The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circu...The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circumstance allows them to obtain information in front and behind,enhancing vehicles perception ability.This paper proposes an intelligent back-looking distance driver model(IBDM)considering the desired distance of the following vehicle in homogeneous CAVs environment.Based on intelligent driver model(IDM),the IBDM integrates behind information of vehicles as a control term.The stability condition against a small perturbation is analyzed using linear stability theory in the homogeneous traffic flow.To validate the theoretical analysis,simulations are carried out on a single lane under the open boundary condition,and compared with the IDM not considering the following vehicle and the extended IDM considering the information of vehicle preceding and next preceding.Six scenarios are designed to evaluate the results under different disturbance strength,disturbance location,and initial platoon space distance.The results reveal that the IBDM has an advantage over IDM and the extended IDM in control of CAVs car-following process in maintaining string stability,and the stability improves by increasing the proportion of the new item.展开更多
The effects of different habits of the drivers on gear shifting strategies for manual powertrain were investigated. For the realization of simulation, the shifting habits of the drivers were conducted in the Advisor s...The effects of different habits of the drivers on gear shifting strategies for manual powertrain were investigated. For the realization of simulation, the shifting habits of the drivers were conducted in the Advisor software to investigate and compare the emission rates. Simulation was developed based on the optimal gear shifting strategy and criteria and was validated both in fuel economy and emissions by analyzing the results in the various driving cycle and driving styles. To explore an optimal gear shifting strategy with best fuel economy and lowest emission for a manual transmission, a strategy was designed with a highest possible gear criterion as long as the torque requirement can be satisfied. Based on two different criteria, namely the engine working conditions and the driver's intention, the governing parameters in decision making for gear shifting of manual transmission in conventional engine were discussed. It is also shown that the optimum gear shifting strategy is based on that both the engine state and the driver's intention eliminates unnecessary shiftings that are present when the intention is overlooked. The optimum shifting habit and the best driving cycle in terms of minimum emissions and fuel consumption were proposed.展开更多
在“双碳”目标背景下,探究重庆三峡库区农业碳排放特征及其驱动因素,可为库区低碳农业发展提供科学依据。采用联合国政府间气候变化专门委员会(IPCC)的因子法测算2015—2022年重庆三峡库区农业碳排放量,系统分析库区农业碳排放量和强...在“双碳”目标背景下,探究重庆三峡库区农业碳排放特征及其驱动因素,可为库区低碳农业发展提供科学依据。采用联合国政府间气候变化专门委员会(IPCC)的因子法测算2015—2022年重庆三峡库区农业碳排放量,系统分析库区农业碳排放量和强度时空分异特征,利用Tapio脱钩模型分析库区农业碳排放量与农业经济增长的脱钩关系,并进一步运用LMDI(logarithmic mean divisia index)模型解析库区农业碳排放驱动因素。结果表明:重庆三峡库区农业碳排放总量整体呈波动降低趋势,农业碳排放总量从2015年的645.89万t降至2022年的620.74万t,库区农业碳排放主要来源为农田土壤碳排放和畜禽养殖碳排放。库区农业碳排放强度总体呈下降趋势,各区县间碳排放强度差距逐渐缩小。2015—2022年,库区农业经济与农业碳排放量整体上呈脱钩关系。随着农业生产的恢复与发展,农业产值增长,农业碳排放量增加。脱钩关系以2019年为节点表现为由强脱钩向弱脱钩转变。农业生产效率、农业人口规模、农业产业结构对库区农业碳排放量的增长具有抑制作用,而农业经济规模对农业碳排放量的增长则具有促进作用。基于以上结果,本文提出减少禽畜养殖业碳排放量、控制农田土壤利用碳排放量和发挥农业碳排放驱动因素抑制作用等相关建议,以期为库区低碳农业发展提供理论依据。展开更多
Driving safety field(DSF) model has been proposed to represent comprehensive driving risk formed by interactions of driver-vehicle-road in mixed traffic environment. In this work, we establish an optimization model ba...Driving safety field(DSF) model has been proposed to represent comprehensive driving risk formed by interactions of driver-vehicle-road in mixed traffic environment. In this work, we establish an optimization model based on grey relation degree analysis to calibrate risk coefficients of DSF model. To solve the optimum solution, a genetic algorithm is employed. Finally, the DSF model is verified through a real-world driving experiment. Results show that the DSF model is consistent with driver's hazard perception and more sensitive than TTC. Moreover, the proposed DSF model offers a novel way for criticality assessment and decision-making of advanced driver assistance systems and intelligent connected vehicles.展开更多
中国西北干旱半干旱区是中国北方重要的生态屏障。在气候变化和人类活动双重影响下,开展该区域植被净生态系统生产力(NEP)时空变化特征及关键驱动因素的研究对区域生态安全与可持续发展意义重大。基于气象、植被、土壤和地形等多源遥感...中国西北干旱半干旱区是中国北方重要的生态屏障。在气候变化和人类活动双重影响下,开展该区域植被净生态系统生产力(NEP)时空变化特征及关键驱动因素的研究对区域生态安全与可持续发展意义重大。基于气象、植被、土壤和地形等多源遥感及再分析数据集,利用改进的CASA模型、土壤呼吸模型、地理探测器等方法,分析了2000-2022年中国西北干旱半干旱区植被NEP时空变化特征及其关键影响因子。主要研究结果表明:(1)研究区植被NEP多年均值为135.32g C/m^(2),整体呈碳汇功能,年内碳汇主要集中在5-9月份;碳源区主要集中在山前低植被覆盖区和东部沙地,面积占29%。(2)近23年研究区植被NEP以增加趋势为主,变化速率为3.98g C m^(-2) a^(-1)。空间上,呈增加趋势的面积占比为88.77%,减少趋势主要集中在天山山脉山前平原区。(3)LAI对植被NEP空间分布的解释力最强(0.75),其次是土壤湿度(0.42),不同要素交互对NEP均呈现双因子增强或非线性增强作用;LAI、降水和太阳辐射与研究区植被NEP主要呈显著正相关,显著正相关面积分别占73.79%、16.28%和15.10%,与降水呈显著正相关区域主要分布在西部山区、呼伦湖和东部沙地,与太阳辐射呈显著正相关区域主要分布在东部沙地。(4)西北干旱半干旱区气候变化对NEP的贡献度大于80%的地区占59.42%。人类活动对NEP的贡献率高于60%的区域占比为13.90%,集中分布在阿尔泰山和天山山脉的山前平原区,极少数散落分布在东部沙地。研究结果有助于了解植被NEP对气候变化的响应机制,为实现"双碳"目标提供理论依据。展开更多
基金Project(2018YFB1600600)supported by the National Key Research and Development Program,ChinaProject(20YJAZH083)supported by the Ministry of Education,China+1 种基金Project(20YJAZH083)supported by the Humanities and Social Sciences,ChinaProject(51878161)supported by the National Natural Science Foundation of China。
文摘The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circumstance allows them to obtain information in front and behind,enhancing vehicles perception ability.This paper proposes an intelligent back-looking distance driver model(IBDM)considering the desired distance of the following vehicle in homogeneous CAVs environment.Based on intelligent driver model(IDM),the IBDM integrates behind information of vehicles as a control term.The stability condition against a small perturbation is analyzed using linear stability theory in the homogeneous traffic flow.To validate the theoretical analysis,simulations are carried out on a single lane under the open boundary condition,and compared with the IDM not considering the following vehicle and the extended IDM considering the information of vehicle preceding and next preceding.Six scenarios are designed to evaluate the results under different disturbance strength,disturbance location,and initial platoon space distance.The results reveal that the IBDM has an advantage over IDM and the extended IDM in control of CAVs car-following process in maintaining string stability,and the stability improves by increasing the proportion of the new item.
文摘The effects of different habits of the drivers on gear shifting strategies for manual powertrain were investigated. For the realization of simulation, the shifting habits of the drivers were conducted in the Advisor software to investigate and compare the emission rates. Simulation was developed based on the optimal gear shifting strategy and criteria and was validated both in fuel economy and emissions by analyzing the results in the various driving cycle and driving styles. To explore an optimal gear shifting strategy with best fuel economy and lowest emission for a manual transmission, a strategy was designed with a highest possible gear criterion as long as the torque requirement can be satisfied. Based on two different criteria, namely the engine working conditions and the driver's intention, the governing parameters in decision making for gear shifting of manual transmission in conventional engine were discussed. It is also shown that the optimum gear shifting strategy is based on that both the engine state and the driver's intention eliminates unnecessary shiftings that are present when the intention is overlooked. The optimum shifting habit and the best driving cycle in terms of minimum emissions and fuel consumption were proposed.
文摘在“双碳”目标背景下,探究重庆三峡库区农业碳排放特征及其驱动因素,可为库区低碳农业发展提供科学依据。采用联合国政府间气候变化专门委员会(IPCC)的因子法测算2015—2022年重庆三峡库区农业碳排放量,系统分析库区农业碳排放量和强度时空分异特征,利用Tapio脱钩模型分析库区农业碳排放量与农业经济增长的脱钩关系,并进一步运用LMDI(logarithmic mean divisia index)模型解析库区农业碳排放驱动因素。结果表明:重庆三峡库区农业碳排放总量整体呈波动降低趋势,农业碳排放总量从2015年的645.89万t降至2022年的620.74万t,库区农业碳排放主要来源为农田土壤碳排放和畜禽养殖碳排放。库区农业碳排放强度总体呈下降趋势,各区县间碳排放强度差距逐渐缩小。2015—2022年,库区农业经济与农业碳排放量整体上呈脱钩关系。随着农业生产的恢复与发展,农业产值增长,农业碳排放量增加。脱钩关系以2019年为节点表现为由强脱钩向弱脱钩转变。农业生产效率、农业人口规模、农业产业结构对库区农业碳排放量的增长具有抑制作用,而农业经济规模对农业碳排放量的增长则具有促进作用。基于以上结果,本文提出减少禽畜养殖业碳排放量、控制农田土壤利用碳排放量和发挥农业碳排放驱动因素抑制作用等相关建议,以期为库区低碳农业发展提供理论依据。
基金Projects(51475254,51625503)supported by the National Natural Science Foundation of ChinaProject(MCM20150302)supported by the Joint Project of Tsinghua and China Mobile,ChinaProject supported by the joint Project of Tsinghua and Daimler Greater China Ltd.,Beijing,China
文摘Driving safety field(DSF) model has been proposed to represent comprehensive driving risk formed by interactions of driver-vehicle-road in mixed traffic environment. In this work, we establish an optimization model based on grey relation degree analysis to calibrate risk coefficients of DSF model. To solve the optimum solution, a genetic algorithm is employed. Finally, the DSF model is verified through a real-world driving experiment. Results show that the DSF model is consistent with driver's hazard perception and more sensitive than TTC. Moreover, the proposed DSF model offers a novel way for criticality assessment and decision-making of advanced driver assistance systems and intelligent connected vehicles.
文摘中国西北干旱半干旱区是中国北方重要的生态屏障。在气候变化和人类活动双重影响下,开展该区域植被净生态系统生产力(NEP)时空变化特征及关键驱动因素的研究对区域生态安全与可持续发展意义重大。基于气象、植被、土壤和地形等多源遥感及再分析数据集,利用改进的CASA模型、土壤呼吸模型、地理探测器等方法,分析了2000-2022年中国西北干旱半干旱区植被NEP时空变化特征及其关键影响因子。主要研究结果表明:(1)研究区植被NEP多年均值为135.32g C/m^(2),整体呈碳汇功能,年内碳汇主要集中在5-9月份;碳源区主要集中在山前低植被覆盖区和东部沙地,面积占29%。(2)近23年研究区植被NEP以增加趋势为主,变化速率为3.98g C m^(-2) a^(-1)。空间上,呈增加趋势的面积占比为88.77%,减少趋势主要集中在天山山脉山前平原区。(3)LAI对植被NEP空间分布的解释力最强(0.75),其次是土壤湿度(0.42),不同要素交互对NEP均呈现双因子增强或非线性增强作用;LAI、降水和太阳辐射与研究区植被NEP主要呈显著正相关,显著正相关面积分别占73.79%、16.28%和15.10%,与降水呈显著正相关区域主要分布在西部山区、呼伦湖和东部沙地,与太阳辐射呈显著正相关区域主要分布在东部沙地。(4)西北干旱半干旱区气候变化对NEP的贡献度大于80%的地区占59.42%。人类活动对NEP的贡献率高于60%的区域占比为13.90%,集中分布在阿尔泰山和天山山脉的山前平原区,极少数散落分布在东部沙地。研究结果有助于了解植被NEP对气候变化的响应机制,为实现"双碳"目标提供理论依据。
