Yanni Ju

Road traffic accident consequences are multidimensional, involving fatalities, injuries, and property loss. Existing studies have mainly focused on single outcomes, limiting the understanding of heterogeneous mechanisms across different consequence dimensions. Based on road accident data from Yancheng City in 2022, this study develops an integrated framework combining multi-output prediction, NSGA-II multi-objective optimization, SHAP-based interpretation, LOWESS nonlinear analysis, and DirectLiNGAM causal inference. The results show that the optimized Voting ensemble achieved competitive and comparatively balanced performance across the three accident-consequence dimensions. Road category, traffic control type, junction/road-segment type, and crash-cause category are identified as key influencing factors, with differentiated effects across accident consequences. POI variables exhibit nonlinear and threshold effects, while causal analysis further indicates that road infrastructure and traffic control conditions are positioned upstream in the formation mechanism of accident consequences. This study provides evidence for multidimensional accident-consequence category prediction and differentiated traffic safety management, rather than traditional continuous regression-based modeling.

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.

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.

The transition to mixed traffic flows comprising Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HVs) induces a fundamental spatial reconfiguration of risk in freeway merging areas. This study proposes a novel spatiotemporal safety assessment framework to characterize the dynamic evolution of risk hotspots. Unlike traditional models, the framework integrates a conflict prediction model based on Negative Binomial regression with a high-resolution, grid-based risk mapping technique. By applying this framework to data from a microscopically simulated and carefully calibrated environment, we successfully identify a distinct migration pattern of risk hotspots: as CAV penetration increases, high-risk zones shift from the static geometric bottleneck at the ramp merge point to a dynamic interaction interface on the mainline. This paradigm shift is further quantified using a multi-dimensional indicator system. A case study demonstrates that increasing the CAV penetration rate from 10% to 50% can improve the safety grade of a merging area from D (Poor) to A (Excellent). The proposed framework provides a practical tool for refined safety diagnostics and offers insights for spatiotemporal risk analysis, informing the development of future cooperative control strategies in mixed traffic environments.

The efficient dispatch of rescue teams (RTs) during traffic accidents is crucial for the rapid restoration of normal operations in the affected urban road network (URN), thereby enhancing the network’s resilience during such events. However, previous studies focusing on optimizing RT dispatch strategies to enhance URN resilience remain limited. To address this gap, this paper develops a mixed-integer linear programming model aimed at optimizing RT dispatch during traffic accidents. The formulated model is solved using the commercial solver (i.e., CPLEX). Numerical experiments conducted on a hypothetical URN demonstrate that the model generates an optimal dispatch scheme. Compared to baseline strategies, the optimized scheme reduces the total objective function values by 27.36% in small-scale cases and 16.28% in large-scale case, respectively. Furthermore, sensitivity analysis reveal that accident severity and destination locations significantly influence the dispatch scheme design. Finally, the paper discusses the impact of several parameters on the model’s solution, showing that its performance is highly sensitive to several critical factors like RT dispatch costs, the maximum allowable delay time, passenger value of time, and vehicle travel speeds.

Accurate traffic flow prediction plays a vital role in intelligent transportation systems, helping traffic management departments maintain stable traffic order, reduce traffic congestion, and improve road safety. Existing prediction methods focus on dynamic modeling of the spatiotemporal dependencies of traffic flow, capturing the periodicity and spatial heterogeneity in traffic data. However, they still suffer from a lack of focus on the important local information in long-term predictions, leading to overly smooth results that fail to effectively capture sudden changes in traffic patterns. To address these limitations, we propose the sLSTM-Attention-Based Multi-Head Dynamic Graph Convolutional Network (sAMDGCN) model. Specifically, we extend sLSTM and introduce temporal trend-aware multi-head attention to jointly capture the complex temporal dependencies. We propose a multi-head dynamic graph convolutional network to capture a wider range of dynamic spatial dependencies. To validate the effectiveness of sAMDGCN, we perform extensive experiments on four real-world traffic flow datasets. Experimental results show that our proposed sAMDGCN model outperforms the advanced baseline methods in long-term traffic flow prediction tasks, demonstrating its superior performance in capturing complex and dynamic traffic patterns.