Exploring Bus Stop Mobility Pattern With Temporal Variation: A Multiperiod Prediction Framework

TA-STGAT: A Spatio-Temporal Graph Attention Network for Edge-State Prediction in Vehicular Edge Computing

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.

Accurate ETA Prediction Using Dynamic Route-Aware Graph Neural Networks for Improved Urban Mobility and Well-Being

Traffic Flow Prediction System Based on Spatiotemporal Graph Neural Network

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.

An enhanced dynamic spatiotemporal residual network with multi-scale gridding for network-scale traffic speed prediction

Network-scale traffic speed prediction plays a central role in signal timing, routing, traffic control and congestion management. However, existing methods face challenges in capturing complex spatiotemporal dependencies among road segments, and in achieving computational efficiency in large-scale urban networks. To address these challenges, we propose an enhanced Dynamic Spatiotemporal Residual Network (DST-ResNet) framework for network-scale traffic speed prediction. First, the method employs a multi-scale grid partitioning strategy to segment urban road networks at varying levels of granularity, enabling precise predictions at both local and global scales. Second, a dynamic convolution mechanism is introduced to integrate real-time traffic data with static road network features, allowing the model to adaptively capture the dynamic and heterogeneous characteristics of urban traffic. Then, a spatiotemporal attention mechanism is incorporated to capture sequential dependencies in traffic patterns, improving the accuracy and robustness of long-term traffic speed prediction. Last, we conduct experiments based on GPS trajectory data from taxis in Xi’an city, China. The results demonstrate that DST-ResNet outperforms five baseline models. The model maintains stable performance across five spatial resolutions, and a mask-aware retraining strategy reduces the average MSE by 49.65% under missing observations. Ablation studies confirm the contributions of dynamic convolution and attention mechanisms, while residual units mitigate the vanishing gradient problem, facilitating deeper network training.

Big Data-Based Analysis of Urban Traffic Congestion: A Case Study of Beijing

As urbanization continues to reshape large cities, traffic congestion remains a persistent challenge for urban transportation systems. Using Beijing as a case study, this paper examines the spatiotemporal evolution of urban traffic congestion from a deep learning perspective based on multi-source data. The results suggest that traffic congestion in Beijing displays a pronounced “dual-peak” pattern associated with daily commuting activities. Compared with the pre-pandemic period, weekday travel demand has generally recovered and in some cases exceeded previous levels, whereas holiday travel remains relatively subdued, accompanied by increasingly concentrated travel behavior. Among the models considered, Long Short-Term Memory (LSTM) performs particularly well in capturing nonlinear variations and temporal dependencies in traffic flow, leading to improved prediction accuracy. Between 2020 and 2025, the congestion index experienced a trajectory of decline, recovery, and subsequent stabilization, a pattern that appears to be associated with the gradual implementation of intelligent traffic management measures. These findings contribute to a better understanding of recent changes in urban traffic dynamics and may offer useful insights for future traffic planning and governance.

Navi Bus: A Smart Urban Mobility and Passenger Intent Prediction Platform

Population explosion in cities has led to an ever-increasing need of smart, efficient, dynamic, and affordable transportation. Current public transport systems are mostly static, bound to certain routes and time-tables, limiting their adaptability to dynamic changes in demand and availability. Furthermore, these systems often lead to problems of buses missing stops, long wait times for passengers, and inadequate information for passengers to reach their destinations on time. Navi bus: A Smart Urban Mobility and Passenger Intent Prediction System for Intelligent Transportation is an alternative approach designed to tackle these problems. The proposed Navi Bus platform integrates intelligent routing algorithms with real-time GPS and passenger request data to optimize bus operations, minimize waiting time, and improve service efficiency. The Adaptive System Initialization and Resource Management Algorithm (ASIRMA) facilitate initialization of the system and allocation of resources, while Real-Time Passenger Monitoring and Interaction Algorithm (RPMIA) deals with monitoring and interaction of passengers in real time. Intelligent Request Handling and Decision Optimization Algorithm (IRHDOA) processes requests of passengers by calculating the optimal route, and calculating any deviations or delays that may occur due to incorporation of new stops. Demand-Aware Dynamic Routing and Service Algorithm (DADRSA) dynamically plans the route and service by incorporating new pickup points without incurring operational risks. The proposed system is expected to minimize wait time for passengers, enhance communication between drivers and passengers, and pave the way for future smart transport systems utilizing AI for passenger intent prediction to make smarter decision while planning routes.