Pub Date : 2024-10-01DOI: 10.1016/j.physa.2024.130137
By safety supervision on dangerous driving behaviors, emergent braking in autonomous vehicles can effectively prevent collisions when using the car following algorithm based on deep reinforcement learning. However, the significant deceleration associated with emergent braking often results in an uncomfortable driving experience and high energy consumption. To address this issue, a soft collision avoidance based car following algorithm is proposed. Different from emergent braking, our approach introduces a deceleration adjustment value to the current acceleration output. This adjustment value is calculated by considering safe distance with attenuation coefficient in terms of multi-step prediction, while the attenuation coefficient and the predicted time step are discussed in detail. Comparative analysis, including statistical results and representative cases, demonstrates that the proposed algorithm significantly enhances driving comfort (improve 37.341 %) and reduces energy consumption (improve 11.244 %) without increasing collision risks.
{"title":"Soft collision avoidance based car following algorithm for autonomous driving with reinforcement learning","authors":"","doi":"10.1016/j.physa.2024.130137","DOIUrl":"10.1016/j.physa.2024.130137","url":null,"abstract":"<div><div>By safety supervision on dangerous driving behaviors, emergent braking in autonomous vehicles can effectively prevent collisions when using the car following algorithm based on deep reinforcement learning. However, the significant deceleration associated with emergent braking often results in an uncomfortable driving experience and high energy consumption. To address this issue, a soft collision avoidance based car following algorithm is proposed. Different from emergent braking, our approach introduces a deceleration adjustment value to the current acceleration output. This adjustment value is calculated by considering safe distance with attenuation coefficient in terms of multi-step prediction, while the attenuation coefficient and the predicted time step are discussed in detail. Comparative analysis, including statistical results and representative cases, demonstrates that the proposed algorithm significantly enhances driving comfort (improve 37.341 %) and reduces energy consumption (improve 11.244 %) without increasing collision risks.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.physa.2024.130133
The dynamic, interconnected nature of modern supply chains makes it important to understand how firm-level decision-making will impact the robustness of supply chains to disruption. The behavior of naturally evolved distribution systems offers a useful starting point to identify potential design features that can promote robustness without compromising the viability of individual firms. Drawing inspiration from how ant food-sharing networks respond to supply shortages, we developed an agent-based model of a generalized supply network and evaluated how different local strategies influenced the ability of firms to acquire sufficient resources to meet their demand. Our simulations reveal that differences among firms in strategic behavior can reduce variation in outcomes across firms while maintaining mean performance, thereby buffering system-level robustness. In addition, the ability to expand one’s supplier network bolstered performance when firms experienced difficulty in meeting their demand. Conversely, under the assumptions of our model, overly relying on distributors to gain access to additional suppliers or to gain competitive advantages was ineffective in helping firms to meet their consumptive demand. Our nature-inspired modeling framework provides a potentially useful approach for evaluating how different participant decision-making strategies may impact the robustness and resilience of global supply chains that are increasingly likely to face frequent and unpredictable disruptions.
{"title":"Nature-inspired design principles promote supply network resilience","authors":"","doi":"10.1016/j.physa.2024.130133","DOIUrl":"10.1016/j.physa.2024.130133","url":null,"abstract":"<div><div>The dynamic, interconnected nature of modern supply chains makes it important to understand how firm-level decision-making will impact the robustness of supply chains to disruption. The behavior of naturally evolved distribution systems offers a useful starting point to identify potential design features that can promote robustness without compromising the viability of individual firms. Drawing inspiration from how ant food-sharing networks respond to supply shortages, we developed an agent-based model of a generalized supply network and evaluated how different local strategies influenced the ability of firms to acquire sufficient resources to meet their demand. Our simulations reveal that differences among firms in strategic behavior can reduce variation in outcomes across firms while maintaining mean performance, thereby buffering system-level robustness. In addition, the ability to expand one’s supplier network bolstered performance when firms experienced difficulty in meeting their demand. Conversely, under the assumptions of our model, overly relying on distributors to gain access to additional suppliers or to gain competitive advantages was ineffective in helping firms to meet their consumptive demand. Our nature-inspired modeling framework provides a potentially useful approach for evaluating how different participant decision-making strategies may impact the robustness and resilience of global supply chains that are increasingly likely to face frequent and unpredictable disruptions.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.physa.2024.130132
This work demonstrates the application of a birth–death Markov process, inspired by radioactive decay, to capture the dynamics of innovation processes. Leveraging the Bass diffusion model, we derive a Gompertz-like function explaining long-term innovation trends. The validity of our model is confirmed using citation data, Google trends, and a recurrent neural network, which also reveals short-term fluctuations. Further analysis through an automaton model suggests these fluctuations can arise from the inherent stochastic nature of the underlying physics.
{"title":"Chain reaction of ideas: Can radioactive decay predict technological innovation?","authors":"","doi":"10.1016/j.physa.2024.130132","DOIUrl":"10.1016/j.physa.2024.130132","url":null,"abstract":"<div><div>This work demonstrates the application of a birth–death Markov process, inspired by radioactive decay, to capture the dynamics of innovation processes. Leveraging the Bass diffusion model, we derive a Gompertz-like function explaining long-term innovation trends. The validity of our model is confirmed using citation data, Google trends, and a recurrent neural network, which also reveals short-term fluctuations. Further analysis through an automaton model suggests these fluctuations can arise from the inherent stochastic nature of the underlying physics.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-30DOI: 10.1016/j.physa.2024.130119
In the transportation network, we observe that the distance people travel by means of transportation follows a certain distribution. Statistical analysis shows that people’s travel distance is mainly concentrated in a medium range by the same vehicle, and they choose fewer destinations that are extremely close or far away. However, in previous studies, the impact of distance on the distribution of load flow within the network has often been neglected, or at best, addressed with overly simplistic assumptions. Therefore, we quantify the load flow distribution based on the Gaussian distribution of distances between the nodes. On this basis, a new cascading failure model is proposed using the shortest path strategy to calculate the initial load of the edge. Through the simulation of three real traffic networks and two artificially constructed networks with similar structural characteristics of traffic networks, we found the following interesting anomalies: First, increasing the load-bearing capacity of edges within the network does not necessarily lead to enhanced robustness. Second, we observed that removing more edges does not necessarily lead to a decrease in network robustness; conversely, the network robustness can be higher when a moderate number of edges are removed compared to fewer edges. To better understand the two anomalous dynamics phenomena we observed, we ran simulations on a small-scale network extracted from a real traffic network. We found that, under certain circumstances, the premature failure of some edges may isolate certain regions from the network, which may be responsible for this paradox.
{"title":"Abnormal cascading dynamics in transportation networks based on Gaussian distribution of load","authors":"","doi":"10.1016/j.physa.2024.130119","DOIUrl":"10.1016/j.physa.2024.130119","url":null,"abstract":"<div><div>In the transportation network, we observe that the distance people travel by means of transportation follows a certain distribution. Statistical analysis shows that people’s travel distance is mainly concentrated in a medium range by the same vehicle, and they choose fewer destinations that are extremely close or far away. However, in previous studies, the impact of distance on the distribution of load flow within the network has often been neglected, or at best, addressed with overly simplistic assumptions. Therefore, we quantify the load flow distribution based on the Gaussian distribution of distances between the nodes. On this basis, a new cascading failure model is proposed using the shortest path strategy to calculate the initial load of the edge. Through the simulation of three real traffic networks and two artificially constructed networks with similar structural characteristics of traffic networks, we found the following interesting anomalies: First, increasing the load-bearing capacity of edges within the network does not necessarily lead to enhanced robustness. Second, we observed that removing more edges does not necessarily lead to a decrease in network robustness; conversely, the network robustness can be higher when a moderate number of edges are removed compared to fewer edges. To better understand the two anomalous dynamics phenomena we observed, we ran simulations on a small-scale network extracted from a real traffic network. We found that, under certain circumstances, the premature failure of some edges may isolate certain regions from the network, which may be responsible for this paradox.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-30DOI: 10.1016/j.physa.2024.130135
This paper addresses the challenges of analyzing stochastic dynamical systems with a single time-delay within a data-driven framework. The presence of time-delay introduces non-Markovian characteristics to the system, complicating the analysis of its dynamic behavior using traditional approaches. Drawing inspiration from the small delay approximation, we apply a sparse identification technique to approximate the non-Markovian process with a Markovian one. This innovative method circumvents limitations associated with the system's dimensionality and the complexity of delayed diffusion terms, offering a versatile tool for investigating the dynamics of stochastic time-delayed systems. Our approach begins by establishing a connection between the system's coefficients and simulated data using the Kramers-Moyal formula, which captures the essential statistical properties of the system. We then leverage sparse identification to extract a concise model of the stochastic dynamical system, effectively eliminating the time-delay from consideration. The practicality and efficacy of our method are substantiated through a series of illustrative examples that showcase its application and validate its performance. By introducing this method, we aim to provide a novel analytical framework for stochastic time-delayed systems, advancing the current capabilities for modeling and understanding such complex dynamics.
{"title":"Approximation identification for the stochastic time-delayed dynamical system","authors":"","doi":"10.1016/j.physa.2024.130135","DOIUrl":"10.1016/j.physa.2024.130135","url":null,"abstract":"<div><div>This paper addresses the challenges of analyzing stochastic dynamical systems with a single time-delay within a data-driven framework. The presence of time-delay introduces non-Markovian characteristics to the system, complicating the analysis of its dynamic behavior using traditional approaches. Drawing inspiration from the small delay approximation, we apply a sparse identification technique to approximate the non-Markovian process with a Markovian one. This innovative method circumvents limitations associated with the system's dimensionality and the complexity of delayed diffusion terms, offering a versatile tool for investigating the dynamics of stochastic time-delayed systems. Our approach begins by establishing a connection between the system's coefficients and simulated data using the Kramers-Moyal formula, which captures the essential statistical properties of the system. We then leverage sparse identification to extract a concise model of the stochastic dynamical system, effectively eliminating the time-delay from consideration. The practicality and efficacy of our method are substantiated through a series of illustrative examples that showcase its application and validate its performance. By introducing this method, we aim to provide a novel analytical framework for stochastic time-delayed systems, advancing the current capabilities for modeling and understanding such complex dynamics.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-28DOI: 10.1016/j.physa.2024.130130
Bounded confidence models (BCM) are extensively used to model continuous opinion dynamics in social networks. Typically, these models are analysed on static networks where edges do not vary over time. Following in the footsteps of adaptive voter models, further research has considered BCMs in the setting where agents are able to dynamically adjust their edges, which subsequently feedback into the opinion dynamics of the network. Several methods of updating connections have been proposed ranging from random rewiring to more sophisticated approaches based on concordant edges, homophily and cognitive dissonance. We present a modified form of the bounded confidence model, termed the selfish agent opinion (SAO) model, where connection updates are evaluated using a general cost function. Agents in the SAO model maintain two classes of relationships, friends and acquaintances, based on which they update their opinions and edges to optimise a payoff function that may include multiple social factors. This paper explores the effects, which we describe as “cognitive dissonance” and “introversion” that attempt to mimic postulated behaviours associated with the social concepts of the same name, on social network and opinion dynamics. We find that the SAO model naturally produces echo chambers for social networks with increased sensitivity to cognitive dissonance, whilst introversion produces high levels of fragmentation and low opinion mobility. Additionally, the effect of tolerant agents and inquisitive social encounters is investigated. It is found that both the presence of very small numbers of tolerant agents and inquisitive encounters are able to strongly promote consensus formation.
{"title":"Cognitive dissonance and introversion effects on opinion dynamics and echo chamber formation","authors":"","doi":"10.1016/j.physa.2024.130130","DOIUrl":"10.1016/j.physa.2024.130130","url":null,"abstract":"<div><div>Bounded confidence models (BCM) are extensively used to model continuous opinion dynamics in social networks. Typically, these models are analysed on static networks where edges do not vary over time. Following in the footsteps of adaptive voter models, further research has considered BCMs in the setting where agents are able to dynamically adjust their edges, which subsequently feedback into the opinion dynamics of the network. Several methods of updating connections have been proposed ranging from random rewiring to more sophisticated approaches based on concordant edges, homophily and cognitive dissonance. We present a modified form of the bounded confidence model, termed the selfish agent opinion (SAO) model, where connection updates are evaluated using a general cost function. Agents in the SAO model maintain two classes of relationships, friends and acquaintances, based on which they update their opinions and edges to optimise a payoff function that may include multiple social factors. This paper explores the effects, which we describe as “cognitive dissonance” and “introversion” that attempt to mimic postulated behaviours associated with the social concepts of the same name, on social network and opinion dynamics. We find that the SAO model naturally produces echo chambers for social networks with increased sensitivity to cognitive dissonance, whilst introversion produces high levels of fragmentation and low opinion mobility. Additionally, the effect of tolerant agents and inquisitive social encounters is investigated. It is found that both the presence of very small numbers of tolerant agents and inquisitive encounters are able to strongly promote consensus formation.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1016/j.physa.2024.130127
The coexistence of Connected Automated Vehicles (CAVs) and Human-driven Vehicles (HVs) in traffic scenarios is expected to persist in the long term. Hence, proposing effective mixed traffic flow control strategies is crucial for alleviating traffic congestion, reducing energy consumption, and enhancing traffic flow efficiency. However, existing reviews on car-following and lane-changing control encounter several issues: First, current studies are relatively disconnected, lacking a comprehensive framework to summarize both car-following and lane-changing models in heterogeneous environments; Second, existing reviews predominantly focus on individual vehicle-level models, rarely exploring coupling control strategies considering multi-vehicle interactions; Third, the explored driving scenarios in current review articles are relatively single or are not clearly distinguished. To fill this research gap, this paper offers a novel perspective on car-following and lane-changing models across three perspectives: i) HVs CF and LC behaviour simulation models; ii) CAVs CF and LC maneuver control models; and iii) The basic coupling architecture of CAV CF and LC maneuver control considering multi-vehicle interaction and its applications in some typical driving scenarios, including platoon scheduling, intersections, ramps, and CAV dedicated lanes. The objective of this paper is to provide valuable insights for future research on vehicle maneuver control in heterogeneous environments.
{"title":"A review of car-following and lane-changing models under heterogeneous environments","authors":"","doi":"10.1016/j.physa.2024.130127","DOIUrl":"10.1016/j.physa.2024.130127","url":null,"abstract":"<div><div>The coexistence of Connected Automated Vehicles (CAVs) and Human-driven Vehicles (HVs) in traffic scenarios is expected to persist in the long term. Hence, proposing effective mixed traffic flow control strategies is crucial for alleviating traffic congestion, reducing energy consumption, and enhancing traffic flow efficiency. However, existing reviews on car-following and lane-changing control encounter several issues: First, current studies are relatively disconnected, lacking a comprehensive framework to summarize both car-following and lane-changing models in heterogeneous environments; Second, existing reviews predominantly focus on individual vehicle-level models, rarely exploring coupling control strategies considering multi-vehicle interactions; Third, the explored driving scenarios in current review articles are relatively single or are not clearly distinguished. To fill this research gap, this paper offers a novel perspective on car-following and lane-changing models across three perspectives: i) HVs CF and LC behaviour simulation models; ii) CAVs CF and LC maneuver control models; and iii) The basic coupling architecture of CAV CF and LC maneuver control considering multi-vehicle interaction and its applications in some typical driving scenarios, including platoon scheduling, intersections, ramps, and CAV dedicated lanes. The objective of this paper is to provide valuable insights for future research on vehicle maneuver control in heterogeneous environments.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.physa.2024.130118
A new simplicial epidemic model that considers the pressure of out-going is proposed to describe the characteristics of clustering on disease transmission more accurately. In addition, the probability evolution equations of nodes in each state are obtained by the quenched mean-field method. Furthermore, we analyze the conditions of the existence and the stability of the equilibrium points. Subsequently, the sensitivity analysis of the parameters is investigated, and it can be concluded that the degree about pairwise transmission rate has great impact on the propagation threshold. Our simulation results indicate that the system produces forward bifurcation or backward bifurcation via the one-parameter bifurcation diagram, and the bistable state of the system appears under certain conditions. Meanwhile, we obtain the transition conditions of the system from the disease-free equilibrium state to the bistable state through the divisional diagram. It is also noticed that the pressure of out-going plays a crucial role in the spreading process of diseases. On the one hand, the increasing of the pressure of out-going leads to the decreasing of the disease transmission threshold and a faster outbreak of disease. On the other hand, an increase in the individuals without the pressure of out-going causes the increasing of transmission threshold and a slower outbreak of disease.
{"title":"A simplicial SSIS epidemic model with the outgoing pressure","authors":"","doi":"10.1016/j.physa.2024.130118","DOIUrl":"10.1016/j.physa.2024.130118","url":null,"abstract":"<div><div>A new simplicial epidemic model that considers the pressure of out-going is proposed to describe the characteristics of clustering on disease transmission more accurately. In addition, the probability evolution equations of nodes in each state are obtained by the quenched mean-field method. Furthermore, we analyze the conditions of the existence and the stability of the equilibrium points. Subsequently, the sensitivity analysis of the parameters is investigated, and it can be concluded that the degree about pairwise transmission rate has great impact on the propagation threshold. Our simulation results indicate that the system produces forward bifurcation or backward bifurcation via the one-parameter bifurcation diagram, and the bistable state of the system appears under certain conditions. Meanwhile, we obtain the transition conditions of the system from the disease-free equilibrium state to the bistable state through the divisional diagram. It is also noticed that the pressure of out-going plays a crucial role in the spreading process of diseases. On the one hand, the increasing of the pressure of out-going leads to the decreasing of the disease transmission threshold and a faster outbreak of disease. On the other hand, an increase in the individuals without the pressure of out-going causes the increasing of transmission threshold and a slower outbreak of disease.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.physa.2024.130117
Traffic oscillations, often induced by repetitive acceleration and deceleration maneuvers in vehicles’ car-following behaviors, can cause many negative impacts on the traffic flow. With the development of connected and automated vehicle (CAV) technologies recently, scholars have made numerous effects on mitigating the propagation of traffic oscillations through a variety of advanced CAV control strategies, especially those related to the CAV platoon control. Different from the previous works, this paper proposed a platoon-based adaptive cruise control (PACC) strategy for CAV platoon to mitigate the traffic oscillations. The strategy is designed based on the novel and unique leader-following (LF) information topology. Built on the classical proportional-derivative (PD) controller that is implemented in the adaptive cruise control (ACC) strategy of autonomous vehicles (AVs), the PACC strategy is exquisitely designed to ensure the string stability of entire CAV platoon and the critically damped condition of each following CAV in the platoon. Credit to the rapid response of following CAV to the vibration of leading CAV’s dynamic status under LF information topology and the thorough consideration of string stability and damping characteristics in the PD controller design, the PACC strategy enables the CAV platoon to mitigate the traffic oscillations more efficiently than the existing cooperative adaptive cruise control (CACC) and ACC strategies. The numerical experiment for the mixed traffic flow on a single-lane ring road indicates that, when the CAV platoon adopts the PACC strategy, the performance of traffic flow in terms of operational efficiency, driving safety, passenger’s comfort, and fuel economy is substantially enhanced, compared with CACC and ACC strategies. In addition, the performance of PACC strategy gradually improves with the increase of market penetration rate (MPR) of CAVs and length of CAV platoon. Overall, the proposed PACC strategy is a promising solution to the mitigation of traffic oscillation under the CAV environment.
{"title":"PACC: A platoon-based adaptive cruise control strategy based on leader-following information topology to mitigate traffic oscillations under CAV environment","authors":"","doi":"10.1016/j.physa.2024.130117","DOIUrl":"10.1016/j.physa.2024.130117","url":null,"abstract":"<div><div>Traffic oscillations, often induced by repetitive acceleration and deceleration maneuvers in vehicles’ car-following behaviors, can cause many negative impacts on the traffic flow. With the development of connected and automated vehicle (CAV) technologies recently, scholars have made numerous effects on mitigating the propagation of traffic oscillations through a variety of advanced CAV control strategies, especially those related to the CAV platoon control. Different from the previous works, this paper proposed a platoon-based adaptive cruise control (PACC) strategy for CAV platoon to mitigate the traffic oscillations. The strategy is designed based on the novel and unique leader-following (LF) information topology. Built on the classical proportional-derivative (PD) controller that is implemented in the adaptive cruise control (ACC) strategy of autonomous vehicles (AVs), the PACC strategy is exquisitely designed to ensure the string stability of entire CAV platoon and the critically damped condition of each following CAV in the platoon. Credit to the rapid response of following CAV to the vibration of leading CAV’s dynamic status under LF information topology and the thorough consideration of string stability and damping characteristics in the PD controller design, the PACC strategy enables the CAV platoon to mitigate the traffic oscillations more efficiently than the existing cooperative adaptive cruise control (CACC) and ACC strategies. The numerical experiment for the mixed traffic flow on a single-lane ring road indicates that, when the CAV platoon adopts the PACC strategy, the performance of traffic flow in terms of operational efficiency, driving safety, passenger’s comfort, and fuel economy is substantially enhanced, compared with CACC and ACC strategies. In addition, the performance of PACC strategy gradually improves with the increase of market penetration rate (MPR) of CAVs and length of CAV platoon. Overall, the proposed PACC strategy is a promising solution to the mitigation of traffic oscillation under the CAV environment.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.physa.2024.130126
Most real-world network evolution mechanisms not only have a preference attachment mechanism, but also exhibit high clustering characteristics. The existing information dissemination hypernetwork models are based on scale-free hypernetworks, and in this paper, we extend the scale-free hypernetwork evolution model by adding an adjustable high clustering and growth mechanism based on preference attachment, and propose a growing scale-free hypernetwork with tunable clustering. Thus hypernetwork models extend the traditional models and are more realistic. An information propagation model of SIS in hypernetworks based on reaction process strategy is constructed, and the dynamic process of information propagation under different network structure parameters is theoretically analyzed and numerically simulated. The results show that the propagation capacity of information increase with the growth rate, but suppressed with the increase of clustering coefficient. Additionally, we have discovered an important phenomenon: when the growth rate reaches 0.4 and increases further, the density of information nodes reaches saturation in the steady state. The proposed hypernetwork model is more suitable for real social networks and can provide some theoretical references for public opinion prediction and information control.
{"title":"Information dissemination in growing scale-free hypernetworks with tunable clustering","authors":"","doi":"10.1016/j.physa.2024.130126","DOIUrl":"10.1016/j.physa.2024.130126","url":null,"abstract":"<div><div>Most real-world network evolution mechanisms not only have a preference attachment mechanism, but also exhibit high clustering characteristics. The existing information dissemination hypernetwork models are based on scale-free hypernetworks, and in this paper, we extend the scale-free hypernetwork evolution model by adding an adjustable high clustering and growth mechanism based on preference attachment, and propose a growing scale-free hypernetwork with tunable clustering. Thus hypernetwork models extend the traditional models and are more realistic. An information propagation model of SIS in hypernetworks based on reaction process strategy is constructed, and the dynamic process of information propagation under different network structure parameters is theoretically analyzed and numerically simulated. The results show that the propagation capacity of information increase with the growth rate, but suppressed with the increase of clustering coefficient. Additionally, we have discovered an important phenomenon: when the growth rate reaches 0.4 and increases further, the density of information nodes reaches saturation in the steady state. The proposed hypernetwork model is more suitable for real social networks and can provide some theoretical references for public opinion prediction and information control.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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