The application of connected automated vehicles (CAVs) provides new opportunities and challenges for optimizing and controlling urban intersections. To avoid collisions of vehicles in conflicting directions at intersections and improve the efficiency of intersections, an optimal scheduling model for CAVs at an unsignalized intersection is proposed. The model develops a linear programming model of intersection vehicle timing with the minimum average vehicle delay within the optimization time window as the optimization objective and the minimum safe time interval for vehicles to pass through the intersection as the constraint. A rolling optimization algorithm is designed to improve the efficiency of the algorithm solution. Finally, the effects of different traffic demand conditions on the results are investigated based on numerical simulation experiments. The results show that both the proposed algorithm and the Gurobi solver can significantly reduce the average vehicle delay compared with the first-come-first-served (FCFS) control method, and the proposed model and algorithm can reduce the average vehicle delay by 76.22% at most. Compared with the Gurobi solver, the proposed model and algorithm can reduce the solution time and ensure the optimization effect to the greatest extent. Therefore, the proposed model and algorithm provide theoretical support for managing CAVs at unsignalized intersections.

In this paper, we explore the trade-offs between public and private investment in autonomous driving technologies. Utilizing an evolutionary game model, we delve into the complex interaction mechanisms between governments and auto manufacturers, focusing on how strategic decisions impact overall outcomes. Specifically, we predict that governments may opt for strategies such as constructing and maintaining infrastructure for Roadside Infrastructure-based Vehicles (RIVs) or subsidizing high-level Autonomous Driving Vehicles (ADVs) without additional road infrastructure. Manufacturers’ choices involve deciding whether to invest in RIVs or ADVs, depending on governmental policies and market conditions. Our simulation results, based on scenarios derived from existing economic data and forecasts on technology development costs, suggest that government subsidy policies need to dynamically adjust in response to manufacturers’ shifting strategies and market behavior. This dynamic adjustment is crucial as it addresses the evolving economic environment and technological advancements, ensuring that subsidies effectively incentivize the desired outcomes in autonomous vehicle development. The findings of this paper could serve as valuable decision-making tools for governments and auto manufacturers, guiding investment strategies that align with the dynamic landscape of autonomous driving technology.

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.

Accurate short-term traffic forecasting and efficient dynamic route guidance are pivotal for mitigating urban congestion. However, accurately capturing complex temporal dependencies and nonlinear variations in traffic flow remains challenging, while conventional path-planning algorithms may also suffer from local-optimum problems. To address these challenges, this study proposes a hybrid framework integrating an optimized prediction model with an enhanced ant colony optimization algorithm. First, a GA-BiGRU-ATT model is developed for traffic state prediction. By combining a bidirectional gated recurrent unit (BiGRU), a temporal attention mechanism, and genetic algorithm-based hyperparameter optimization, the model captures contextual temporal dependencies within the observed historical input window and emphasizes critical time-step features. Under the evaluated model configurations, the proposed approach achieved traffic-state classification accuracies of 90.28% and 87.50% on Segments 1 and 2, respectively. Second, based on the predicted traffic states, an Improved Ant Colony Algorithm (IACA) incorporating traffic-state feedback is proposed to mitigate slow convergence and local-optimum entrapment in conventional ACO. Within the ant-colony-based comparison, the IACA reduced the average computational time by 49.57% relative to the conventional ACA. Furthermore, under the evaluated SUMO evening-peak scenario, periodic dynamic guidance reduced the average travel time of the selected guided vehicles by 12.06% and increased their average speed by 27.07%. These results demonstrate the potential of prediction-guided dynamic rerouting under the evaluated simulation conditions.