Jun 2026· Journal of Intelligent Decision Making and Information Science· Vol 3, pp. 618-637· 0 citations· 122 references
TL;DR
This research proposes an integrated framework for traffic path recommendation that combines systematic feature extraction, temporal prediction, and dynamic graph-based routing and demonstrates that tightly coupling predictive traffic modeling with dynamic graph routing yields measurable improvements in route efficiency over baseline approaches.
Abstract
Urban transportation networks generate massive volumes of spatio-temporal data, and the effective exploitation of such data to enable intelligent route recommendations has become a critical priority in smart city development. Existing systems predominantly rely on static or short-horizon prediction models that fail to capture the complex, non-stationary dynamics characterizing real-world vehicular traffic, particularly under congestion-prone conditions in densely populated urban corridors. The challenges are further compounded by high-dimensional, noisy, and heterogeneous sensor data that makes robust feature extraction and temporal modeling particularly difficult, as traditional approaches exhibit poor generalization across varying prediction horizons and traffic regimes. This research proposes an integrated framework for traffic path recommendation that combines systematic feature extraction, temporal prediction, and dynamic graph-based routing. The methodology begins with the Feature Extraction for Vehicular Traffic Time Series (FEVTTS) algorithm, which applies Mutual Information, Random Forest Regression, and Correlation Coefficient analysis to derive an optimal, low-redundancy feature set from raw sensor data. The refined features are then fed to the Temporal Traffic Feature Aggregation Algorithm (TFTAA), which integrates short-term and long-term traffic patterns using a rolling window mechanism to produce accurate multi-horizon forecasts. Finally, the predicted traffic variables are embedded as dynamic edge weights into a Time Series Based Graph (TSBG), on which Dijkstra's algorithm computes optimal time-dependent routes. The proposed TFTAA algorithm employs a hybrid statistical and ensemble machine learning design, incorporating SARIMAX for seasonal decomposition and gradient-boosted residual correction using XGBoost, augmented by quantile regression for uncertainty quantification. The framework was validated using the London Traffic Dataset (Highways England) and a locally collected Pune city dataset comprising 26,785 and 25,920 samples, respectively, across diverse road types. TFTAA consistently outperformed SARIMAX and XGBoost baselines at all evaluated prediction horizons from 5 to 60 minutes, achieving a Mean Absolute Error (MAE) of 2.1 at the 5-minute horizon and 14.3 at the 60-minute horizon, compared to 25.1 for SARIMAX at the same horizon. The TSBG-based routing reduced average travel time by an average of 18.3% over static Dijkstra across 15 test days on the London dataset. The proposed framework demonstrates that tightly coupling predictive traffic modeling with dynamic graph routing yields measurable improvements in route efficiency over baseline approaches. The research advances the state of the art in intelligent transportation systems by providing a scalable, feature-engineered, and prediction-integrated path recommendation solution applicable to both developed and emerging urban road networks.
This framework introduces an adaptive graph learning module that dynamically infers meaningful connectivity relationships among traffic sensors—not relying on fixed geographic or distance-based assumptions—but instead leveraging real-time traffic correlations and node-level embeddings, enabling effective modeling of both localized spatial interactions and multi-scale temporal dependencies across varying prediction horizons.
Zhengxu Luan, Huan Wang, Miaobowen Wang et al.· Computers and artificial int...· 0 citations
Urban traffic prediction plays a critical role in improving transportation efficiency and supporting sustainability in smart cities. This study proposes an explainable spatio-temporal machine learning framework that integrates periodic temporal features with graph-based spatial representations. Temporal dynamics are modeled using cyclical transformations of time-related variables, while spatial dependencies are captured through graph centrality metrics derived from the transportation network. The proposed approach is evaluated on real-world traffic data collected from seven critical intersections in Sakarya, Türkiye. To ensure a comprehensive assessment, multiple models including Random Forest, XGBoost, and Support Vector Regression (SVR) are compared. Experimental results show that tree-based ensemble methods perform significantly better, and the highest prediction performance is consistently obtained with the spatio-temporal feature configuration ( $R^{2} \approx 0.96$ ). These findings demonstrate that traffic flow is not only governed by temporal patterns but is also strongly influenced by spatial interactions within the network. In addition, the impact of lag-based features, which are widely used in the literature, is systematically analyzed. Results indicate that although lag variables appear highly important in feature importance analyses, their contribution to predictive performance is limited. This highlights a critical distinction between feature importance and actual predictive utility, referred to in this study as misleading importance. Overall, the proposed approach provides a computationally efficient and interpretable solution for urban traffic prediction, while also providing insights into feature selection in spatio-temporal modeling.
: Intelligent transportation is the core solution to alleviating urban traffic congestion and enhancing traffic governance efficiency. Accurately capturing the spatiotemporal coupling characteristics of traffic flow is key to achieving precise traffic flow prediction. Addressing current issues such as the neglect of spatiotemporal correlations in traffic flow prediction models, low efficiency in intelligent transportation big data mining, insufficient prediction accuracy, and weak generalization capabilities, this study focuses on the core spatiotemporal characteristics of traffic flow—time periodicity, volatility, spatial correlation, and aggregation—while incorporating the "5V" features of multi-source heterogeneous big data in intelligent transportation. It conducts research on big data mining and prediction models. First, it identifies and quantifies traffic flow spatiotemporal characteristics to establish a targeted quantitative indicator system. Second, it designs a full-process big data mining framework to complete multi-source data preprocessing and spatiotemporal correlation feature extraction. Building on this, it improves and optimizes the model by introducing a spatiotemporal attention mechanism based on LSTM and GCN algorithms, constructing a traffic flow prediction model that integrates spatiotemporal characteristics. Finally, the model's performance is validated through case studies. The results demonstrate that the proposed model significantly outperforms traditional models, with a minimum MAPE of 4.87%, a single-prediction time of 0.32 seconds, and strong real-time adaptability and scenario compatibility. This study enriches the theory of spatiotemporal analysis and big data integration in traffic flow, providing scientific decision-making support for intelligent transportation management and road network optimization, with important theoretical and practical application value.
Shukun Zhou· Academic Journal of Engineer...· 0 citations
The ability to forecast traffic conditions in urban environments is essential for intelligent transport systems because it provides proactive congestion management, traffic control and informed urban mobility planning. However, due to the extreme spatial and temporal volatility of traffic flow patterns, conventional statistical, machine learning, and sequence-based deep learning approaches either fail to account for the spatial relationships between highway segments or fail to sufficiently model long-range temporal dynamics. To address these gaps, the present study uses a Spatial-Temporal Graph Neural Network (STGNN) framework that can learn a combination of spatial and temporal relationships in road networks and changing time-varying patterns in traffic flow. The model has a graph-based architecture where graph-convolutional layers are combined with gated recurrent units and transformer-based attention units, thus creating a hybrid architecture that is capable of multi-scale spatio-temporal dependencies. The METR-LA dataset was used in experiments, and it was observed that STGNN had smaller Mean Absolute Percentage Error(MAPE), Mean Absolute Error(MAE), and Root Mean Squared Error(RMSE) at 15, 30, and 60 minute horizons of prediction than the baseline and the implemented models: Sequence to Sequence (Seq2Seq) and Temporal Graph Convolutional Network (T-GCN). The model achieved the best results for the 15-minute interval with an MAE of 2.71, an RMSE of 5.17, and a MAPE of 7.08. The results imply that adaptive spatial learning, together with the temporal sequence modeling, can produce much better forecasting stability, which underlines the potential STGNN-based traffic prediction systems have to contribute to real-time traffic control.
Traffic congestion is a growing challenge in urban environments, driven by increasing population and vehicle density, leading to significant economic and societal impacts. Accurate traffic forecasting is a key component of Cooperative Intelligent Transportation Systems (C-ITS), enabling proactive traffic management strategies such as adaptive signal control and dynamic routing. However, existing approaches often struggle to capture spatial dependencies in road networks and long-range temporal dynamics in traffic data. This paper proposes TETRA, a hybrid spatio-temporal traffic forecasting model that integrates Graph Convolutional Networks (GCNs) with Extended Long Short-Term Memory (xLSTM). By incorporating matrix-based memory and memory mixing, xLSTM enables the model to capture complex multi-timescale temporal patterns, including congestion propagation and delayed recovery dynamics, which are not well represented by conventional recurrent models. The proposed approach is evaluated on a real-world urban traffic dataset and the widely used METR-LA benchmark. Experimental results show that TETRA outperforms or matches established baseline models representative of the main spatio-temporal paradigms, with the most pronounced gains at medium- and long-term horizons, achieving up to 13.0% lower MAE, 20.0% lower RMSE, and 6.0% higher
$$R^2$$
on a real-world dataset relative to the strongest investigated baseline at each horizon. Statistical analysis confirms that these improvements are robust across prediction horizons. Additional evaluations, including ablation and sensitivity analyses, demonstrate the effectiveness and scalability of the proposed model.
Norman Bereczki, Vilmos Simon· International Journal of Int...· 0 citations
A robust focused comparative evaluation of seven traffic forecasting approaches suggests that traffic forecasting models should be assessed not only by clean-data accuracy but also by their robustness under degraded sensing conditions before deployment in real intelligent transportation systems.
Shreya N. Desai, Kasim Ishaque Ghanchi, Ali Mehdi Mirza et al.· International journal of res...· 0 citations