ABSTRACT In urban rail transit systems, due to their efficiency and punctuality, metro systems have become the preferred choice for daily commuting. Accurate metro passenger flow prediction is crucial for ensuring stable system operations and optimizing resource allocation. In recent years, Graph Convolutional Networks (GCNs) have been widely adopted to extract spatial features in traffic flow data. However, they still face challenges in capturing complex global spatial dependencies, especially latent associations between different traffic nodes. Moreover, existing methods often focus on local neighborhood information, which makes it difficult to fully model the widespread correlations among regions with similar functional characteristics. To address these issues, this paper proposes a Multi‐View Fusion Graph Convolutional Network (MVFGCN) model, which introduces a multi‐view fusion strategy to capture spatial features of traffic flow from multiple perspectives. This enhances the model's capability to represent global spatial dependencies among different traffic nodes. In addition, a functional‐region‐based hypergraph construction method is designed, which includes node functional region recognition using K‐means and DTW algorithms and the generation of a functional‐region‐based hypergraph. This approach effectively captures correlations among nodes with similar periodic characteristics. By combining multi‐view graph convolution and self‐attention convolution, the proposed method can more effectively capture spatiotemporal features in traffic networks, leading to more precise traffic flow forecasts. Tests on real‐world datasets from metro systems and highways show that the proposed method significantly outperforms several mainstream models in prediction accuracy, validating the effectiveness and robustness of MVFGCN in complex urban traffic scenarios.

ABSTRACT To overcome the difficulties of avoiding obstacles in real time and being vulnerable to local optima in multi‐robot path planning (MRPP) in unfamiliar locations, a lens imaging reverse‐based learning Harris Hawks optimization algorithm (LRHHO) is proposed. Initially, the Latin hypercube sampling method is employed for population initialization to enhance the diversity and uniformity of the population. Subsequently, during the local exploitation phase, the lens imaging reverse‐based learning strategy is introduced to refine individual position updating mechanisms. It is complemented by an adaptive roulette wheel mechanism designed to select between current solutions and their reverse‐based counterparts. Finally, a restart mechanism is implemented for inferior individuals to improve the overall evolutionary efficacy of the population. Comprehensive evaluations on benchmark test functions demonstrate the superior optimization performance of LRHHO compared to existing algorithms. A real‐time MRPP model is constructed utilizing relative positioning methods, where LRHHO optimizes robots' velocity and angular parameters to determine subsequent positions. The system integrates three coordinated obstacle avoidance mechanisms: static obstacle avoidance, dynamic obstacle avoidance, and inter‐robot coordination, enabling rapid responses to randomly moving and unforeseen obstacles. Simulation experiments conducted in two scenarios with varying complexity levels reveal that the proposed method achieves intelligent real‐time obstacle avoidance while coordinating concurrent multi‐robot operations. Comparative results indicate that LRHHO generates higher‐quality paths with enhanced efficiency relative to alternative algorithms.

In this paper, the process of evacuation in teaching building is considered. The concept of steady degree based on cellular automata and potential field is introduced and it can describe the behavior tendency of evacuees during the evacuation process. With the help of steady degree, the model simulates the indoor evacuation behavior. To reduce the congestion and evacuation time, a multi-objective optimization model considering steady degree and evacuation clearance time is proposed. Finally, an experiment in the Teaching Building No.1 of Wuhan University of Technology is carried out. The results show that this model can reduce the clearance time of emergency evacuation in teaching building compared to other models.

In this paper, a space-time simulation model based on particle swarm optimization algorithm for stadium evacuation is presented. In this new model, the fast evacuation, going with the crowd and the panic behaviors are considered and the corresponding moving rules are defined. The model is applied to a stadium and simulations are carried out to analyze the spacetime evacuation efficiency by different behaviors. The simulation results show that the behaviors of going with the crowd and panic will slow down the evacuation process while quickest evacuation psychology can accelerate the process, and panic is helpful to some extent. The setting of parameters is discussed to obtain best performance. The simulation results can offer effective suggestions for evacuees under emergency situation.

Multi-objective evacuation routing optimization problem is defined to find out optimal evacuation routes for a group of evacuees according to multiple evacuation objectives. For improving the evacuation efficiency, we abstracted the evacuation zone as a positive-point-charge-potential-field-like model (PPCPF-like model), and we proposed PPCPF-ACO algorithm to solve this problem based on the proposed model. In PPCPF-ACO algorithm, we use non-dominated sorting based roulette wheel routing method (NSRWR) to further improve evacuation efficiency. In Wuhan Sports Center case, we compared PPCPF-ACO with HMERP-ACO (hierarchical multi-objective evacuation routing problem - ant colony optimization) and traditional ACO according to three evacuation objectives, namely, total evacuation time, total evacuation route length and cumulative congestion degree. The experimental results show that PPCPF-ACO has a better performance than HMERP-ACO algorithm and traditional ACO algorithm while solving multi-objective evacuation routing optimization problem.

Evacuation routing problem with mixed traffic flow is complex due to the interaction among different types of evacuees. The positive feedback mechanism of single ant colony system may lead to congestion on some optimum routes. Like different ant colony systems in nature, different components of traffic flow compete and interact with each other during evacuation process. In this paper, an approach based on multi-ant colony system was proposed to tackle evacuation routing problem with mixed traffic flow. Total evacuation time is minimized and traffic load of the whole road network is balanced by this approach. The experimental results show that this approach based on multi-ant colony system can obtain better solutions than single ant colony system and solve mixed traffic flow evacuation problem with reasonable routing plans.