With the rapid development of novel vehicle technologies,air taxis are becoming a new transport servicing model to achieve better urban air mobility(UAM)systems.The UAM system can mitigate severe traffic congestion pr...With the rapid development of novel vehicle technologies,air taxis are becoming a new transport servicing model to achieve better urban air mobility(UAM)systems.The UAM system can mitigate severe traffic congestion problems on the ground in many metropolitan areas effectively.This research aims to design a robust UAM network to serve broader mobility needs in the future.In this study,we consider the two main competition transportation modes of air taxis(luxury ground taxis and ordinary ground taxis)and establish a travel mode choice behaviour model based on the multinomial logit choice approach to determine the aggregate air taxi demand flow.Furthermore,we account for the varying temporal preferences of different UAM passengers by incorporating different time values for heterogeneous passengers,such as leisure and business passengers,into the travel mode choice behaviour model.Additionally,to address uncertainties in user demand,we introduce a robust optimisation model for vertiport location under a budget uncertainty set.This model is then reformulated as a mixed-integer linear programming model to address computational challenges.Ultimately,we conduct a series of numerical experiments to showcase the effectiveness of our proposed mathematical model.The outcomes of this research offer valuable managerial insights and implications,aiding governments and UAM operators in scientifically designing UAM networks and making strategic decisions regarding infrastructure investments.展开更多
随着新型城镇化进程的持续推进与城市群交通矛盾的激化,构建立体化交通网络已成为缓解城市出行供需失衡的关键路径。当前,在“双碳”战略目标驱动下,垂直起降飞行器(electric vertical take-off and landing,eVTOL)技术发展呈现出显著...随着新型城镇化进程的持续推进与城市群交通矛盾的激化,构建立体化交通网络已成为缓解城市出行供需失衡的关键路径。当前,在“双碳”战略目标驱动下,垂直起降飞行器(electric vertical take-off and landing,eVTOL)技术发展呈现出显著的可持续性与智能化导向特征,因而已成为新型城市交通体系建设的重点攻关方向。基于Web of Science核心合集数据库,运用CiteSpace和VOSviewer等文献计量分析工具,对2019—2024年间eVTOL研究领域发表的561篇核心文献的主题词、国家、机构、作者、期刊及被引情况等进行系统分析,主要对发文量、被引量和合作网络进行分析;对eVTOL电推进关键技术研究热点、挑战与研究趋势和eVTOL领域国际未来发展趋势进行分析,为该领域的研究热点和趋势提供参考依据;系统揭示了基础研究、技术研发、产业应用的协同演进规律,为把握国际前沿动态提供定量化参考依据。展开更多
Siting low-altitude takeoff and landing platforms(vertiports)is a fundamental challenge for developing urban air mobility(UAM).This study formulates this issue as a variant of the capacitated facility location problem...Siting low-altitude takeoff and landing platforms(vertiports)is a fundamental challenge for developing urban air mobility(UAM).This study formulates this issue as a variant of the capacitated facility location problem,incorporating flight range and service capacity constraints,and proposes SPID,a deep reinforcement learning(DRL)-based solution framework that models the problem as a Markov decision process.To handle dynamic coverage,the designed DRL framework-based SPID uses a multi-head attention mechanism to capture spatiotemporal patterns,followed by integrating dynamic and static information into a unified input state vector.Afterward,a gated recurrent unit(GRU)is used to generate the query vector,thereby enhancing sequential decision-making.The action network within the DRL network is regulated by a loss function that integrates service distance costs with unmet demand penalties,enabling end-to-end optimization.Subsequent experimental results demonstrate that SPID significantly enhances solution efficiency and robustness compared with traditional methods under flight and capacity constraints.Especially,across the social performance metrics emphasized in this study,SPID outperforms the suboptimal solutions produced by traditional clustering and graph neural network(GNN)-based methods by up to approximately 29%.This improvement comes with an increase in distance-based cost that is kept within 10%.Overall,we demonstrate an efficient,scalable approach for vertiport siting,supporting rapid decisionmaking in large-scale UAM scenarios.展开更多
基金Department of Aeronautical and Avi-ation Engineering,The Hong Kong Polytechnic University,Hong Kong SAR(RJJ9,RJ85).
文摘With the rapid development of novel vehicle technologies,air taxis are becoming a new transport servicing model to achieve better urban air mobility(UAM)systems.The UAM system can mitigate severe traffic congestion problems on the ground in many metropolitan areas effectively.This research aims to design a robust UAM network to serve broader mobility needs in the future.In this study,we consider the two main competition transportation modes of air taxis(luxury ground taxis and ordinary ground taxis)and establish a travel mode choice behaviour model based on the multinomial logit choice approach to determine the aggregate air taxi demand flow.Furthermore,we account for the varying temporal preferences of different UAM passengers by incorporating different time values for heterogeneous passengers,such as leisure and business passengers,into the travel mode choice behaviour model.Additionally,to address uncertainties in user demand,we introduce a robust optimisation model for vertiport location under a budget uncertainty set.This model is then reformulated as a mixed-integer linear programming model to address computational challenges.Ultimately,we conduct a series of numerical experiments to showcase the effectiveness of our proposed mathematical model.The outcomes of this research offer valuable managerial insights and implications,aiding governments and UAM operators in scientifically designing UAM networks and making strategic decisions regarding infrastructure investments.
文摘随着新型城镇化进程的持续推进与城市群交通矛盾的激化,构建立体化交通网络已成为缓解城市出行供需失衡的关键路径。当前,在“双碳”战略目标驱动下,垂直起降飞行器(electric vertical take-off and landing,eVTOL)技术发展呈现出显著的可持续性与智能化导向特征,因而已成为新型城市交通体系建设的重点攻关方向。基于Web of Science核心合集数据库,运用CiteSpace和VOSviewer等文献计量分析工具,对2019—2024年间eVTOL研究领域发表的561篇核心文献的主题词、国家、机构、作者、期刊及被引情况等进行系统分析,主要对发文量、被引量和合作网络进行分析;对eVTOL电推进关键技术研究热点、挑战与研究趋势和eVTOL领域国际未来发展趋势进行分析,为该领域的研究热点和趋势提供参考依据;系统揭示了基础研究、技术研发、产业应用的协同演进规律,为把握国际前沿动态提供定量化参考依据。
文摘Siting low-altitude takeoff and landing platforms(vertiports)is a fundamental challenge for developing urban air mobility(UAM).This study formulates this issue as a variant of the capacitated facility location problem,incorporating flight range and service capacity constraints,and proposes SPID,a deep reinforcement learning(DRL)-based solution framework that models the problem as a Markov decision process.To handle dynamic coverage,the designed DRL framework-based SPID uses a multi-head attention mechanism to capture spatiotemporal patterns,followed by integrating dynamic and static information into a unified input state vector.Afterward,a gated recurrent unit(GRU)is used to generate the query vector,thereby enhancing sequential decision-making.The action network within the DRL network is regulated by a loss function that integrates service distance costs with unmet demand penalties,enabling end-to-end optimization.Subsequent experimental results demonstrate that SPID significantly enhances solution efficiency and robustness compared with traditional methods under flight and capacity constraints.Especially,across the social performance metrics emphasized in this study,SPID outperforms the suboptimal solutions produced by traditional clustering and graph neural network(GNN)-based methods by up to approximately 29%.This improvement comes with an increase in distance-based cost that is kept within 10%.Overall,we demonstrate an efficient,scalable approach for vertiport siting,supporting rapid decisionmaking in large-scale UAM scenarios.
