摘要
作为公共交通出行的重要补充,出租车运营产生的燃料消耗和CO_(2)排放模式与城市居民的出行足迹相一致。在公共交通电气化的背景下,精准计算多种燃料类型出租车出行的CO_(2)排放量并挖掘其在城市不同区域的时空特征,对了解城市居民出行CO_(2)排放的空间特征与实现城市CO_(2)减排具有重要的现实意义。运用兰州市出租车运行轨迹数据,通过隐马尔可夫模型轨迹匹配实现居民出租车出行轨迹与路径的精准识别,使用COPERT模型计算了汽油、CNG、油气混动三种燃料类型出租车的CO_(2)排放量,并在不同时空尺度对居民出行CO_(2)排放的时空特征进行分析。研究结果发现:由于电气化进程中汽油车数量的减少,在三种燃料类型出租车CO_(2)排放量中,油气混动车最高,CNG车次之,汽油车的CO_(2)排放量最低,工作日早晚高峰时段CO_(2)排放量高于非工作日,而凌晨时段CO_(2)排放量较低。CO_(2)排放热点主要集中在交通枢纽、商圈和住宅区附近,且以兰州市各城市中心区为原点沿带状向城市外围递减,这些区域的高排放量反映了城市居民的出行需求和活动模式。研究结论可作为多燃料类型出租车温室气体排放的精准测算与城市公共交通减排路径的研究基础,同时也对居民出行碳排放的时空特征挖掘和推动城市交通低碳出行提供依据。
As an important complement to public transport travel,the fuel consumption and CO_(2) emission patterns generated by taxi operations are consistent with the travel footprint of urban residents.In the context of public transport electrification,it is of great practical significance to accurately calculate the CO_(2) emissions of taxi trips with various fuel types and explore their spatio-temporal characteristics in different urban areas to understand the spatial characteristics of CO_(2) emissions from urban residents'trips and realize urban CO_(2) emission reduction.In this paper,the trajectory data of taxis in Lanzhou city are used to accurately identify the trajectory and path of resident taxis by using hidden Markov model trajectory matching.The CO_(2) emissions of taxis with gasoline,CNG and oil-gas hybrid fuel types are calculated by using COPERT model,and the spatio-temporal characteristics of CO_(2) emissions from residents'travel are analyzed at different spatio-temporal scales.The results show that,due to the reduction of the number of gasoline and oil vehicles in the electrification process,the CO_(2) emission of the three fuel types of taxis is the highest,and that of the CNG vehicles is the lowest,and the CO_(2) emission of the gasoline vehicles is higher in the morning and evening peak hours on working days than in non-working days,and the CO_(2) emission is lower in the morning hours.The hot spots of CO_(2) emission are mainly concentrated in the vicinity of transportation hubs,business districts and residential areas,and decline from the central areas of cities in Lanzhou along the belt to the outskirts of the city.The high emissions in these areas reflect the travel demand and activity pattern of urban residents.The conclusions of this study can be used as the basis for accurate calculation of greenhouse gas emissions of multi-fuel taxis and research on emission reduction paths of urban public transportation,and also provide a basis for mining the spatial-temporal characteristics of carbon emissions of residents'travel and promoting low-carbon travel of urban transportation.
作者
焦萍
马宁远
赵剑楠
方歆杰
刘赛
白洁
耿新瑞
JIAO Ping;MA Ningyuan;ZHAO Jiannan;FANG Xinjie;LIU Sai;BAI Jie;GENG Xinrui(Xi’an Aeronautical University,Xi’an,Shaanxi 710077,China;College of Transportation Engineering,Chang’an University,Xi’an,Shaanxi 710064,China;Party School of Shaanxi Provincial Cormittee of C.P.C(Shaanxi Academy of Governance),Xi’an,Shaanxi 710061,China;BYD Automotive Industry Co.,Ltd.,Xi’an,Shaanxi 710119,China)
出处
《黑龙江交通科技》
2024年第7期147-155,共9页
Communications Science and Technology Heilongjiang
基金
陕西省杰出青年基金(2021JC-27)。
