonlinearity, sparsity, and complex spatiotemporal dependencies of traffic flow sequences, the raw trajectory data are first cleaned, spatially gridded, and temporally discretized. Based on the spatial adjacency relationships among grid nodes, a graph structure is then constructed, and a serially coupled graph convolutional network and long short-term memory model is developed to capture spatial dependency features and temporal dynamic features, respectively. Experimental results on the New York City taxi dataset show that, compared with baseline models including the historical average model, long short-term memory network, graph convolutional network, and Transformer, the proposed model achieves better performance in terms of mean absolute error, root mean square error, and coefficient of determination. Furthermore, the SHAP (SHapley Additive exPlanations) method is employed to ANALYZE the differences in feature contributions across nodes in different functional zones from both temporal and spatial perspectives. The results indicate that the model exhibits heterogeneous temporal dependency depths and spatial aggregation patterns across different types of regions within the study area. In addition, regions with high feature contributions show a certain degree of spatial correspondence with the major traffic corridors in Manhattan, suggesting that the model is able to capture part of the spatiotemporal correlation structure of traffic flow in this dataset. Finally, the limitations of the proposed method in terms of static graph structure, response to extreme events, and integration of external factors are discussed. It should be noted that these findings are derived from New York City taxi data from the first half of 2016, and their generalizability to other cities, time periods, or traffic scenarios remains to be further validated. Full article

Accurate prediction of departure flight taxi-out time is critical for enhancing airport surface efficiency and reducing flight delays. However, existing methods often struggle with data sparsity, inadequate representation of complex spatio-temporal interactions among aircraft, and imbalanced sample distributions. To address these challenges, this paper proposes a synergistic multi-module fusion model named DDA-SIM-ATT-CatBoost. The model integrates three core modules: a Dynamic Data Augmentation (DDA) module that expands the training distribution through operationally consistent perturbations to mitigate data imbalance; a Similarity Theory (SIM) module employing K-Prototypes clustering and Mahalanobis distance to achieve precise matching of historical operational patterns; and an Attention Mechanism (ATT) module that dynamically recalibrates feature weights to emphasize critical influencing factors. These modules work synergistically to provide a robust and discriminative input representation for the CatBoost regressor, which excels at handling categorical features and complex nonlinearities. Using real-world departure data from a major hub airport, the proposed model achieves prediction accuracies of 74.57%, 89.12%, and 97.76% within error margins of ±120 s, ±180 s, and ±300 s, respectively, with a Mean Absolute Percentage Error (MAPE) of 10.34%, Mean Absolute Error (MAE) of 87.55 s, and Root Mean Square Error (RMSE) of 125.61 s. Ablation studies validate the positive contribution and synergistic effect of each module, while comparative experiments demonstrate that our model significantly outperforms baseline models such as XGBoost and Random Forest. The DDA-SIM-ATT framework provides a generalizable and high-precision solution for taxi-out time prediction, offering reliable decision support for airport surface operations. Full article

Accurate multi-modal traffic demand forecasting is key to optimizing intelligent transportation systems (ITSs). To overcome the shortcomings of existing methods in capturing dynamic high-order correlations between heterogeneous spatial units and decoupling intra- and inter-mode dependencies at multiple time scales, this paper proposes a Dynamic Multi-Relation Learning with Multi-Scale Hypergraph Transformer method (MST-Hype Trans). The model integrates three novel modules. Firstly, the Multi-Scale Temporal Hypergraph Convolutional Network (MSTHCN) achieves collaborative decoupling and captures periodic and cross-modal temporal interactions of transportation demand at multiple granularities, such as time, day, and week, by constructing a multi-scale temporal hypergraph. Secondly, the Dynamic Multi-Relationship Spatial Hypergraph Network (DMRSHN) innovatively integrates geographic proximity, passenger flow similarity, and transportation connectivity to construct structural hyperedges and combines KNN and K-means algorithms to generate dynamic hyperedges, thereby accurately modeling the high-order spatial correlations of dynamic evolution between heterogeneous nodes. Finally, the Conditional Meta Attention Gated Fusion Network (CMAGFN), as a lightweight meta network, introduces a gate control mechanism based on multi-head cross-attention. It can dynamically generate node features based on real-time traffic context and adaptively calibrate the fusion weights of multi-source information, achieving optimal prediction decisions for scene perception. Experiments on three real-world datasets (NYC-Taxi, -Bike, and -Subway) demonstrate that MST-Hyper Trans achieves an average reduction of 7.6% in RMSE and 9.2% in MAE across all modes compared to the strongest baseline, while maintaining interpretability of spatiotemporal interactions. This study not only provides good model interpretability but also offers a reliable solution for multi-modal traffic collaborative management. Full article

Electric-powered unmanned guidance vehicles provide surface taxiing guidance for arriving and departing aircraft within the airport movement area, enabling sustained safety under complex operational conditions and improving overall operational efficiency, particularly under low-visibility scenarios. In this context, how to design scientifically rigorous operational trajectories for the three phases of unmanned guidance vehicle operations—dispatch, guidance, and recovery—remains an open and important research problem. This study proposes a three-stage trajectory-planning method for unmanned guidance vehicles, including initial trajectory planning, conflict prediction, and conflict resolution. First, the Guidance Unit—composed of the unmanned guidance vehicle and the guided aircraft—is defined, and a standard speed-profile design model is established for this unit. Then, considering airport operational-safety constraints, a conflict prediction algorithm for the guidance process is developed, which identifies potential conflicts in guidance trajectory planning based on time-window overlap analysis. Subsequently, under operational safety constraints, an optimization model aiming to minimize the maximum guidance time is formulated, and a trajectory planning algorithm for unmanned guidance vehicles based on the improved A* algorithm is designed to generate conflict-free operational trajectories. Finally, a simulation study is conducted using a major airport in Southwest China as a case study. The results show that (1) the speed-profile design and airport operational-rule constraints affect the operational trajectories of unmanned guidance vehicles; (2) the proposed algorithm enables coordinated planning of both speed control and path selection, thereby improving overall operational efficiency by 43.65% compared with conventional operations, while ensuring conflict-free airport surface taxiing, due to the adoption of an improved A* trajectory-planning algorithm for unmanned guidance vehicles; (3) under the electric-powered guidance-vehicle scheme proposed in this study, the method achieves a 34.52% reduction in total energy consumption during the guidance phase compared with traditional Follow-Me guidance, enabling the simultaneous optimization of operational efficiency and energy consumption. Full article

To identify the emission reduction potential and policy synergies of Tokyo’s road passenger and urban road freight transport under the “carbon neutrality target,” this paper constructs an assessment framework for megacities. First, based on macroeconomic socioeconomic variables (population, GDP, road length, and employment), regression equations are used to predict traffic turnover for different modes of transport from 2021 to 2050. Then, the prediction results are imported into the LEAP (Long-range Energy Alternatives Planning) model. By adjusting three policy levers—vehicle technology substitution (ZEV: EV/FCEV), energy intensity improvement, and upstream electricity and hydrogen supply decarbonization—a “single-factor vs. multi-factor (policy synergy)” scenario matrix is designed for comparison. The results show that the emission reduction potential of a single measure is limited; upstream decarbonization yields the greatest independent emission reduction effect, while the emission reduction effect of deploying zero-emission vehicles and improving energy efficiency alone is small. In the most ambitious composite scenario, emissions will decrease by approximately 83% by 2050 compared to the baseline scenario, with cumulative emissions decreasing by over 35%. Emissions from rail and taxis will approach zero, while buses and freight will remain the primary residual sources. This indicates that achieving net zero emissions in the transportation sector requires not only accelerated ZEV penetration but also the simultaneous decarbonization of electricity and hydrogen, as well as policy timing design oriented towards fleet replacement cycles. The integrated modeling and scenario analysis presented in this paper provide quantifiable evidence for the formulation of a medium- to long-term emissions reduction roadmap and the optimization of policy mix in Tokyo’s transportation sector. Full article

Airport traffic flow prediction is a basic requirement for air traffic management. Building an effective airport traffic flow prediction model helps reveal how traffic demand evolves over time and supports short-term planning. At the same time, a large amount of air traffic data supports using deep learning to learn traffic patterns with stable and accurate performance. In practice, airports need forecasts at short time intervals and need to know the departure flow and its uncertainty 1–2 h in advance. To meet this need, we treat airport departure flow prediction as a multi-step probabilistic forecasting problem on a multi-airport dataset that is organized by airport and time. Scheduled departure counts, recent taxi-out time statistics (P50/P90 over 30- and 60-minute windows), and calendar variables are put on the same time scale and standardized separately for each airport. Based on these data, we propose an end-to-end multi-step forecasting method built on PatchTST. The method uses patch partitioning and a Transformer encoder to extract temporal features from the past 48 h of multivariate history and directly outputs the 10th, 50th, and 90th percentile forecasts of departure flow for each 10 min step in the next 120 min. In this way, the model provides both point forecasts and prediction intervals. Experiments were conducted on 80 airports with the highest departure volumes, using April–July for training, August for validation, September for testing, and October for robustness evaluation. The results show that at a 10 min interval, the model achieves an MAE of 0.411 and an RMSE of 0.713 on the test set. The error increases smoothly with the forecast horizon and remains stable within the 60–120 min range. When the forecasts are aggregated to 1 h intervals in time or aggregated by airport clusters in space, the point forecast errors decrease further, and the average empirical coverage is 0.78 and the width of the percentile-based intervals is 1.29, which can meet the risk-awareness requirements of tactical operations management. The proposed method is relatively simple and also provides a unified modeling framework for later including external factors such as weather, runway configuration, and operational procedures, and for applications across different airports and years. Full article

This study presents models for estimating the charging time and travel time in autonomous electric taxi systems, based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning methods. In addition to these models, two classical time-series forecasting techniques ARIMA and Prophet were also applied to provide a broader comparative baseline. Unlike traditional time-series prediction methods, the proposed system combines artificial intelligence with Internet of Things (IoT) technologies to perform secure charging operations based on multi-layer cybersecurity mechanisms, including IP authentication, encrypted communication, and charger server validation steps. The models were trained and validated using a comprehensive dataset obtained from 100 electric vehicles with different battery capacities at 50 charging stations located in Kocaeli Province. In the predictions considering parameters such as the vehicle type, battery capacity, and charge level, both models showed high accuracy rates, with the GRU model performing better than the LSTM model in terms of the error rate and temporal consistency. ARIMA and Prophet, on the other hand, produced significantly lower performance compared to deep learning models, confirming that GRU is the most suitable approach for real-time duration estimation. Customers can obtain the estimated time, cost, and charging requirements before their trip, and continuous multi-stage IP-based security controls are performed throughout the charging process as part of the cybersecurity framework. If a foreign or unauthorized connection is detected, the charging operation is automatically stopped. The proposed approach not only increases the efficiency in electric vehicle energy management but also presents an innovative framework that contributes to sustainable and smart transportation. By combining deep learning models, classical statistical forecasting methods, IoT integration, and enhanced cybersecurity controls, this work represents a pioneering step toward autonomous, secure, and eco-friendly urban transportation systems. 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

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 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

Efficient sales route optimization is a critical challenge in logistics and distribution, especially under real-world conditions involving traffic variability and dynamic constraints. This study proposes a novel Hybrid Genetic Algorithm (GAAM-TS) that integrates Adaptive Mutation, Tabu Search, and an LSTM-based travel time prediction model to enable real-time, intelligent route planning. The approach addresses the limitations of traditional genetic algorithms by enhancing solution quality, maintaining population diversity, and incorporating data-driven traffic estimations via deep learning. Experimental results on real-world data from the NYC Taxi dataset show that GAAM-TS significantly outperforms both Standard GA and GA-AM variants, achieving up to 20% improvement in travel efficiency while maintaining robustness across problem sizes. Although GAAM-TS incurs higher computational costs, it is best suited for offline or batch optimization scenarios, whereas GA-AM provides a balanced alternative for near-real-time applications. The proposed methodology is applicable to last-mile delivery, fleet routing, and sales territory management, offering a scalable and adaptive solution. Future work will explore parallelization strategies and multi-objective extensions for sustainability-aware routing. 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

This paper discusses SESAR’s Auto-Steer Taxi at Airport (ASTAIR) project, which seeks to advance airport ground operations including engine-off taxiing to move towards sustainable airports. The ASTAIR concept integrates human–AI teaming to optimize aircraft movement from gates to runways, with the primary objectives of improving predictability, efficiency, and environmental sustainability at large airports. Building on previous initiatives such as SESAR’s AEON, ASTAIR brings high-level automation to tasks like autonomous taxiing and vehicle routing. The system assists operators by calculating conflict-free routes for vehicles and dynamically adjusting operations based on real-time data. Based on workshops with several stakeholders, we describe the operational challenges involved in implementing ASTAIR, including managing parking stand availability and adapting to unforeseen events. A significant challenge highlighted is the human–automation partnership, where AI plays a supportive role but humans retain control over critical decisions, particularly in cases of system failure. The need for clear and consistent collaboration between AI and human operators is emphasized to ensure safety, efficiency, and improved compliance with take-off schedules, which in turn facilitates in-flight optimization. Full article

Accurate travel time prediction is essential for optimizing transportation systems and alleviating congestion in urban environments. This study introduces a comprehensive framework that combines advanced spatiotemporal data refinement techniques with a deep learning model, DeepST-Net, to improve prediction accuracy. Using a dataset from the New York City Taxi and Limousine Commission (TLC), an ablation study is conducted to systematically assess the impact of various preprocessing strategies on model performance. Additionally, the DeepST-Net model is benchmarked against established models, including MURAT, ST-NN, XGB-IN, and JSTC. Our findings demonstrate that DeepST-Net, leveraging refined spatiotemporal features, achieves significant improvements in prediction accuracy, with a mean absolute error (MAE) of 59.27 s, a root mean square error (RMSE) of 89.63 s, and a mean absolute percentage error (MAPE) of 9.73%, outperforming all benchmark models. By bridging data refinement and predictive modeling, the proposed framework offers a scalable and effective solution for travel time prediction in complex urban contexts. Full article