Traffic prediction is essential for modern transportation systems, enhancing traffic management and urban planning. Accurate predictions of traffic flow and speed are crucial for understanding road usage, mitigating congestion, and providing real-time traffic monitoring and dynamic route guidance, thus improving road safety and infrastructure efficiency. Traditional research has often focused on predicting traffic flow or speed independently, leading to higher resource consumption due to the need for separate models. Few studies have explored the simultaneous prediction of both metrics, with recent attempts failing to account for spatial correlations, resulting in suboptimal performance. To address these challenges, we propose MTNet, a multi-task learning framework for joint traffic flow and speed prediction. MTNet employs a Transformer-like Encoder-Decoder architecture to process and enhance feature representations, capturing complex spatio-temporal correlations. Specifically, MTNet extracts intra-task dependencies using a cross-task interaction module and models task-specific spatiotemporal dependencies using spatial and temporal-aware modules with cascaded residual structures. Additionally, spatio-temporal positional encoding is integrated to increase awareness of long-term and long-distance dependencies. Extensive experiments on three diverse traffic datasets-Manchester, PeMSD4, and PeMSD8-demonstrate that MTNet significantly outperforms state-of-the-art methods in both traffic flow and speed prediction. MTNet achieves substantial improvements in prediction accuracy and efficiency, striking an optimal balance between performance and computational resource usage.

Data queries play a crucial role in the vehicular platoon, enabling vehicles to obtain traffic information about surrounding road conditions and personalized entertainment information services. However, data query requests from vehicles may expose the vehicle owner’s personal attributes and habits. Although several schemes can address these issues, they are incapable of countering collusion attacks between the coordinating vehicle and roadside units (RSUs). To solve this problem, in this paper we propose a privacy-preserving and collusion-resistant data query scheme, named PCDQ. Specifically, PCDQ uses the Paillier encryption and Chinese Remainder Theorem to protect the query privacy of vehicle owners, allowing the RSU to recover individual data query requests without associating them with the original vehicles. Next, the parameter update mechanism in PCDQ prevents the coordinating vehicle from obtaining the corresponding mapping information between vehicles and query parameters, thereby resisting collusion attacks between the coordinating vehicle and RSUs. In addition, identity-based signcryption is used to ensure secure parameter distribution among vehicles, and the batch verification enables efficient authentication of query requests. Detailed security proofs and analysis demonstrate that PCDQ satisfies multiple security properties, including resistance to collusion attacks and replay attacks, unlinkability, confidentiality, and authentication and data integrity. Experimental results show that, compared to existing solutions, PCDQ performs better in terms of computation overhead, communication overhead, and network performance.

Traffic flow forecasting on a large-scale sensor network is of great practical significance for policy decision-making, urban management, and transport planning. Recently, several prediction methods based on graph convolution have been put forward. However, they are limited to small-scale analyses because of high computation complexity and fail to integrate spatial-temporal dependencies sufficiently between sensors with a large topological distance in multiple time steps. To address these issues, we propose a novel deep framework called PFNet to perform large-scale traffic forecasting. PFNet captures temporal correlations using deep multi-view sequence encoders (DMVSE) and spatial correlations using graph embedding technologies on transportation networks. Spatial-temporal dependencies are more comprehensively fused using cascaded progressive attention (CPA) modules due to the full use of latent temporal and spatial representation. Experiments are conducted on two large-scale traffic datasets (LondonHW and ManchesterHW) from England Highway. The results demonstrate that PFNet outperforms several existing state-of-the-art approaches.