Search Results (283)

The growing complexity of urban mobility requires datasets that integrate dynamic traffic observations with meteorological, geometric, and urban-context information. This study presents MUTra-CDMX, a multisource urban traffic dataset covering a 14.72 km section of the Insurgentes Sur corridor in Mexico City. Traffic data were obtained from TomTom at five-minute intervals for 20 consecutive road segments from 1 November 2024 to 28 February 2025. Hourly meteorological data were retrieved from Meteosource, while segment-level geometry, topology, signalized locations, and nearby points of interest were derived from TomTom metadata and OpenStreetMap. The primary analytical file contains 691,200 segment–timestamp records and 12 variables describing traffic and free-flow conditions, meteorological information, derived operational indicators, and reconstruction status. Of these records, 682,264 are original observations and 8936 are reconstructed segment–timestamp combinations, identified by the Boolean variable is_imputed. Technical validation confirmed complete temporal coverage, preservation of original traffic observations, consistent weather alignment, and reconstruction performance through artificial masking. Predictive utility was evaluated through chronological travel-time forecasting under a leakage-controlled protocol. At the 30 min horizon, XGBoost achieved a mean absolute error of 12.84 s, a root mean squared error of 37.91 s, and a coefficient of determination ($R^2$) of 0.771, outperforming a persistence baseline. MUTra-CDMX supports congestion analysis, imputation studies, spatiotemporal modeling, and travel-time forecasting. Full article

Traffic sensors provide real-time measurements of current traffic conditions, whereas traffic management applications require forecasts of future traffic speed or flow. This study considers the incremental expansion of an operating sensor network, in which a pre-trained network-level forecasting model must predict traffic states at sensor locations that were absent during training. Many spatio-temporal graph neural networks rely on sensor-specific embeddings and graph connections learned from data-rich training networks. These representations are undefined for previously unseen locations, limiting the direct application of pre-trained adaptive-graph forecasters during sensor-network expansion. To address this cold-start problem, we propose support-conditioned sensor-adaptive meta-graph learning (SC-SAMG), which derives target-node representations and spatial dependencies from a short support period. The framework combines a support-set encoder, a task-specific graph learner, and first-order meta-learning to adapt the network-level forecaster using one to seven days of target observations. Experiments on four traffic benchmarks evaluate forecasts of speed or flow over the next 15–60 min under a leakage-controlled held-out-node protocol. SC-SAMG consistently outperforms fine-tuned gated recurrent unit (GRU), adaptive graph convolutional recurrent network (AGCRN), and diffusion convolutional recurrent neural network (DCRNN) baselines. It reduces mean absolute error (MAE) by up to 11% relative to the adaptive-graph baseline and by up to 7% relative to the diffusion convolutional baseline. These results demonstrate the potential of support-conditioned graph adaptation for incorporating previously unseen sensor locations into existing network-level traffic forecasting systems. Full article

Expressway unmanned aerial vehicle (UAV) pre-deployment is a geospatial decision-support task that requires reliable road-segment-level traffic flow prediction based on spatial sensing networks. However, existing spatiotemporal forecasting models remain limited in characterizing cross-segment propagation relationships, long-lag causal dependencies, and atypical traffic evolution patterns. In addition, complex models often fail to meet the computational requirements of edge-device deployment. Based on electronic toll collection (ETC) gantry data, this study proposes a causal–semantic spatiotemporal forecasting framework for long-term traffic flow prediction with a 24 h forecasting horizon. First, conditional Granger causality analysis is used to construct a directed causal prior graph that characterizes traffic propagation relationships among expressway segments. Second, scenario-semantic priors generated by a large language model are introduced to describe atypical traffic conditions. Then, causal structural priors and scenario-semantic priors are integrated into a teacher model and transferred to a lightweight student model through response-level and feature-level knowledge distillation. Experiments using expressway data from Hubei Province, China, show that the proposed model achieves the best overall performance in the typical scenario and competitive performance in the atypical scenario. The results indicate that the proposed framework can provide day-scale decision support for expressway law-enforcement UAV pre-deployment and enhance the spatial intelligence of traffic emergency management. Full article

Traffic states evolve on irregular sensor graphs and vary with calendar context, yet the original ASTGCN does not explicitly model how the contribution of different graph receptive fields changes across traffic periods. This paper proposes CD-MRFG, a context-gated extension of ASTGCN that encodes hour-of-day, day-of-week and weekend information and uses the resulting representation to weight Chebyshev graph-convolution orders in each spatio-temporal block. Under a common 12-step forecasting protocol, CD-MRFG reduced the overall MAE and RMSE of the reproduced ASTGCN baseline from 18.66 and 31.05 to 16.98 and 28.59 on PEMS03, from 22.79 and 35.02 to 20.82 and 32.77 on PEMS04, and from 18.88 and 28.83 to 17.24 and 26.84 on PEMS08. Three-seed experiments confirmed lower mean MAEs on PEMS04 (p = 0.028) and PEMS08 (p = 0.042), although the corresponding RMSE differences did not reach the 0.05 significance threshold. Ablation, gate-weight, sensitivity, complexity and convergence analyses showed that temporal context was the main source of the improvement and that the gate provided a model-internal view of order selection with moderate overhead. CD-MRFG remains less accurate than several stronger recent baselines, so its value is a bounded and interpretable extension of ASTGCN rather than a universal state-of-the-art replacement. Full article

Accurate forecasting of vessel traffic flow (VTF) is essential for modern maritime and port management, as it improves route-planning efficiency, reduces congestion and collision risks, and optimizes port operations. This study proposes a novel deep learning framework, namely, the Bidimensional Empirical Mode Decomposition–Nocal Convolutional Neural Network–Transformer (BEMD–NocalCNN–Transformer), for high-precision VTF prediction. The proposed framework first applies the BEMD algorithm to decompose the original time-series data into high- and low-frequency components. The NocalCNN module is then employed to extract spatial features from each component, while the Transformer module captures temporal dependencies and predicts future traffic-flow trends. The final predictions are obtained by aggregating the outputs of the high- and low-frequency components. Sensitivity analyses are conducted on key parameters, including input sequence length, learning rate, number of iterations, and convolution kernel size, to optimize the model configuration. To comprehensively evaluate the proposed framework, SVM, BPNN, RNN, LSTM, GRU, Transformer, WVMA-LSTM, and NocalCNN–Transformer were implemented and evaluated using the same CFD and Wuhan datasets, data preprocessing procedures, training–testing partitions, prediction settings, and evaluation metrics. The experimental results demonstrate that the proposed model outperforms the benchmark models and achieves substantially lower prediction errors for both the Caofeidian Promontory (CFD) and Wuhan waterways. These findings demonstrate consistent prediction performance of the proposed framework and provide a robust technical foundation for intelligent maritime traffic management and port operation optimization. Full article

Urban traffic flow is difficult to forecast accurately because its evolution is non-linear and governed by dependencies that operate over different spatial and temporal ranges. This paper introduces the Multi-Perspective Spatio-Temporal Feature Fusion Model (MPSTFFM) to describe these dependencies through complementary views. The temporal signal is separated into a slowly varying trend and a residual fluctuation, while the spatial structure is represented by four graphs: first-order adjacency, second-order in-degree, second-order out-degree, and a data-adaptive graph. These graphs respectively encode physical road connectivity, common inflow sources, common outflow destinations, and latent spatial associations. Whereas the first three are constructed from the known network topology, the adaptive graph is learned together with the prediction model and can therefore identify correlations not expressed by physical links. Within each spatio-temporal view, self-attention captures dependencies over long ranges, and convolutional operations extract local patterns. The features learned from all views are subsequently fused into a high-dimensional representation used to predict future flow. Experiments on real-world datasets compare MPSTFFM with twelve methods published during the preceding five years. On these benchmarks MPSTFFM outperforms every baseline, lowering the average MAE, RMSE, and MAPE across the four datasets by 13.04%, 5.28%, and 9.59%, respectively, relative to the best baseline on each one. Full article

Expressway travel demand climbs steeply during public holidays, and the severe congestion that follows has become a principal constraint on both network operating efficiency and the quality of travel services. Dependable forecasting of holiday congestion, coupled with the practical realization of proactive management, accordingly carries considerable theoretical and practical weight for improving how expressways are operated and administered and for safeguarding efficient and safe public travel. Organized around the central theme of congestion prediction and proactive management for expressways over holiday periods, this paper reviews the progress of research across five interrelated areas: traffic speed and flow prediction methods; congestion state identification and forecasting; holiday travel characteristic analysis together with demand prediction; multi-source data fusion and congestion propagation mechanisms; and expressway traffic control together with traveler behavior guidance. Having surveyed the theoretical underpinnings, core technologies, and representative methods of each area, the paper concentrates in particular on the shortcomings and difficulties that existing studies encounter in coping with the hallmark features of holiday traffic, namely demand surges of short duration combined with pronounced spatiotemporal heterogeneity, and proceeds to outline promising avenues for subsequent research. Overall, the review seeks to provide wide-ranging literature support and a theoretical reference for developing technologies that predict and proactively manage congestion on expressways during public holidays. Full article

The rapid growth of Intelligent Transportation Systems (ITSs) and Internet of Things (IoT) technologies has generated massive volumes of distributed traffic data, creating significant challenges related to privacy, scalability, communication overhead, and heterogeneous traffic patterns. To address these challenges, this paper proposes FedTraffic, a hierarchical federated learning framework for traffic flow forecasting that integrates Edge–Fog–Cloud computing, hybrid deep learning, adaptive federated optimization, and Explainable Artificial Intelligence (XAI). The proposed framework combines a Temporal Convolutional Network–Conditional Variational Autoencoder (TCN–CVAE) with traffic-behavior clustering, adaptive client selection, and hierarchical model aggregation to enable accurate, privacy-preserving, and interpretable traffic prediction under heterogeneous non-IID environments. Extensive experiments demonstrate that FedTraffic achieves a best Mean Absolute Error (MAE) of 2.12, a Root Mean Square Error (RMSE) of 4.28, a Mean Absolute Percentage Error (MAPE) of 5.47%, and an $R^2$ score of 0.966. Compared with the strongest federated baseline, it improves MAE by up to 18.77%, RMSE by 16.41%, and MAPE by more than 22%, while reducing communication overhead through an 8:1 latent representation compression ratio. These results demonstrate the effectiveness of FedTraffic as a scalable, privacy-preserving, and interpretable solution for next-generation intelligent transportation systems. Full article

Short-term traffic flow forecasting becomes especially difficult when incomplete observations, within-window frequency variation, and state-dependent sensor relations occur together. Missing readings can affect both node features and the spatial dependencies inferred from them, yet these issues are commonly modeled separately. We therefore propose RIFT-STGNN, a Robust Interleaved Frequency–Trend Spatio-Temporal Graph Neural Network for multi-step traffic flow forecasting. RIFT-STGNN follows a coordinated information flow: observation status is retained during temporal–frequency encoding, the frequency representation supports both node features and graph construction, and four graph sources are fused before each Graph-GRU update. Trend-aware temporal attention then produces direct multi-step forecasts. Experiments on PEMS03, PEMS04, PEMS07, and PEMS08 show competitive numerical performance relative to selected literature-reported baselines under the 12-step setting. On PEMS04 and PEMS08, the three-run mean MAE values are 17.73 and 13.14, respectively. These values are numerically 4.63% and 9.00% lower than the corresponding literature-reported sAMDGCN values. Component ablations, state-dependent graph analysis, and controlled missing-rate experiments support the roles of dynamic graph learning, within-window frequency encoding, and mask-aware input handling under the evaluated settings. Full article

In dynamic Vehicular Edge Computing (VEC) environments, rapidly changing vehicle mobility and traffic lead to fluctuating edge resource demands, challenging task offloading and scheduling. Accurate prediction of future traffic flow and traffic-state-derived workload representations is thus crucial for proactive resource management. To address the limitations of existing methods in short-term dynamic characterization, complex spatial interaction modeling, and heterogeneous target prediction, this paper proposes a Spatio-Temporal Graph Attention Network (TA-STGAT). The proposed model constructs multi-dimensional RSU-level state sequences from simulated trajectories generated on a real-world road network and separately forecasts vehicle flow within RSU coverage areas and the associated traffic-state-derived workload representation under a unified spatio-temporal modeling framework. By integrating gated dilated temporal convolutions with a topology-constrained multi-head graph attention mechanism, the model captures multi-scale temporal dependencies and nonlinear spatial correlations. Experimental results show that, compared with the best-performing baseline in terms of RMSE for each forecasting task, TA-STGAT reduces RMSE by 10.89% and 11.29% in workload-representation prediction and traffic flow prediction, respectively, demonstrating its effectiveness for short-term edge-state forecasting. Full article

The accelerating adoption of electric vehicles (EVs) presents significant challenges for maintaining grid stability and optimizing charging infrastructure. Accurate short-term forecasting of EV charging demand is therefore critical to support reliable grid operation and effective energy management in urban environments. However, existing forecasting models often fail to capture the intricate interdependencies among mobility patterns, weather variations, and real-world charging behaviors, which constrains their generalizability and robustness. This study develops a multi-model forecasting framework that leverages Transformer-based deep learning architectures to integrate real-world charging data with traffic flow and meteorological variables for predicting short-term EV charging demand across metropolitan areas. To benchmark performance, two additional machine learning models—CatBoost and convolutional neural networks (CNNs)—are systematically evaluated using datasets from urban EV supply equipment (EVSE) and electric bus systems. The results indicate that Transformer-based models deliver superior predictive accuracy, temporal consistency, and adaptability compared with CNNs and CatBoost. Furthermore, sensitivity analysis reveals that traffic dynamics and user charging behavior exert the strongest influence on forecast performance. The proposed framework offers actionable insights for utilities and urban planners, facilitating resilient grid operation, optimized charging infrastructure deployment, and accelerated integration of EVs into the power system. Full article

Accurate vessel traffic flow prediction provides an important data basis for intelligent shipping management, including maritime traffic monitoring, navigational risk awareness, waterway organization, and emission-related assessment. Although recent studies have advanced spatiotemporal, graph-based, and hybrid forecasting methods, improving the predictive ability of a conventional ConvLSTM backbone without introducing substantially more complex model structures remains underexplored in grid-based waterway scenarios. This study proposes a lightweight CA-ConvLSTM framework for grid-level vessel inflow and outflow prediction. AIS-derived flow data and MERRA-2 meteorological variables are rasterized onto a common spatial grid and fused at an early stage. A residual dilated convolution module with dilation rates of 1, 2, and 4 is used to extract multi-scale spatial dependencies, and a channel attention mechanism is applied before ConvLSTM-based temporal prediction to adaptively reweight the fused flow-meteorological feature channels. Experiments using AIS and MERRA-2 data from the northern Bohai Strait waterway show that the proposed framework improves baseline ConvLSTM performance. Compared with ConvLSTM, CA-ConvLSTM reduces MSE and MAE by 24.93% and 12.55% for outflow prediction, and by 24.80% and 12.82% for inflow prediction. These results suggest that spatially aligned meteorological fusion, multi-scale spatial feature extraction, and channel-wise feature weighting can effectively enhance ConvLSTM-based grid-level vessel traffic flow prediction without relying on complex model fusion or heavy graph-based architectures. Full article

Accurate traffic flow prediction is fundamental to Intelligent Transportation Systems (ITSs), critical for transportation management and logistics. Despite advances in spatio-temporal prediction methods, existing approaches suffer from two key limitations: (i) multi-scale fusion methods inadequately capture hierarchical constraints between cross-scale features, and (ii) models rely on single spatio-temporal views, neglecting multi-source relationship complementarity. To address these issues, we propose ST-MAFNet, a spatio-temporal multi-scale adaptive fusion network comprising three key components, specifically, a Cross-Scale Hierarchical Anchoring strategy (CSHA) that anchors short-term predictions with multi-scale temporal patterns to mitigate noise; a Dual Spatial Perception Module (DSPM) that learns node heterogeneity and dynamic correlations through node embeddings and adaptive graph attention; and a Spatio-Temporal Adaptive Fusion Module (STAFM) that captures time-varying connectivity by integrating multi-scale temporal features with multi-source spatial relationships. Experiments on four real-world datasets demonstrate that ST-MAFNet is particularly effective for short-term traffic forecasting. Compared with the best previously reported MAE results, ST-MAFNet reduces MAE by 2.95%, 1.43%, 1.25%, and 0.37% on PEMS03, PEMS04, PEMS07, and PEMS08, respectively, and achieves the best or second-best performance on most evaluation metrics. Full article

Trajectory prediction plays a crucial role in autonomous driving by forecasting the future trajectories of agents to support safe and efficient decision-making. Most existing methods that adopt an encoder–decoder architecture have achieved remarkable success, where the scene encoder extracts contextual representations from agents’ history trajectories and lane segments. However, this architecture remains fundamentally constrained by the blind encoder. Specifically, the scene encoder of models extracts contextual information without foresight, leading to significant semantic pollution from proposal-irrelevant context, thereby degrading the prediction performance. To rectify this model deficiency, we propose ProFocus, a proactive encoding framework that reformulates the trajectory prediction model architecture via an anticipatory feedback loop. ProFocus generates the potential proposals in the nascent stage layers, utilizing them as attentional priors to dynamically modulate the scene encoding process. In addition, to optimize the information flow within the attention mechanism and reduce irrelevant context interference in attention distributions, we introduce spatio-temporal focal attention (STFA). By implementing a relation-conditioned sharpening operator through a spatio-temporal relation-controlled softmax, STFA adaptively recalibrates the attention distribution according to related dependencies. Comprehensive evaluations on the Argoverse 1 dataset and INTERACTION dataset validate that ProFocus attains competitive performance across miss rate (MR), minimum average displacement error (minADE) and minimum final displacement error (minFDE), while maintaining a real-time inference speed of 16 ms on an RTX 3090. The results from our ablation studies demonstrate that ProFocus reduces MR, minFDE, and minADE by 2.80%, 2.52%, and 1.41% relative to the baseline, respectively. Furthermore, qualitative visualizations also corroborate that ProFocus exhibits robust performance in diverse traffic scenarios. Full article

Accurate prediction of traffic flow is the key to highways control. However, traditional time series forecasting methods cannot meet the accuracy requirements of long-term forecasting. This paper proposes a multi-channel univariate long-term highway inbound traffic flow forecasting framework with multidimensional signal feature decomposition of time series and a patch time series depth-separable convolutional neural network. Firstly, we propose a multidimensional decomposition block consisting of a principal feature decomposition block based on the Fourier transform, a backbone and noise decomposition block based on the Stationary Wavelet Transform, a cyclic signal enhancer based on threshold comparator, and a trend extraction block based on average pooling. Secondly, we propose to change the depth-separable convolution layer mode and stack multiple depth-separable convolution layers so as to capture the developmental characteristics of the time series signal. Furthermore, a feed-forward neural network layer is set up between the depth-separable convolution layers. Then, true time series decomposition is used in the training phase to compute the multidimensional feature loss, with the aim of improving the shortcoming of the tensor decomposition that does not allow for gradient propagation. Finally, weight aggregation is used to transform the multidimensional data into univariate time series data. Experimental results on real highway inbound traffic flow datasets show that the proposed method achieves better performance than the baseline model and effectively improves the prediction accuracy. Full article