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
With the rapid development of connected autonomous vehicles(CAVs),both road infrastructure and transport are experiencing a profound transformation.In recent years,the cooperative perception and control supported infr...With the rapid development of connected autonomous vehicles(CAVs),both road infrastructure and transport are experiencing a profound transformation.In recent years,the cooperative perception and control supported infrastructure-vehicle system(IVS)attracted increasing attention in the field of intelligent transportation systems(ITS).The perception information of surrounding objects can be obtained by various types of sensors or communication networks.Control commands generated by CAVs or infrastructure can be executed promptly and accurately to improve the overall performance of the transportation system in terms of safety,efficiency,comfort and energy saving.This study presents a comprehensive review of the research progress achieved upon cooperative perception and control supported IVS over the past decade.By focusing on the essential interactions between infrastructure and CAVs and between CAVs,the infrastructure-vehicle cooperative perception and control methods are summarized and analyzed.Furthermore,the mining site as a closed scenario was used to show the current application of IVS.Finally,the existing issues of the cooperative perception and control technology implementation are discussed,and the recommendation for future research directions are proposed.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National Key R&D Program of China under Grant 2020YFB1600302.
文摘With the rapid development of connected autonomous vehicles(CAVs),both road infrastructure and transport are experiencing a profound transformation.In recent years,the cooperative perception and control supported infrastructure-vehicle system(IVS)attracted increasing attention in the field of intelligent transportation systems(ITS).The perception information of surrounding objects can be obtained by various types of sensors or communication networks.Control commands generated by CAVs or infrastructure can be executed promptly and accurately to improve the overall performance of the transportation system in terms of safety,efficiency,comfort and energy saving.This study presents a comprehensive review of the research progress achieved upon cooperative perception and control supported IVS over the past decade.By focusing on the essential interactions between infrastructure and CAVs and between CAVs,the infrastructure-vehicle cooperative perception and control methods are summarized and analyzed.Furthermore,the mining site as a closed scenario was used to show the current application of IVS.Finally,the existing issues of the cooperative perception and control technology implementation are discussed,and the recommendation for future research directions are proposed.