基于轨迹数据和深度学习的CNG出租车CO_(2)排放微观模型构建及碳减排效益评估方法  

Construction of a Micro Model for CO_(2)Emissions from CNG Taxi Based on Trajectory Data and Deep Learning Method and Evaluation of Carbon Reduction Benefits

作　　者：刘琪 陈碧宇[1,2] 李歆艺[1,2] LIU Qi;CHEN Biyu;LI Xinyi(State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;GeoComputation Center for Social Sciences,Wuhan University,Wuhan 430079,China)

机构地区：[1]武汉大学测绘遥感信息工程国家重点实验室,武汉430079 [2]武汉大学社会地理计算联合研究中心,武汉430079

出　　处：《地球信息科学学报》2023年第11期2191-2203,共13页Journal of Geo-information Science

基　　金：国家重点研发计划项目(2021YFB3900900);国家自然科学基金项目(42271473);湖北省自然科学基金杰青项目(2020CFA054)。

摘　　要：为准确评价压缩天然气(CNG)出租车的二氧化碳(CO_(2))减排效益,以武汉市为例,提出了一种基于深度学习的车辆微观CO_(2)排放模型来准确对城市内出租车的CO_(2)排放做时空分析,探究出租车在不同燃料情景下CO_(2)排放时空规律。路测实验中使用便携式排放测量系统(PEMS)收集车辆的CO_(2)排放数据,考虑车辆驾驶特征序列和燃料类型,借助BiLSTM算法构建了车辆微观CO_(2)排放模型,并验证其精度;利用提出的CO_(2)排放模型和武汉市15752辆出租车轨迹数据估算了武汉市出租车使用92#汽油和CNG的CO_(2)排放,探索CNG出租车的CO_(2)减排效益。结果表明,模型精度优于目前常用SVR、LSTM等回归算法和IVE、CMEM等物理模型,能够拟合真实车辆的CO_(2)排放变化,满足大范围估算城市出租车CO_(2)排放的精度需求,为车辆排放估算提供更好思路;实证结果发现,一天内,15752辆武汉市出租车全面使用CNG取代92#汽油可以减少22.05%的CO_(2)排放,同时揭示了CNG出租车在时间空间角度的CO_(2)排放规律以及CO_(2)减排效益。结果对政府交通部门推广车辆使用CNG燃料提供依据。Many large cities have been actively promoting the policy of"replacing oil with gas"for taxis.Taxis are converted from traditional gasoline consumption to Compressed Natural Gas(CNG)to achieve energy conservation and emission reduction goals.To accurately evaluate the carbon dioxide(CO_(2))emission reduction benefits of CNG taxis,taking Wuhan as an example,a vehicle microscopic CO_(2)emission model based on deep learning method and trajectory data was proposed to investigate the spatial-temporal characteristics of CO_(2)emissions of taxis under different fuel scenarios.Considering the driving feature sequence and fuel type of vehicles,the Portable Emission Measurement System(PEMS)was used to collect vehicle CO_(2)emission data in the road test experiment,then we constructed a vehicle microscopic CO_(2)emission model by the BiLSTM algorithm and further verified its accuracy.Based on the proposed CO_(2)emission model and the trajectory data of 15752 Wuhan taxis,the CO_(2)emissions throughout the entire lifecycle of urban taxis by 92#gasoline and CNG were estimated respectively to quantify the CO_(2)emission reduction benefits of CNG taxis.The results show that the proposed model had a higher accuracy than common regression algorithms such as SVR and LSTM,and the predictions matched well with real vehicle CO_(2)emission changes,meeting the accuracy for a large-scale estimation of urban taxi CO_(2)emissions.In addition,the accuracy of taxi CO_(2)emission estimation based on deep learning methods was also higher than that of physical microscopic models such as IVE and CMEM.Especially,when using CNG as vehicle fuel,the physical models had significant computational errors due to not involving technical parameters.The empirical results show that,taxi CO_(2)emissions using CNG were reduced by 22.05%during the PTW process and by 49.45%during the WTP process,compared to emissions using 92#gasoline.Our results reveal both the temporal and spatial patterns of taxi CO_(2)emission as well as the CO_(2)emission reduction benefit

关 键 词：CO_(2)排放 CNG出租车 车辆轨迹数据 深度学习 节能减排 时空分析 油改气 能源生命周期 

分 类 号：X734.2[环境科学与工程—环境工程]