This article models a novel driving-day-based tradable credit scheme (DD-TCS) to alleviate urban traffic congestion. In this model, car-using allowances (in terms of the number of days in a month, termed as "cred...This article models a novel driving-day-based tradable credit scheme (DD-TCS) to alleviate urban traffic congestion. In this model, car-using allowances (in terms of the number of days in a month, termed as "credit") are freely and uniformly allocated to all travellers, who are also allowed to trade them in a market according to his/her travel needs (e.g. driving more or fewer days than the free endowment). As opposed to most studies on TCS, this paper explicitly considers the transaction cost (e.g. infor-mation cost of finding potential traders) in the trading market. To assess the feasibility of DD-TCS, we compare it against the license plate rationing (LPR) scheme, which has been practically implemented in many cities such as Beijing and Chengdu in China. Taking the performance of LPR as a benchmark, we quantify the threshold values of the transaction cost in DD-TCS when the two schemes yield equivalent performance (in terms of the total gener-alized cost). In numerical studies, we also compare the DD-TCS and LPR with the no-action case and the congestion pricing case (representing the theoretical optimum). Results show that both DD-TCS and LPR outperform the no-action case under certain conditions. With small trans-action cost, DD-TCS may achieve a lower system cost that can be very close to the ideal optimum. In addition, parameter analysis shows that DD-TCS performs better than LPR in a wide range of transaction cost, where the threshold values appear to account for a considerable portion of the auto travel time. This implies that DD-TCS will be more appealing than LPR in practice because a transaction cost lower than the extremely large threshold values can be easily achieved for the trading market, e.g. via a mobile platform and modern communication techniques.展开更多
在5 G移动通信网络中,大量采用绿色和电网能源混合供电的通信基站可以显著降低运营成本。针对异构网络混合能源供电基站的用户接入问题,文中提出了基于绿色能源感知的综合效用函数接入算法(Green Energy Perception Comprehensive utili...在5 G移动通信网络中,大量采用绿色和电网能源混合供电的通信基站可以显著降低运营成本。针对异构网络混合能源供电基站的用户接入问题,文中提出了基于绿色能源感知的综合效用函数接入算法(Green Energy Perception Comprehensive utility function,GEPC)和结合调节的综合效用函数接入算法(Green Energy Perception based Comprehensive unility function Adjustment algorithm,GEPCA)。用户首先根据基站的绿色能源状况、接入信噪比等接入选择参数加权计算出效用值,选择效用值最小的基站接入,用户接入基站后,通过基站对接入用户进行调节来实现降低总能耗费用的效果。MATLAB仿真表明:GEPC算法在低负载时相比于RSRP(基于用户接收信号强度)、SINR(基于用户最大信干噪比)算法可以更有效地降低总能量消耗费用;在高负载时GEPCA算法和NEAT(绿色能源用户感知接入)算法相比显著提高了绿色能源的利用率,使之达到90%,同时有利于实现异构网络的负载均衡。展开更多
基金supported by the National Natural Science Foundation of China (Project No.51608455)
文摘This article models a novel driving-day-based tradable credit scheme (DD-TCS) to alleviate urban traffic congestion. In this model, car-using allowances (in terms of the number of days in a month, termed as "credit") are freely and uniformly allocated to all travellers, who are also allowed to trade them in a market according to his/her travel needs (e.g. driving more or fewer days than the free endowment). As opposed to most studies on TCS, this paper explicitly considers the transaction cost (e.g. infor-mation cost of finding potential traders) in the trading market. To assess the feasibility of DD-TCS, we compare it against the license plate rationing (LPR) scheme, which has been practically implemented in many cities such as Beijing and Chengdu in China. Taking the performance of LPR as a benchmark, we quantify the threshold values of the transaction cost in DD-TCS when the two schemes yield equivalent performance (in terms of the total gener-alized cost). In numerical studies, we also compare the DD-TCS and LPR with the no-action case and the congestion pricing case (representing the theoretical optimum). Results show that both DD-TCS and LPR outperform the no-action case under certain conditions. With small trans-action cost, DD-TCS may achieve a lower system cost that can be very close to the ideal optimum. In addition, parameter analysis shows that DD-TCS performs better than LPR in a wide range of transaction cost, where the threshold values appear to account for a considerable portion of the auto travel time. This implies that DD-TCS will be more appealing than LPR in practice because a transaction cost lower than the extremely large threshold values can be easily achieved for the trading market, e.g. via a mobile platform and modern communication techniques.
文摘在5 G移动通信网络中,大量采用绿色和电网能源混合供电的通信基站可以显著降低运营成本。针对异构网络混合能源供电基站的用户接入问题,文中提出了基于绿色能源感知的综合效用函数接入算法(Green Energy Perception Comprehensive utility function,GEPC)和结合调节的综合效用函数接入算法(Green Energy Perception based Comprehensive unility function Adjustment algorithm,GEPCA)。用户首先根据基站的绿色能源状况、接入信噪比等接入选择参数加权计算出效用值,选择效用值最小的基站接入,用户接入基站后,通过基站对接入用户进行调节来实现降低总能耗费用的效果。MATLAB仿真表明:GEPC算法在低负载时相比于RSRP(基于用户接收信号强度)、SINR(基于用户最大信干噪比)算法可以更有效地降低总能量消耗费用;在高负载时GEPCA算法和NEAT(绿色能源用户感知接入)算法相比显著提高了绿色能源的利用率,使之达到90%,同时有利于实现异构网络的负载均衡。
