Time-Sensitive Networking (TSN) employs shaping mechanisms such as Time-Aware Shaping (TAS) and Cyclic Queuing and Forwarding (CQF), which depend heavily on precise time synchronization and complex Gate Control Lists (GCL) configurations, limiting their effectiveness in large-scale mixed traffic networks like those in vehicular systems. In response, IEEE 802.1Qcr protocol introduces the Asynchronous Traffic Shaping (ATS) mechanism, based on Urgency-Based Schedulers (UBS), to asynchronously address diverse traffic needs and ensure low and predictable latency. Nonetheless, no traffic scheduling algorithm exists that can be directly applied to ATS shapers in generic large-scale traffic scenarios to solve for fixed end-to-end (E2E) delay constraints and the number of priority queues.In this paper, we propose an urgency-based fast flow scheduling algorithm (UBFS) to address the issue. UBFS leverages domain-specific optimizing strategies with a focus on traffic delay urgency inspired by greedy algorithm for priority allocation across hops and flows, complemented by preprocessing for scenario solvability and dynamic verification to ensure scheduling feasibility. We benchmark UBFS against the method with both scalability and solution quality in typical network topology and demonstrate that UBFS achieves more rapid scheduling within seconds across linear, ring, and star topologies. Notably, UBFS significantly outperforms the baseline algorithm in scheduling efficiency in mixed and large-scale traffic environments, scheduling a larger number of flows. UBFS also reduces time costs by 2-10 times in delay-sensitive environments and by more than 10 times in large-scale scenarios, effectively balancing time efficiency, performance and scalability, thereby enhancing its applicability in real-world industrial settings.

Time-Sensitive Networking (TSN) achieves low-delay and low-jitter traffic transmission through different traffic scheduling mechanisms. However, despite numerous algorithms developed based on these mechanisms, most fail to concurrently support multipath, hybrid, and multicast traffic, which are prevalent in real-world scenarios. Moreover, balancing performance metrics such as success rate, bandwidth utilization, and computation overhead remains challenging for these algorithms, significantly limiting their application in diverse TSN scenarios. To solve this problem, this paper proposes a universal ultra-low-delay and zero-jitter traffic scheduling model. Based on this model, this paper further designs a performance-balanced algorithm. The algorithm improves traffic scheduling success rate through joint routing and scheduling, increases network bandwidth utilization through hybrid traffic scheduling, and achieves low computation overhead through policy-based searching. Finally, extensive experiments demonstrate that the algorithm effectively balances performance metrics across diverse real-world scenarios. It achieves high scheduling success rate under real-world traffic loads ($\gt $20% improvement over non-joint routing), increased bandwidth utilization in the presence of hybrid traffic (18.3% enhancement over non-hybrid traffic scheduling), and low computation overhead ($\lt $2 minutes).

Data mining is the process of extracting implicit, valuable, and interesting information from large sets of data. Visualization is the process of visually exploring data for pattern and trend analysis, and it is a common method of browsing spatial datasets to look for patterns. However the growing volume of spatial datasets make it difficult for humans to browse such datasets in their entirety, and data mining algorithms are needed to filter out large uninteresting parts of spatial datasets. We construct a web-based visualization software package for observing the summarization of spatial patterns and temporal trends. We also present data mining algorithms for filtering out vast parts of datasets for spatial outlier patterns. The algorithms were implemented and tested with a real-world set of Minneapolis-St. Paul (Twin Cities) traffic data.