Pub Date : 2025-01-15DOI: 10.1016/j.physa.2024.130286
Takemi Nakamura
We introduce the Hessian of the negative ground-state energy as a Riemannian metric on the parametric family of the ground states of a parameterized Hamiltonian of a quantum system and study the critical behavior of the scalar curvature of this new metric. Taking the anisotropic XY chain in a transverse field as an example, we study the critical behaviors of the scalar curvature associated with the quantum phase transitions both numerically and analytically. The behaviors are found to be different from those of the Fubini–Study metric but in agreement with the scalar curvature for the Bogoliubov–Kubo–Mori metric in thermodynamic geometry from the perspective of the universality class. We also briefly discuss the Legendre structure concerning this Hessian metric.
{"title":"Quantum information geometry by the ground-state energy and the criticality of the scalar curvature","authors":"Takemi Nakamura","doi":"10.1016/j.physa.2024.130286","DOIUrl":"10.1016/j.physa.2024.130286","url":null,"abstract":"<div><div>We introduce the Hessian of the negative ground-state energy as a Riemannian metric on the parametric family of the ground states of a parameterized Hamiltonian of a quantum system and study the critical behavior of the scalar curvature of this new metric. Taking the anisotropic XY chain in a transverse field as an example, we study the critical behaviors of the scalar curvature associated with the quantum phase transitions both numerically and analytically. The behaviors are found to be different from those of the Fubini–Study metric but in agreement with the scalar curvature for the Bogoliubov–Kubo–Mori metric in thermodynamic geometry from the perspective of the universality class. We also briefly discuss the Legendre structure concerning this Hessian metric.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"658 ","pages":"Article 130286"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-15DOI: 10.1016/j.physa.2024.130288
Domenico Marino
Corruption silently distorts markets and diverts resources from the public good. This paper explores the cyclical nature of corruption, analyzing corruption from a microeconomic perspective and identifying a relationship between the intensity of state repressive action and the level of corruption. This research offers new insights into the cyclical behavior of corruption, addressing issues relevant to economic policy. An important aspect for understanding corruption dynamics lies in its cyclical behavior. The concept of the corruption cycle has been sufficiently explored at the theoretical level, but empirical evidence remains limited. This paper attempts to fill this gap by constructing a robust theoretical model that elucidates the interaction between sanctions and bribes and between the level of corruption and state intervention as a cause of corruption cyclicality and validating the theoretical findings through empirical analysis using spectral analysis and data mining techniques. The empirical verification of the theoretical hypothesis of cyclicality of corruption opens up interesting scenarios for developing anti-corruption policies.
{"title":"Dynamics of corruption: Theoretical explanatory model and empirical results","authors":"Domenico Marino","doi":"10.1016/j.physa.2024.130288","DOIUrl":"10.1016/j.physa.2024.130288","url":null,"abstract":"<div><div>Corruption silently distorts markets and diverts resources from the public good. This paper explores the cyclical nature of corruption, analyzing corruption from a microeconomic perspective and identifying a relationship between the intensity of state repressive action and the level of corruption. This research offers new insights into the cyclical behavior of corruption, addressing issues relevant to economic policy. An important aspect for understanding corruption dynamics lies in its cyclical behavior. The concept of the corruption cycle has been sufficiently explored at the theoretical level, but empirical evidence remains limited. This paper attempts to fill this gap by constructing a robust theoretical model that elucidates the interaction between sanctions and bribes and between the level of corruption and state intervention as a cause of corruption cyclicality and validating the theoretical findings through empirical analysis using spectral analysis and data mining techniques. The empirical verification of the theoretical hypothesis of cyclicality of corruption opens up interesting scenarios for developing anti-corruption policies.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"658 ","pages":"Article 130288"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-15DOI: 10.1016/j.physa.2024.130256
Shima Esfandiari, Seyed Mostafa Fakhrahmad
Finding the most influential nodes in complex networks is a significant challenge with applications in various fields, including social networks, biology, and transportation systems. Many existing methods rely on different structural properties but often overlook complementary features. This paper highlights the complementary nature of K-Shell and PageRank and proposes a novel linear metric that combines them. Through extensive comparisons of 19 real-world and several artificial networks, the proposed method demonstrates superior accuracy, resolution, and computational efficiency. Evaluations against 11 state-of-the-art methods, including IDME, HGSM, and DNC, underscore the superiority of the proposed approach. Notably, the average accuracy has increased by 33.3% compared to PageRank and 23.1% compared to K-Shell, emphasizing the importance of integrating these two features.
{"title":"The collaborative role of K-Shell and PageRank for identifying influential nodes in complex networks","authors":"Shima Esfandiari, Seyed Mostafa Fakhrahmad","doi":"10.1016/j.physa.2024.130256","DOIUrl":"10.1016/j.physa.2024.130256","url":null,"abstract":"<div><div>Finding the most influential nodes in complex networks is a significant challenge with applications in various fields, including social networks, biology, and transportation systems. Many existing methods rely on different structural properties but often overlook complementary features. This paper highlights the complementary nature of K-Shell and PageRank and proposes a novel linear metric that combines them. Through extensive comparisons of 19 real-world and several artificial networks, the proposed method demonstrates superior accuracy, resolution, and computational efficiency. Evaluations against 11 state-of-the-art methods, including IDME, HGSM, and DNC, underscore the superiority of the proposed approach. Notably, the average accuracy has increased by 33.3% compared to PageRank and 23.1% compared to K-Shell, emphasizing the importance of integrating these two features.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"658 ","pages":"Article 130256"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095432","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 : 2025-01-15DOI: 10.1016/j.physa.2024.130307
Jose Alvarez-Ramirez, Gilberto Espinosa-Paredes, E. Jaime Vernon-Carter
This study employed wavelet analysis to investigate Bitcoin price dynamics from 2014 to 2024. Unlike existing research, which relies on bidirectional wavelet functions, our approach utilized causal wavelet analysis. This method ensures that wavelet basis functions only account for past values, reflecting the impact of past prices on present prices while maintaining causality. The complex Morlet wavelet revealed that market complexity varies over time and scale. Our results showed that regions of high wavelet power coincide with bearish market phases leading to historical price maxima. The phase scalogram indicated that price return dynamics are primarily dominated by even components, reflecting fluctuation patterns across a wide range of oscillation frequencies. In a secondary analysis, we modified the wavelet analysis by decoupling the oscillation scale and the modulation (memory) function scale. This allowed us to estimate the decaying memory characteristic time scale. The resulting scalograms exhibited sharper magnitude and phase patterns, suggesting that Bitcoin price return dynamics are influenced by long-run memory. Our findings conclude that incorporating causality and long-run memory into wavelet analysis provides a more accurate characterization of cryptocurrency price dynamics.
{"title":"Causal wavelet analysis of the Bitcoin price dynamics","authors":"Jose Alvarez-Ramirez, Gilberto Espinosa-Paredes, E. Jaime Vernon-Carter","doi":"10.1016/j.physa.2024.130307","DOIUrl":"10.1016/j.physa.2024.130307","url":null,"abstract":"<div><div>This study employed wavelet analysis to investigate Bitcoin price dynamics from 2014 to 2024. Unlike existing research, which relies on bidirectional wavelet functions, our approach utilized causal wavelet analysis. This method ensures that wavelet basis functions only account for past values, reflecting the impact of past prices on present prices while maintaining causality. The complex Morlet wavelet revealed that market complexity varies over time and scale. Our results showed that regions of high wavelet power coincide with bearish market phases leading to historical price maxima. The phase scalogram indicated that price return dynamics are primarily dominated by even components, reflecting fluctuation patterns across a wide range of oscillation frequencies. In a secondary analysis, we modified the wavelet analysis by decoupling the oscillation scale and the modulation (memory) function scale. This allowed us to estimate the decaying memory characteristic time scale. The resulting scalograms exhibited sharper magnitude and phase patterns, suggesting that Bitcoin price return dynamics are influenced by long-run memory. Our findings conclude that incorporating causality and long-run memory into wavelet analysis provides a more accurate characterization of cryptocurrency price dynamics.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"658 ","pages":"Article 130307"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143104759","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 : 2025-01-15DOI: 10.1016/j.physa.2024.130306
Werner Kristjanpoller , Benjamin Miranda Tabak
This article analyzes the calendar anomaly of the day of the week effect in the cryptocurrency market. Financial markets thrive on open marketplaces, faster communication and more information, making them more efficient. Higher efficiency would result in the disappearance of these calendar anomalies. We contribute by employing a novel asymmetric multifractal analysis of high-frequency returns during trading hours. Our findings revealed significant differences in the multifractal behavior of the hourly returns of the five leading cryptocurrencies. In particular, we observe a higher degree of multifractality on Thursdays. The Hurst exponent also showed statistically different values for the days of the week. Four of the five cryptocurrencies showed persistent behavior on Fridays and anti-persistent behavior on Tuesdays and Saturdays. These patterns offer potential for traders and investors to exploit, underscoring the practical value of this research.
{"title":"Day of the week effect on the cryptomarket: A high-frequency asymmetric multifractal analysis","authors":"Werner Kristjanpoller , Benjamin Miranda Tabak","doi":"10.1016/j.physa.2024.130306","DOIUrl":"10.1016/j.physa.2024.130306","url":null,"abstract":"<div><div>This article analyzes the calendar anomaly of the day of the week effect in the cryptocurrency market. Financial markets thrive on open marketplaces, faster communication and more information, making them more efficient. Higher efficiency would result in the disappearance of these calendar anomalies. We contribute by employing a novel asymmetric multifractal analysis of high-frequency returns during trading hours. Our findings revealed significant differences in the multifractal behavior of the hourly returns of the five leading cryptocurrencies. In particular, we observe a higher degree of multifractality on Thursdays. The Hurst exponent also showed statistically different values for the days of the week. Four of the five cryptocurrencies showed persistent behavior on Fridays and anti-persistent behavior on Tuesdays and Saturdays. These patterns offer potential for traders and investors to exploit, underscoring the practical value of this research.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"658 ","pages":"Article 130306"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143104760","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}
We study the behavior of a single spin in the presence of a time-varying magnetic field utilizing Glauber dynamics. We engineer the system to function as an engine by changing the magnetic field according to specific protocols. Subsequently, we analyze the engine’s performance using various protocols and stochastic thermodynamics to compute average values of crucial quantities for quantifying engine performance. In the longtime limit of the engine cycle, we derive exact analytical expressions for work, heat, and efficiency in terms of a generalized protocol. We then analyze the model in terms of optimization of efficiency and power. Additionally, we use different protocols and employ a gradient descent algorithm to best fit those to obtain optimal efficiency and then optimal power for a finite cycle time. All the protocols converge to the piece-wise constant protocol during efficiency optimization. We then explore a more general approach using the variational principle to determine the optimal protocols for optimizing power and efficiency. During the optimization process for both power and efficiency, the net entropy production decreases, which enhances the engine’s performance. This approach demonstrates the superior optimization of efficiency and power in this system compared to the gradient descent algorithm.
{"title":"Optimizing power and efficiency of a single spin heat engine","authors":"Rita Majumdar , Monojit Chatterjee , Rahul Marathe","doi":"10.1016/j.physa.2024.130278","DOIUrl":"10.1016/j.physa.2024.130278","url":null,"abstract":"<div><div>We study the behavior of a single spin in the presence of a time-varying magnetic field utilizing Glauber dynamics. We engineer the system to function as an engine by changing the magnetic field according to specific protocols. Subsequently, we analyze the engine’s performance using various protocols and stochastic thermodynamics to compute average values of crucial quantities for quantifying engine performance. In the longtime limit of the engine cycle, we derive exact analytical expressions for work, heat, and efficiency in terms of a generalized protocol. We then analyze the model in terms of optimization of efficiency and power. Additionally, we use different protocols and employ a gradient descent algorithm to best fit those to obtain optimal efficiency and then optimal power for a finite cycle time. All the protocols converge to the piece-wise constant protocol during efficiency optimization. We then explore a more general approach using the variational principle to determine the optimal protocols for optimizing power and efficiency. During the optimization process for both power and efficiency, the net entropy production decreases, which enhances the engine’s performance. This approach demonstrates the superior optimization of efficiency and power in this system compared to the gradient descent algorithm.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"658 ","pages":"Article 130278"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143104762","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 : 2025-01-15DOI: 10.1016/j.physa.2024.130305
G.G. Piva , C. Anteneodo
We investigate a nonlocal generalization of the Fisher–KPP equation, which incorporates logistic growth and diffusion, for a single species population in a viable patch (refuge). In this framework, diffusion plays an homogenizing role, while nonlocal interactions can destabilize the spatially uniform state, leading to the emergence of spontaneous patterns. Notably, even when the uniform state is stable, spatial perturbations, such as the presence of a refuge, can still induce patterns. These phenomena are well known for environments with constant diffusivity. Our goal is to investigate how the formation of winkles in the population distribution is affected when the diffusivity is density-dependent. Then, we explore scenarios in which diffusivity is sensitive to either rarefaction or overcrowding. We find that state-dependent diffusivity affects the shape and stability of the patterns, potentially leading to either explosive growth or fragmentation of the population distribution, depending on how diffusion reacts to changes in density.
{"title":"Influence of density-dependent diffusion on pattern formation in a refuge","authors":"G.G. Piva , C. Anteneodo","doi":"10.1016/j.physa.2024.130305","DOIUrl":"10.1016/j.physa.2024.130305","url":null,"abstract":"<div><div>We investigate a nonlocal generalization of the Fisher–KPP equation, which incorporates logistic growth and diffusion, for a single species population in a viable patch (refuge). In this framework, diffusion plays an homogenizing role, while nonlocal interactions can destabilize the spatially uniform state, leading to the emergence of spontaneous patterns. Notably, even when the uniform state is stable, spatial perturbations, such as the presence of a refuge, can still induce patterns. These phenomena are well known for environments with constant diffusivity. Our goal is to investigate how the formation of winkles in the population distribution is affected when the diffusivity is density-dependent. Then, we explore scenarios in which diffusivity is sensitive to either rarefaction or overcrowding. We find that state-dependent diffusivity affects the shape and stability of the patterns, potentially leading to either explosive growth or fragmentation of the population distribution, depending on how diffusion reacts to changes in density.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"658 ","pages":"Article 130305"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095355","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 : 2025-01-15DOI: 10.1016/j.physa.2024.130234
Roberto Mota Navarro, Francois Leyvraz, Hernán Larralde
The study of order volumes in financial markets has shown that these display several non-trivial statistical properties. The majority of studies have focused on the sizes of incoming orders or of realized transactions, the present work is a study of dynamical aspects of volume available at the best ask and best bid. The interest in these volumes stems from their capacity to limit or otherwise affect possible trades in the near future Using limit order book data from the Bitcoin/USDT market we study, among other things, the behavior of the distribution of volume changes as a function of the time scale at which the changes are measured, the autocorrelations of volume changes at each side of the book and the autocorrelations of volume imbalances between asks and bids. We find that several of these properties can be well approximated by power laws.
{"title":"Empirical properties of volume dynamics in the limit order book","authors":"Roberto Mota Navarro, Francois Leyvraz, Hernán Larralde","doi":"10.1016/j.physa.2024.130234","DOIUrl":"10.1016/j.physa.2024.130234","url":null,"abstract":"<div><div>The study of order volumes in financial markets has shown that these display several non-trivial statistical properties. The majority of studies have focused on the sizes of incoming orders or of realized transactions, the present work is a study of dynamical aspects of volume available at the best ask and best bid. The interest in these volumes stems from their capacity to limit or otherwise affect possible trades in the near future Using limit order book data from the Bitcoin/USDT market we study, among other things, the behavior of the distribution of volume changes as a function of the time scale at which the changes are measured, the autocorrelations of volume changes at each side of the book and the autocorrelations of volume imbalances between asks and bids. We find that several of these properties can be well approximated by power laws.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"658 ","pages":"Article 130234"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095964","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 : 2025-01-15DOI: 10.1016/j.physa.2024.130267
Sergei Sidorov , Sergei Mironov , Timofei D. Emelianov
One of the most well-known mechanisms contributing to the emergence of networks with a power-law degree distribution is preferential attachment. In this study, we examined a family of network evolution models based on the merging of two arbitrary vertices, which is shown to also lead to the creation of power-law distributed networks. These models simultaneously apply rules for both node addition and merging, which reflects that many real systems exhibit the processes of growth and shrink. At each iteration, when two vertices merge, the neighbors of one of the vertices become neighbors of the other, and the vertex itself is removed from the network. In addition, at each iteration, a new vertex appears that is attached to randomly selected nodes. As an enhancement, we incorporate a triadic closure mechanism into the evolution to increase the clustering coefficient, a key characteristic of real social networks. We show that in the process of evolution any initial network converges to a stationary state with a power law degree distribution, while the number of edges, the average degree, and the average clustering coefficient saturate to a certain limit values depending on the model parameters.
{"title":"Generating complex networks through a vertex merging mechanism: Empirical and analytical analysis","authors":"Sergei Sidorov , Sergei Mironov , Timofei D. Emelianov","doi":"10.1016/j.physa.2024.130267","DOIUrl":"10.1016/j.physa.2024.130267","url":null,"abstract":"<div><div>One of the most well-known mechanisms contributing to the emergence of networks with a power-law degree distribution is preferential attachment. In this study, we examined a family of network evolution models based on the merging of two arbitrary vertices, which is shown to also lead to the creation of power-law distributed networks. These models simultaneously apply rules for both node addition and merging, which reflects that many real systems exhibit the processes of growth and shrink. At each iteration, when two vertices merge, the neighbors of one of the vertices become neighbors of the other, and the vertex itself is removed from the network. In addition, at each iteration, a new vertex appears that is attached to randomly selected nodes. As an enhancement, we incorporate a triadic closure mechanism into the evolution to increase the clustering coefficient, a key characteristic of real social networks. We show that in the process of evolution any initial network converges to a stationary state with a power law degree distribution, while the number of edges, the average degree, and the average clustering coefficient saturate to a certain limit values depending on the model parameters.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"658 ","pages":"Article 130267"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135720","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}
Related studies on traffic control in partially connected environments either did not consider the collaboration of traffic signal control and vehicular control, or did not consider others’ responsive actions before decision-making in coupled vehicle-signal control. Thus, we propose a Stackelberg Game Enabled Multi-agent Reinforcement Learning (SGMRL) method for coupled vehicle-signal control at an intersection with mixed traffic flow of Connected and Automated Vehicles (CAVs)/Human Driven Vehicles (HDVs). A two-stage framework is applied in SGMRL to learn optimal signal control strategy and CAV platoon strategies in mixed flows of all entrance roads at an intersection. Stackelberg game theory is introduced in SGMRL to make an asynchronous decision-making mechanism. The signal controller is a leader that allocates green times to different phases based on predictions of vehicles’ responsive actions, and CAVs in different directions are followers that form platoons and adjust speeds to adapt to the signal lights decided by the leader. Moreover, CAV platoons in different directions are regarded as agents and form a multi-agent learning framework with the signal controller. Then, an improved Dueling Double Deep Q Network (ID3QN) algorithm is investigated to calculate the Stackelberg equilibrium for the control problem. Experimental results demonstrate that the proposed model effectively reduces the overall waiting time and queue length of all vehicles, in the mixed traffic environment with different CAV penetration rates.
{"title":"Coupled vehicle-signal control based on Stackelberg Game Enabled Multi-agent Reinforcement Learning in mixed traffic environment","authors":"Xinshao Zhang , Zhaocheng He , Yiting Zhu , Wei Huang","doi":"10.1016/j.physa.2024.130289","DOIUrl":"10.1016/j.physa.2024.130289","url":null,"abstract":"<div><div>Related studies on traffic control in partially connected environments either did not consider the collaboration of traffic signal control and vehicular control, or did not consider others’ responsive actions before decision-making in coupled vehicle-signal control. Thus, we propose a Stackelberg Game Enabled Multi-agent Reinforcement Learning (SGMRL) method for coupled vehicle-signal control at an intersection with mixed traffic flow of Connected and Automated Vehicles (CAVs)/Human Driven Vehicles (HDVs). A two-stage framework is applied in SGMRL to learn optimal signal control strategy and CAV platoon strategies in mixed flows of all entrance roads at an intersection. Stackelberg game theory is introduced in SGMRL to make an asynchronous decision-making mechanism. The signal controller is a leader that allocates green times to different phases based on predictions of vehicles’ responsive actions, and CAVs in different directions are followers that form platoons and adjust speeds to adapt to the signal lights decided by the leader. Moreover, CAV platoons in different directions are regarded as agents and form a multi-agent learning framework with the signal controller. Then, an improved Dueling Double Deep Q Network (ID3QN) algorithm is investigated to calculate the Stackelberg equilibrium for the control problem. Experimental results demonstrate that the proposed model effectively reduces the overall waiting time and queue length of all vehicles, in the mixed traffic environment with different CAV penetration rates.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"658 ","pages":"Article 130289"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095956","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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