Physica A: Statistical Mechanics and its Applications最新文献

This study investigates multidirectional pedestrian flow by examining walking characteristics and behavioral factors impacting pedestrian dynamics. Parameters such as mean walking velocities, distance covered, and time taken by pedestrians were analyzed. Contrary to the expected inverse relationship between velocity and density, our findings revealed an increase in mean velocities in certain scenarios, attributed to conditions where the pedestrian density had not reached its maximum possible level and unpredicted pedestrian behaviors. Comparative analysis revealed that both Pakistani and Chinese pedestrians walk at nearly similar speeds. For instance, Pakistani pedestrians maintain a mean velocity of 1.36 ± 0.42 m/s at a density of 0.48 ped/m², while Chinese pedestrians maintain 1.27 ± 0.23 m/s at a density of 0.41 ped/m². Behavioral characteristics such as aggressive behavior, crowd avoidance, and waiting behavior were identified, with aggressive pedestrians exhibiting higher velocities than those avoiding crowds. A space-time diagram was utilized to categorize pedestrian flow types and behaviors, demonstrating increased stopping times with higher pedestrian numbers. Cultural factors, including side preferences, were analyzed using a binomial test, which returned a p-value of 0.968, indicating no significant preference for right-hand movement among Pakistani participants. These insights enhance the understanding of pedestrian dynamics and the influence of cultural norms on movement efficiency.

We consider the duration of discussions in face-to-face contacts and propose a stochastic model to describe it. It is based on the points of a Levy flight where the duration of each contact corresponds to the size of the clusters produced during the walk. When confronting it to the data measured from proximity sensors, we show that several datasets obtained in different environments, are precisely reproduced by the model fixing a single parameter, the Levy index, to 1.15. We analyze the dynamics of the cluster formation during the walk and compute analytically the cluster size distribution. We find that discussions are first driven by a maximum-entropy geometric distribution and then by a rich-get-richer mechanism reminiscent of preferential-attachment (the more a discussion lasts, the more it is likely to continue). In this model, conversations may be viewed as an aggregation process with a characteristic scale fixed by the mean interaction time between the two individuals.

The weighted planar stochastic (WPS) lattice introduces a topological disorder that emerges from a multifractal structure. Its dual network has a power-law degree distribution and is embedded in a two-dimensional space, forming a planar network. We modify the original recipe to construct WPS networks with degree distributions interpolating smoothly between the original power-law tail, $P\left(q\right)\sim q^{-\alpha }$ with exponent $\alpha \approx 5.6$, and a square lattice. We analyze the role of the disorder in the modified WPS model, considering the critical behavior of the contact process (CP). We report a critical scaling depending on the network degree distribution. The scaling exponents differ from the standard mean-field behavior reported for CP on infinite-dimensional (random) graphs with power-law degree distribution. Furthermore, the disorder present in the WPS lattice model is in agreement with the Luck-Harris criterion for the relevance of disorder in critical dynamics. However, despite the same wandering exponent $\omega =1/2$, the disorder effects observed for the WPS lattice are weaker than those found for uncorrelated disorder.

Urban rail transit passenger flow modeling is the foundation of urban rail transit planning, design, and operation. The motivation of this paper is to accurately and efficiently simulate passenger flow dynamics in urban rail transit systems. To this end, we propose a State-dependent Multi-agent Discrete Event Simulation (SdMaDES). The state-dependence (or congestion-dependence) means that the service abilities (or travel times) of bottleneck facilities (e.g. platform screen doors and transfer corridors) are not constant and they interact with the congestion dynamics. Specifically, we first establish a modular Multi-agent Discrete Event Simulation (MaDES), which includes four types of modules (passenger, train, station, and network modules) and three types of agents (passenger, train, and station agents). The logical connections and event-triggering modes between the modules are analyzed and defined, and thirty types of events are designed. The state-dependence is then captured by an Improved Social Force Model (ISFM), which adds an autonomous obstacle avoidance mechanism. The ISFM reproduces passenger movement behavior within bottleneck facilities of urban rail transit systems at the microscopic level. These state-dependent functions or general rules are explicitly formulated by fitting the results of ISFM and are subsequently applied to the proposed MaDES model, resulting in the SdMaDES. This integration aims to enhance the accuracy of the simulation. We conducted a real case from the Chengdu Metro network. Some interesting results are found. (a) The maximum number of boarding passengers in a train carriage is a complex nonlinear function that is dependent on the state (density inside the train carriage). This challenges the linear function commonly utilized in most studies. (b) Compared to the actual data, the proposed SdMaDES model shows a cumulative error of 9.85% after data smoothing, while the conventional MaDES model exhibits a much higher cumulative error of 21.9% after data smoothing. (c) As the overall traffic demand level increases, the gap between the two simulation models’ results is getting wider and wider due to the amplified nonlinear impact of congestion.

Cooperation and competition are two pivotal topics in the literature on the evolutionary dynamics of individual behavior on social networks. This study provides a perspective of joint analysis of social dilemmas within groups and inter-group competition in a mixed cooperative–competitive structured population. Although specific mechanisms for interpreting the emergence and promotion of cooperation have been proposed, including reward, punishment, reputation, and environmental factors, little is known about how inter-group competition affects the cooperation level of groups, especially the intensity and structures of competitive games. Based on multi-games, a mixed cooperative–competitive game model is proposed that individuals within a group engage in a donation game and those from different groups participate in competitive games. The results of numerical simulations suggest that the reward of inter-group competition serves as an external incentive, fostering cooperation within groups. As the intensity of competitive games increases, cooperation within groups is prompted. Additionally, strategic inter-group connections based on degree heterogeneity significantly enhance within-group cooperation, particularly when hubs from one group engage in competitive games with marginal nodes from another group. This research contributes valuable insights into the dynamics of cooperation within groups considering the impact of inter-group competition on within-group cooperation.

A novel approach to accounting for the influence of initial system–bath correlations on the dynamics of an open quantum system, based on the conventional projection operator technique, is suggested. To avoid the difficulties of treating the initial correlations, the conventional Nakajima–Zwanzig inhomogeneous generalized master equations (GMEs) for a system’s reduced statistical operator and correlation function are exactly converted into the homogeneous GMEs (HGMEs), which take into account the initial correlations in the kernel governing the evolution of these HGMEs. In the second order (Born) approximation in the system–bath interaction, the obtained HGMEs are local in time and valid at all timescales. They are further specialized for a realistic equilibrium Gibbs initial (at $t=t_0$) system+bath state (for a system reduced statistical operator an external force at $t>t_0$ is applied) and then for a bath of oscillators (Boson field). As an example, the evolution of a selected quantum oscillator (a localized mode) interacting with a Boson field (Fano-like model) is considered at different timescales. It is shown explicitly how the initial correlations influence the oscillator evolution process. In particular, it is shown that the equilibrium system’s correlation function acquires at the large timescale the additional constant phase factor conditioned by survived initial system–bath correlations.

With the advancement of technology, connected and autonomous vehicles (CAVs) can be applied to complex tunnel networks in long tunnel construction to enhance vehicle operation safety and efficiency. This paper proposes an optimization method for CAVs' operation in long tunnel constructions. Firstly, a spatiotemporal coordinated optimization model with decentralized time and hierarchical networks is proposed to minimize the total working time for completing transportation services. The model integrates macro task allocation and micro node control and optimizes the vehicle-space-time relationships of CAVs to prevent conflicts and collisions. Secondly, a heuristic algorithm named Search-Adjustment Genetic Algorithm (SAGA) is developed to solve the problem considering the model's complexity and engineering characteristics. Thirdly, numerical experiments are designed to validate the feasibility and efficiency of the proposed model and algorithm. The results indicate that (1) the proposed model can effectively deconflict CAVs in the road network to ensure safety and obtain a low total working time to fulfill the transportation demand. (2) Compared to the commercial solver Gurobi, the proposed algorithm demonstrates significantly superior solution accuracy and efficiency within an acceptable time limit. (3) The solution ensures the safety and efficiency of CAVs and increases their utilization compared with engineering-oriented methods, resulting in a 50 % reduction in CAV acquisition costs, a 29 % and 85 % reduction in running time and delay respectively, and a reduction in fuel consumption. (4) As the number of transportation services and the complexity of the road network increases, the efficiency gains become more prominent and better adapted to the needs of the actual long tunnel construction project. To sum up, the proposed model and algorithm can ensure the safety and efficiency of providing transportation services in future long tunnel construction. Moreover, it can be adapted for controlling CAVs in road networks such as other construction scenarios and urban road networks.

During an emergency evacuation in a building, participants' evacuation signs obedience behaviors have a significant impact on evacuation efficiency, while participants' possible phototropism and hazard avoidance behaviors occur during evacuation, which will affect evacuation efficiency. To investigate the effects of participants' phototropism and hazard avoidance behaviors on compliance with evacuation signs. One pre-experiment and four groups of evacuation experiments were conducted in the T-junction and Room of the building with a total of 170 participants. A fumes generator and searchlight to simulate light and fumes conditions under fire. Using the statistical analysis to explore the differences in the route choice behavior of evacuees under different conditions. The results show that: (1) In the natural state, participants showed significant phototropism and hazard avoidance. (2) Phototropism and hazard avoidance affect participants' obedience to evacuation signs. (3) In the room scene, participants demonstrated more obvious phototropism and hazard avoidance, even deviating from the evacuation sign's guidelines；(4) Participants showed some decision inertia during two consecutive decisions. Further combined with SHAP interpretable machine learning for analysis, it was found that (1) Scene conditions exist for phototropism and hazard avoidance in evacuees. (2) Fumes prevented participants' obedience to the evacuation sign, whereas light promoted participants' obedience to the sign. This indicates that evacuation signs need to be installed regarding the actual building structure. These findings can be used as theoretical references for building safety design, campus safety, and evacuation simulation tests.

Urban traffic systems are facing significant challenges due to the ever-growing number of vehicles on the road, leading to increased congestion and suboptimal traffic flow. Traditional research focusing on individual traffic flows is often insufficient to meet the complex demands of modern urban transportation. While studying integrated shared single-vehicle flows offers a potential solution to mitigate these issues, the unique characteristics of shared bikes present substantial obstacles to accurate traffic flow research. These obstacles include the high liquidity, sparsity, and variability of shared bikes, the vagueness of travel characteristics, the lack of correlation between travel groups, and the unpredictability of travel patterns. The study endeavors to confront the challenges above by proposing an innovative model that correlates multiuser interactions and elucidates behavioral dynamics. This model utilizes a deep clustering method to analyze the evolution of superlarge-scale shared bike systems in Beijing. It uncovers the complex mechanisms governing user behavior and employs a neural network algorithm to predict shared bike users’ travel patterns effectively. By focusing on the theoretical and algorithmic aspects of behavioral dynamics for large-scale shared single-vehicle flows, this study offers a unique contribution to the field, with significant implications for multi-traffic flow management and urban planning in scenarios with extensive multi-traffic flows.

Collective behavior in animal groups exhibits intriguing dynamics that can be analyzed through the lens of self-organized criticality. In this context, we analyze behavioral cascades in zebrafish (Danio rerio) groups of varying sizes within controlled tank environments. Through experimental observations and data analysis, we unveil scale-free signatures reminiscent of self-organized critical processes in the collective movement of zebrafish. Notably, as fish density varies, we observe a dynamic phase transition: at low densities, coordinated and highly polarized movement dominates, while at high densities, the group fractures into uncorrelated domains. These findings shed light on the complex dynamics of collective behavior in fish groups and provide valuable insights into the responses of individuals to environmental stimuli. Moreover, the observed phase transition highlights the sensitivity of zebrafish behavior to changes in population density, which has implications for understanding collective behavior in various contexts, from ecological systems to preclinical studies. Finally, we compare our findings with the known results of avalanche analyses of collective motion and neuronal activity. All follow the same power law, indicating a possible universality in one parameter of avalanche processes.