Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a ma...Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related data.For the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS applications.At the same time,prediction of short-term traffic flow is highly essential to manage the traffic accurately.Due to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow forecasting.This paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so on.The presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow prediction.The IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count process.In addition,deep belief network(DBN)model is applied for the prediction of short-term traffic flow.To further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of DBN.Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.展开更多
In this paper, the roles of infrastructure development and transportation coordination for Northeast Asian economic cooperation are discussed. It would be necessary to establish an efficient transportation network as ...In this paper, the roles of infrastructure development and transportation coordination for Northeast Asian economic cooperation are discussed. It would be necessary to establish an efficient transportation network as soon as possible. 'Hub-and-Spoke'transportation system and China-Korean peninsula railway container transportation system might be more significant for regional economic cooperation.展开更多
Cooperation among enterprises can bring overall and individual performance improvement,and a smooth coordination method is indispensable.However,due to the lack of customized coordination methods,cooperation in the do...Cooperation among enterprises can bring overall and individual performance improvement,and a smooth coordination method is indispensable.However,due to the lack of customized coordination methods,cooperation in the downstream oil supply chain cannot be carried out smoothly.This paper intends to propose a multi-party coordination method to promote cooperation between oil shippers and pipeline operator by optimizing oil transportation,oil substitution and pipeline pricing schemes.An integrated game-theoretic modeling and analysis approach is developed to characterize the operation behaviors of all stakeholders in the downstream oil supply chain.The proposed mixed integer nonlinear programming model constrains supply and demand capacity,transportation routes,oil substitution rules and pipeline freight levels.Logarithm transformation and price discretization are introduced for model linear approximation.Simulation experiments are carried out in the oil distribution system in South China.The results show that compared to the business-as-usual scheme,the new scheme saves transportation cost by 3.48%,increases pipeline turnover by 5.7%,and reduces energy consumption and emissions by 7.66%and 6.77%.It is proved that the proposed method improves the revenue of the whole system,achieves fair revenue distribution,and also improves the energy and environmental benefits of the oil supply chain.展开更多
针对情景记忆算法中记忆池中的样本利用率低的问题,提出了一种基于情景记忆和值函数分解框架相结合的合作型多智能体强化学习算法,即情景记忆值分解(episodic memory value decomposition,EMVD)算法。EMVD算法在情景记忆部分以时间差分...针对情景记忆算法中记忆池中的样本利用率低的问题,提出了一种基于情景记忆和值函数分解框架相结合的合作型多智能体强化学习算法,即情景记忆值分解(episodic memory value decomposition,EMVD)算法。EMVD算法在情景记忆部分以时间差分误差平方为依据来更新记忆池,使记忆池中一直保留对学习效果提升更重要的情景记忆样本,并将情景记忆算法与神经网络相结合,提高了算法的收敛速度。为了将EMVD算法应用于机器人协作运输任务中,设定机器人和运输目标的位置为状态,并且设计了回报函数。仿真结果表明,EMVD算法可以探索出机器人协作运输任务的最优策略,提高了算法的收敛速度。展开更多
Given limited terrain adaptability,most existing multirobot cooperative transportation systems(MRCTSs)mainly work on flat pavements,restricting their outdoor applications.The connectors'finite deformation capabili...Given limited terrain adaptability,most existing multirobot cooperative transportation systems(MRCTSs)mainly work on flat pavements,restricting their outdoor applications.The connectors'finite deformation capability and the control strategies'limitations are primarily responsible for this phenomenon.This study proposes a novel MRCTS based on tracked mobile robots(TMRs)to improve terrain adaptability and expand the application scenarios of MRCTSs.In structure design,we develop a novel 6-degree-of-freedom passive adaptive connector to link multiple TMRs and the transported object(the communal payload).In addition,the connector is set with sensors to measure the position and orientation of the robot with respect to the object for feedback control.In the control strategy,we present a virtual leader-physical follower collaborative paradigm.The leader robot is imaginary to describe the movement of the entire system and manage the follower robots.All the TMRs in the system act as follower robots to transport the object cooperatively.Having divided the whole control structure into the leader robot level and the follower robot level,we convert the motion control of the two kinds of robots to trajectory tracking control problems and propose a novel double closed-loop kinematics control framework.Furthermore,a control law satisfying saturation constraints is derived to ensure transportation stability.An adaptive control algorithm processes the wheelbase uncertainty of the TMR.Finally,we develop a prototype of the TMR-based MRCTS for experiments.In the trajectory tracking experiment,the developed MRCTS with the proposed control scheme can converge to the reference trajectory in the presence of initial tracking errors in a finite time.In the outdoor experiment,the proposed MRCTS consisting of four TMRs can successfully transport a payload weighing 60 kg on an uneven road with the single TMR's maximum load limited to 15 kg.The experimental results demonstrate the effectiveness of the structural design and control strategies of the TMR-based MRCTS.展开更多
文摘Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related data.For the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS applications.At the same time,prediction of short-term traffic flow is highly essential to manage the traffic accurately.Due to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow forecasting.This paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so on.The presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow prediction.The IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count process.In addition,deep belief network(DBN)model is applied for the prediction of short-term traffic flow.To further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of DBN.Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.
基金Under the auspices of the National Natural Science Foundation of China (No. 49871026) key Directional Project of Knowledge Inno
文摘In this paper, the roles of infrastructure development and transportation coordination for Northeast Asian economic cooperation are discussed. It would be necessary to establish an efficient transportation network as soon as possible. 'Hub-and-Spoke'transportation system and China-Korean peninsula railway container transportation system might be more significant for regional economic cooperation.
基金partially supported by the Science Foundation of China University of Petroleum,Beijing(2462023XKBH013)the National Natural Science Foundation of China(52202405)。
文摘Cooperation among enterprises can bring overall and individual performance improvement,and a smooth coordination method is indispensable.However,due to the lack of customized coordination methods,cooperation in the downstream oil supply chain cannot be carried out smoothly.This paper intends to propose a multi-party coordination method to promote cooperation between oil shippers and pipeline operator by optimizing oil transportation,oil substitution and pipeline pricing schemes.An integrated game-theoretic modeling and analysis approach is developed to characterize the operation behaviors of all stakeholders in the downstream oil supply chain.The proposed mixed integer nonlinear programming model constrains supply and demand capacity,transportation routes,oil substitution rules and pipeline freight levels.Logarithm transformation and price discretization are introduced for model linear approximation.Simulation experiments are carried out in the oil distribution system in South China.The results show that compared to the business-as-usual scheme,the new scheme saves transportation cost by 3.48%,increases pipeline turnover by 5.7%,and reduces energy consumption and emissions by 7.66%and 6.77%.It is proved that the proposed method improves the revenue of the whole system,achieves fair revenue distribution,and also improves the energy and environmental benefits of the oil supply chain.
文摘针对情景记忆算法中记忆池中的样本利用率低的问题,提出了一种基于情景记忆和值函数分解框架相结合的合作型多智能体强化学习算法,即情景记忆值分解(episodic memory value decomposition,EMVD)算法。EMVD算法在情景记忆部分以时间差分误差平方为依据来更新记忆池,使记忆池中一直保留对学习效果提升更重要的情景记忆样本,并将情景记忆算法与神经网络相结合,提高了算法的收敛速度。为了将EMVD算法应用于机器人协作运输任务中,设定机器人和运输目标的位置为状态,并且设计了回报函数。仿真结果表明,EMVD算法可以探索出机器人协作运输任务的最优策略,提高了算法的收敛速度。
基金supported by the National Natural Science Foundation of China(Grant No.52175237)Beijing Municipal Science and Technology Commission,China(Grant No.Z211100004021022).
文摘Given limited terrain adaptability,most existing multirobot cooperative transportation systems(MRCTSs)mainly work on flat pavements,restricting their outdoor applications.The connectors'finite deformation capability and the control strategies'limitations are primarily responsible for this phenomenon.This study proposes a novel MRCTS based on tracked mobile robots(TMRs)to improve terrain adaptability and expand the application scenarios of MRCTSs.In structure design,we develop a novel 6-degree-of-freedom passive adaptive connector to link multiple TMRs and the transported object(the communal payload).In addition,the connector is set with sensors to measure the position and orientation of the robot with respect to the object for feedback control.In the control strategy,we present a virtual leader-physical follower collaborative paradigm.The leader robot is imaginary to describe the movement of the entire system and manage the follower robots.All the TMRs in the system act as follower robots to transport the object cooperatively.Having divided the whole control structure into the leader robot level and the follower robot level,we convert the motion control of the two kinds of robots to trajectory tracking control problems and propose a novel double closed-loop kinematics control framework.Furthermore,a control law satisfying saturation constraints is derived to ensure transportation stability.An adaptive control algorithm processes the wheelbase uncertainty of the TMR.Finally,we develop a prototype of the TMR-based MRCTS for experiments.In the trajectory tracking experiment,the developed MRCTS with the proposed control scheme can converge to the reference trajectory in the presence of initial tracking errors in a finite time.In the outdoor experiment,the proposed MRCTS consisting of four TMRs can successfully transport a payload weighing 60 kg on an uneven road with the single TMR's maximum load limited to 15 kg.The experimental results demonstrate the effectiveness of the structural design and control strategies of the TMR-based MRCTS.
