Investigating the fundamental link between microscopic vehicular motion parameters and macroscopic traffic flow states is pivotal for advancing refined traffic state estimation research and propelling the progression of Intelligent Transportation Systems. In this paper, a basic Transformer model has been optimized and extended by incorporating embedding and pooling layers, and the model’s hyperparameters have been finely tuned through random search cross-validation. The creation of the Generalized Optimized Transformer (GOT) model ensued, where the multi-head attention mechanism adeptly encapsulates all spatiotemporal dynamics inherent in traffic data. Various benchmark models such as LSTM, RNN, and Transformer were put to test, each demonstrating unique performances in managing different traffic flow states. Among them, the GOT model exhibited superior performance, adeptly handling lane-level traffic state estimation tasks derived from microscopic vehicle trajectory data. In conclusion, this research elucidates the intricate and mutable mapping relationship between microscopic vehicular motion parameters and traffic flow states, proficiently executing lane-level traffic state estimation grounded on microscopic trajectory data. This paper is anticipated to provide fresh insights into the understanding of the complex relationship between microscopic vehicular motion parameters and traffic flow states.

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.

This paper proposes event-triggered pinning graph reinforcement learning (EPGRL) for cooperative ramp merging in mixed traffic with connected and automated vehicles (CAVs) and human-driven vehicles. EPGRL represents vehicle interactions as a time-varying directed graph and uses a dual-stream graph encoder with temporal updating to capture evolving traffic couplings. A Lyapunov-based trigger activates cooperation when a stability-oriented surrogate index indicates loss of contractivity, while topology-aware pinning allocates control to influential CAVs. A PPO policy generates longitudinal and lateral actions, which are filtered by a safety shield. SUMO experiments across traffic demands and CAV penetration rates show that EPGRL improves travel time, fuel use, CO₂ emissions, and system-level time-to-collision relative to uncontrolled, infrastructure-based, and learning-based baselines. Event-timing and spatial analyses characterize the distribution of cooperative updates, while the control-update reduction rate ranges from 73.1% to 79.8% across representative settings. Ablation experiments further show that graph modeling, instability-aware triggering, and pinning selection make complementary contributions. These results indicate that selective, state-dependent cooperation can balance mobility, safety, energy use, and communication cost in simulated mixed-traffic merging.

This study proposes an enhanced key-node-driven framework for traffic flow prediction in large-scale transportation networks. Building upon the classical K-shell decomposition, the proposed method integrates traffic-flow-based weighting to jointly capture structural hierarchy, functional relevance, and global topological influence of network nodes. By ranking node importance through composite indicators lambda-c, lambda-f, and lambda-s, the framework identifies structurally dominant nodes and evaluates their effectiveness across multiple traffic prediction models. Comprehensive experiments conducted on three benchmark datasets—PEMS04, METR-LA, and PEMS-BAY—demonstrate that graph-based spatiotemporal models such as ST-GCN, GraphWaveNet, DCRNN, STDN, STTN, and SWAVE maintain high predictive accuracy even under reduced node coverage. In particular, DCRNN exhibits strong robustness in capturing dynamic spatiotemporal dependencies, while STTN effectively models long-term temporal patterns. Overall, the results highlight the robustness and versatility of the proposed framework in real-world traffic scenarios, demonstrating its potential to enhance prediction accuracy and scalability through key-node identification and selective coverage analysis.