In order to improve the efficiency of traffic signal control for an over-saturated intersection group, a nondominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ） based traffic signal control optimization algorithm is prop...In order to improve the efficiency of traffic signal control for an over-saturated intersection group, a nondominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ） based traffic signal control optimization algorithm is proposed. The throughput maximum and average queue ratio minimum for the critical route of the intersection group are selected as the optimization objectives of the traffic signal control for the over-saturated condition. The consequences of the efficiency between traffic signal timing plans generated by the proposed algorithm and a commonly utilized signal timing optimization software Synchro are compared in a VISSIM signal control application programming interfaces （SCAPI） simulation environment by using real filed observed traffic data. The simulation results indicate that the signal timing plan generated by the proposed algorithm is more efficient in managing oversaturated flows at intersection groups, and, thus, it has the capability of optimizing signal timing under the over-saturated conditions.展开更多
Signal retiming is a prominent way that transportation agencies use to fight congestion and change of traffic pattern.Performance evaluations of traffic conditions at signalized intersections and arterials provide act...Signal retiming is a prominent way that transportation agencies use to fight congestion and change of traffic pattern.Performance evaluations of traffic conditions at signalized intersections and arterials provide actionable data for agencies to make well-informed and prioritized signal retiming decisions.However,the abundance of data sources,the lack of standardized evaluation methods and oftentimes the shortage of resources make it a difficult endeavor.The review detailed in this paper examines the advances made in traffic signal performance evaluation.We establish the necessity for the evaluations,study the process of continuous improvement of traffic signal performance using the evaluations,and then examine multiple methodologies in a plethora of research endeavors.Particularly,we focus on probe vehicles and sensors data,the two major sources of data.We discuss how sensors are connected to signal controllers to provide relevant in-depth traffic data including speed and occupancy measures.We also review the nature of probe vehicles and the level of penetration.We then define and summarize performance measures derived from both sources,to aid in performance evaluations.For performance evaluation methods,we discuss the research studies and provide summaries including advantages and disadvantages of the methods used,as well as a holistic outlook for future research.This paper is aimed to provide a comprehensive review on the state-of-the-art to benefit researcher,traffic agencies,and commercial entities that thrive to improve safety and efficiency of traffic signals through performance evaluations.展开更多
This paper considers the optimal traffic signal setting for an urban arterial road. By introducing the concepts of synchronization rate and non-synchronization degree, a mathematical model is constructed and an optimi...This paper considers the optimal traffic signal setting for an urban arterial road. By introducing the concepts of synchronization rate and non-synchronization degree, a mathematical model is constructed and an optimization problem is posed. Then, a new iterative algorithm is developed to solve this optimal traffic control signal setting problem. Convergence properties for this iterative algorithm are established. Finally, a numerical example is solved to illustrate the effectiveness of the method.展开更多
Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is co...Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.展开更多
基金The National Natural Science Foundation of China(No.51208054)
文摘In order to improve the efficiency of traffic signal control for an over-saturated intersection group, a nondominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ） based traffic signal control optimization algorithm is proposed. The throughput maximum and average queue ratio minimum for the critical route of the intersection group are selected as the optimization objectives of the traffic signal control for the over-saturated condition. The consequences of the efficiency between traffic signal timing plans generated by the proposed algorithm and a commonly utilized signal timing optimization software Synchro are compared in a VISSIM signal control application programming interfaces （SCAPI） simulation environment by using real filed observed traffic data. The simulation results indicate that the signal timing plan generated by the proposed algorithm is more efficient in managing oversaturated flows at intersection groups, and, thus, it has the capability of optimizing signal timing under the over-saturated conditions.
基金supported in part by Tennessee Department of Transportation(TDOT)and Federal Highway Administration(FHWA),under TDOT grant RES2021-09
文摘Signal retiming is a prominent way that transportation agencies use to fight congestion and change of traffic pattern.Performance evaluations of traffic conditions at signalized intersections and arterials provide actionable data for agencies to make well-informed and prioritized signal retiming decisions.However,the abundance of data sources,the lack of standardized evaluation methods and oftentimes the shortage of resources make it a difficult endeavor.The review detailed in this paper examines the advances made in traffic signal performance evaluation.We establish the necessity for the evaluations,study the process of continuous improvement of traffic signal performance using the evaluations,and then examine multiple methodologies in a plethora of research endeavors.Particularly,we focus on probe vehicles and sensors data,the two major sources of data.We discuss how sensors are connected to signal controllers to provide relevant in-depth traffic data including speed and occupancy measures.We also review the nature of probe vehicles and the level of penetration.We then define and summarize performance measures derived from both sources,to aid in performance evaluations.For performance evaluation methods,we discuss the research studies and provide summaries including advantages and disadvantages of the methods used,as well as a holistic outlook for future research.This paper is aimed to provide a comprehensive review on the state-of-the-art to benefit researcher,traffic agencies,and commercial entities that thrive to improve safety and efficiency of traffic signals through performance evaluations.
基金Supported by the National Natural Science Foundation of China (10671045)
文摘This paper considers the optimal traffic signal setting for an urban arterial road. By introducing the concepts of synchronization rate and non-synchronization degree, a mathematical model is constructed and an optimization problem is posed. Then, a new iterative algorithm is developed to solve this optimal traffic control signal setting problem. Convergence properties for this iterative algorithm are established. Finally, a numerical example is solved to illustrate the effectiveness of the method.
基金financially supported by the Technology Development Fund of China Academy of Machinery Science and Technology(No.170221ZY01)。
文摘Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.
