Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination syst...Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.展开更多
Behavioral decision-making at urban intersections is one of the primary difficulties currently impeding the development of intelligent vehicle technology.The problem is that existing decision-making algorithms cannot ...Behavioral decision-making at urban intersections is one of the primary difficulties currently impeding the development of intelligent vehicle technology.The problem is that existing decision-making algorithms cannot effectively deal with complex random scenarios at urban intersections.To deal with this,a deep deterministic policy gradient(DDPG)decision-making algorithm(T-DDPG)based on a time-series Markov decision process(T-MDP)was developed,where the state was extended to collect observations from several consecutive frames.Experiments found that T-DDPG performed better in terms of convergence and generalizability in complex intersection scenarios than a traditional DDPG algorithm.Furthermore,model-agnostic meta-learning(MAML)was incorporated into the T-DDPG algorithm to improve the training method,leading to a decision algorithm(T-MAML-DDPG)based on a secondary gradient.Simulation experiments of intersection scenarios were carried out on the Gym-Carla platform to verify and compare the decision models.The results showed that T-MAML-DDPG was able to easily deal with the random states of complex intersection scenarios,which could improve traffic safety and efficiency.The above decision-making models based on meta-reinforcement learning are significant for enhancing the decision-making ability of intelligent vehicles at urban intersections.展开更多
With the rapid growth of urbanization and the increasing demand for transportation, urban traffic congestion has become a hindrance to individuals’ travel experience. Urban intersections are one of the primary source...With the rapid growth of urbanization and the increasing demand for transportation, urban traffic congestion has become a hindrance to individuals’ travel experience. Urban intersections are one of the primary sources of traffic congestion, and these bottlenecks have a negative impact not only on traffic efficacy but also on the surrounding road traffic in the region. To alleviate urban traffic congestion, cyber-physical systems have been widely implemented in the transportation industry, allowing for the perception, analysis, calculation, and dispatching of urban traffic flow, as well as making urban transportation safe, efficient, and quick. As the system scale and functions increase, system design has become increasingly complex, necessitating a deeper comprehension of the system’s structure and interaction relationships to construct a stable and reliable system. Therefore, this study proposes a method for designing cyber-physical systems for urban traffic intersections based on Model-Based Systems Engineering (MBSE). This method models and analyses exhaustively the system’s requirements, functions, and logical architecture using System Modeling Language (SysML). After the architecture design has been completed, an architecture verification and optimization method based on Failure Mode and Effect Analysis (FMEA) for urban road intersection cyber-physical systems is utilized to analyze the architecture’s reliability by analyzing the failure modes of activities and to optimize the system architecture to improve the design’s efficiency and reliability.展开更多
Intersections appear where one street crosses another,acting as fundamental nodes embedded in the network of urban public space.Though discussed in configurationa and perceptive studies,limited attention has been paid...Intersections appear where one street crosses another,acting as fundamental nodes embedded in the network of urban public space.Though discussed in configurationa and perceptive studies,limited attention has been paid to the morphological aspects of inter.sections.This study proposes and tested a new approach to modeling the urban intersection form.First,effective cylinder was introduced to define the related space for each intersection based on its scale.Second,a set of morphological indicators were presented to measure the physical properties of the intersection form.Third,the affinity propagation algorithm was used to examine the patterns of intersection form.Using the Old City of Nanjing,China as the study area,844 intersections were analyzed to test this new method.As a result,we were able to classify the intersections into eight types.This study shows that the intersection can be modelled as a volumetric and integral spatial unit of urban form,which may demand more attention from urban designers and architects in the future for shaping the built environment.The quantitative nature of our method could also open more possibilities for intersection-based studies.展开更多
This paper presents a fuzzy logic adaptive traffic signal control method for an isolated four-approach intersection with through and left-turning movements. In the proposed method, the fuzzy logic controller can make...This paper presents a fuzzy logic adaptive traffic signal control method for an isolated four-approach intersection with through and left-turning movements. In the proposed method, the fuzzy logic controller can make adjustments to signal timing in response to observed changes. The 'urgency degree' term that can describe different user's demands for a green light is used in the fuzzy logic decision-making. In addition, a three-level fuzzy controller model decides whether to extend or terminate the current signal phase and the sequence of phases. Simulation results show that the fuzzy controller can adjust its signal timing in response to changing traffic conditions on a real-time basis and that the proposed fuzzy logic controller leads to less vehicle delays and a lower percentage of stopped vehicles.展开更多
基金supported by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(C)23K03898.
文摘Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.
基金supported in part by the Beijing Municipal Science and Technology Project(No.Z191100007419010)Automobile Industry Joint Fund(No.U1764261)of the National Natural Science Foundation of China+1 种基金Shandong Key R&D Program(No.2020CXGC010118)Key Laboratory for New Technology Application of Road Conveyance of Jiangsu Province(No.BM20082061706)。
文摘Behavioral decision-making at urban intersections is one of the primary difficulties currently impeding the development of intelligent vehicle technology.The problem is that existing decision-making algorithms cannot effectively deal with complex random scenarios at urban intersections.To deal with this,a deep deterministic policy gradient(DDPG)decision-making algorithm(T-DDPG)based on a time-series Markov decision process(T-MDP)was developed,where the state was extended to collect observations from several consecutive frames.Experiments found that T-DDPG performed better in terms of convergence and generalizability in complex intersection scenarios than a traditional DDPG algorithm.Furthermore,model-agnostic meta-learning(MAML)was incorporated into the T-DDPG algorithm to improve the training method,leading to a decision algorithm(T-MAML-DDPG)based on a secondary gradient.Simulation experiments of intersection scenarios were carried out on the Gym-Carla platform to verify and compare the decision models.The results showed that T-MAML-DDPG was able to easily deal with the random states of complex intersection scenarios,which could improve traffic safety and efficiency.The above decision-making models based on meta-reinforcement learning are significant for enhancing the decision-making ability of intelligent vehicles at urban intersections.
基金supported by the National Key R&D Program of China(2021YFB2501000).
文摘With the rapid growth of urbanization and the increasing demand for transportation, urban traffic congestion has become a hindrance to individuals’ travel experience. Urban intersections are one of the primary sources of traffic congestion, and these bottlenecks have a negative impact not only on traffic efficacy but also on the surrounding road traffic in the region. To alleviate urban traffic congestion, cyber-physical systems have been widely implemented in the transportation industry, allowing for the perception, analysis, calculation, and dispatching of urban traffic flow, as well as making urban transportation safe, efficient, and quick. As the system scale and functions increase, system design has become increasingly complex, necessitating a deeper comprehension of the system’s structure and interaction relationships to construct a stable and reliable system. Therefore, this study proposes a method for designing cyber-physical systems for urban traffic intersections based on Model-Based Systems Engineering (MBSE). This method models and analyses exhaustively the system’s requirements, functions, and logical architecture using System Modeling Language (SysML). After the architecture design has been completed, an architecture verification and optimization method based on Failure Mode and Effect Analysis (FMEA) for urban road intersection cyber-physical systems is utilized to analyze the architecture’s reliability by analyzing the failure modes of activities and to optimize the system architecture to improve the design’s efficiency and reliability.
基金This work was supported by the Key Project of National Natural Science Foundation of China(grand number 51838002)the program of China Scholarship Council(grand number 201906090005).
文摘Intersections appear where one street crosses another,acting as fundamental nodes embedded in the network of urban public space.Though discussed in configurationa and perceptive studies,limited attention has been paid to the morphological aspects of inter.sections.This study proposes and tested a new approach to modeling the urban intersection form.First,effective cylinder was introduced to define the related space for each intersection based on its scale.Second,a set of morphological indicators were presented to measure the physical properties of the intersection form.Third,the affinity propagation algorithm was used to examine the patterns of intersection form.Using the Old City of Nanjing,China as the study area,844 intersections were analyzed to test this new method.As a result,we were able to classify the intersections into eight types.This study shows that the intersection can be modelled as a volumetric and integral spatial unit of urban form,which may demand more attention from urban designers and architects in the future for shaping the built environment.The quantitative nature of our method could also open more possibilities for intersection-based studies.
基金Supported by the Major Research Project of theDepartm ent of Communication of China and ChinaPostdoctoral Science Foundation
文摘This paper presents a fuzzy logic adaptive traffic signal control method for an isolated four-approach intersection with through and left-turning movements. In the proposed method, the fuzzy logic controller can make adjustments to signal timing in response to observed changes. The 'urgency degree' term that can describe different user's demands for a green light is used in the fuzzy logic decision-making. In addition, a three-level fuzzy controller model decides whether to extend or terminate the current signal phase and the sequence of phases. Simulation results show that the fuzzy controller can adjust its signal timing in response to changing traffic conditions on a real-time basis and that the proposed fuzzy logic controller leads to less vehicle delays and a lower percentage of stopped vehicles.
