武汉市出租车轨迹二氧化碳排放的时空模式分析
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摘要
车辆尾气是道路交通二氧化碳(CO_2)排放的重要来源,目前的研究主要集中在区域CO_2排放清单的计算和分析上,鲜有从微观层面上进行CO_2排放的反演分析。采用微观尾气排放模型对出租车行程轨迹的CO_2排放量进行定量反演,进而从点、线、面3个粒度对武汉市出租车行程轨迹CO_2排放的时空模式进行分析。研究结果表明,利用出租车轨迹可以有效地反演出租车CO_2排放量,并发现其在不同日期和时段具有明显的规律性。通过时空聚类技术发现了点粒度下的出租车CO_2排放的类簇数目存在一定的时空变化规律,采用数据场模型展示了线粒度下的道路线段出租车CO_2排放强度存在明显的时空分布规律,利用时空自相关技术揭示了面粒度下的区域出租车CO_2排放量具有较高的时空正相关性。研究成果可以为城市减排措施制定等提供辅助支持。
The intensive usage of vehicles has led to many urban problems including the traffic jams and the environmental pollutions. To handle these problems, most previous studies have used macro-based models to estimate and analyze the CO_2 emission inventories, but very few studies have focused on computing vehicle CO_2 emissions using a micro-based model. This paper presents an in-depth study on computing the CO_2 emissions from taxi trajectory data and further analyzing their spatiotemporal patterns from three aspects.Taking the Wuhan city as a case study, this paper uses the comprehensive modal emission model to quantitatively compute the CO_2 emissions from the taxi trajectory, and statistical results suggest that CO_2 emissions of the entire city has experienced a remarkable regularity in times of the day and days of the week. Specifically, the spatiotemporal patterns of CO_2 emissions from taxi trajectories are analyzed from three different aspects. Firstly, a spatial clustering algorithm is used to aggregate the taxi trajectory points with high CO_2 emissions, and the number of clusters displayed a regular change pattern. For example, the cluster number is gradually increased from workdays to weekends or holidays and from normal time period to peak time period in one day. Secondly, the data field model is used to allocate the CO_2 emissions of taxi trajectory to the individual streets, and the emission from street network exhibited a remarkable distribution in space and time. For instance, streets with high CO_2 emission tend to appear in the morning peak time period of workdays and evening peak time period of weekends or holidays, and they are spatially adjacent to the universities, railway stations, or central business districts. Thirdly, a spatiotemporal autocorrelation technique is adopted to examine the concentration of taxi trajectory emissions in space, and different regions are positively auto-correlated with each other in both times of the day and days of the week. These results can help to the proposal of efficient CO_2 emissions reduction strategies, and they can provide guidance for taxis management, low-carbon traveling, and so on.
The intensive usage of vehicles has led to many urban problems including the traffic jams and the environmental pollutions. To handle these problems, most previous studies have used macro-based models to estimate and analyze the CO_2 emission inventories, but very few studies have focused on computing vehicle CO_2 emissions using a micro-based model. This paper presents an in-depth study on computing the CO_2 emissions from taxi trajectory data and further analyzing their spatiotemporal patterns from three aspects.Taking the Wuhan city as a case study, this paper uses the comprehensive modal emission model to quantitatively compute the CO_2 emissions from the taxi trajectory, and statistical results suggest that CO_2 emissions of the entire city has experienced a remarkable regularity in times of the day and days of the week. Specifically, the spatiotemporal patterns of CO_2 emissions from taxi trajectories are analyzed from three different aspects. Firstly, a spatial clustering algorithm is used to aggregate the taxi trajectory points with high CO_2 emissions, and the number of clusters displayed a regular change pattern. For example, the cluster number is gradually increased from workdays to weekends or holidays and from normal time period to peak time period in one day. Secondly, the data field model is used to allocate the CO_2 emissions of taxi trajectory to the individual streets, and the emission from street network exhibited a remarkable distribution in space and time. For instance, streets with high CO_2 emission tend to appear in the morning peak time period of workdays and evening peak time period of weekends or holidays, and they are spatially adjacent to the universities, railway stations, or central business districts. Thirdly, a spatiotemporal autocorrelation technique is adopted to examine the concentration of taxi trajectory emissions in space, and different regions are positively auto-correlated with each other in both times of the day and days of the week. These results can help to the proposal of efficient CO_2 emissions reduction strategies, and they can provide guidance for taxis management, low-carbon traveling, and so on.
引文
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[2] Zhang Lanyi,Hu Xisheng,Qiu Rongzu. A Review of Vehicle Exhaust Emission Model[J]. World SciTech R&D,2017(4):355-362(张兰怡,胡喜生,邱荣祖.机动车尾气污染物排放模型研究综述[J].世界科技研究与发展,2017(4):355-362)
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[10] Zheng B,Zhang Q,Borken-Kleefeld J,et al. How will Greenhouse Gas Emissions from Motor Vehicles be Constrained in China Around 2030?[J]. Applied Energy,2015,156:230-240
[11] Shan Jie,Qin Kun,Huang Changqing,et al. Methods of Crowd Sourcing Geographic Data Processing and Analysis[J]. Geomatics and Information Science of Wuhan University,2014,39(4):390-396(单杰,秦昆,黄长青,等.众源地理数据处理与分析方法探讨[J].武汉大学学报·信息科学版,2014,39(4):390-396)
[12] Sun F,Zhang X,Tang L,et al. Temporal and Spatial Distribution of High Efficiency Passengers Based on GPS Trajectory Big Data[J]. Journal of GeoInformation Science,2015,5(4):93-101
[13] Mazimpaka J D. Trajectory Data Mining:A Review of Methods and Applications[J]. Journal of Spatial Information Science,2016,13:61-99
[14] Taylor J,Zhou X,Rouphail N M,et al. Method for Investigating Intradriver Heterogeneity Using Vehicle Trajectory Data:A Dynamic Time Warping Approach[J]. Transportation Research Part B:Methodological,2015,73:59-80
[15] Feng Z,Zhu Y. A Survey on Trajectory Data Mining:Techniques and Applications[J]. IEEE Access,2017,4:2 056-2 067
[16] Lei Wei. Research on Simulation and Optimization of Urban Road Traffic Emission[D]. Wuhan:Wuhan University of Technology,2011(雷伟.城市道路交通排放的仿真优化研究[D].武汉:武汉理工大学,2011)
[17] Wu Chaozhong,Xu Chengwei,Yan Xinping. Micro Exhaust Emission Model of Urban Traffic Based on Optimal Velocity Model[J]. Journal of Wuhan University of Technology(Transportation Science&Engineering),2008,32(2):203-205(吴超仲,徐成伟,严新平.基于优化速度模型的城市交通微观尾气排放模型[J].武汉理工大学学报(交通科学与工程版),2008,32(2):203-205)
[18] Chen Kun,Yu Lei. Simulation and Example Analysis of Microscopic Traffic Tail Gas for Traffic Control Strategy Evaluation[J]. Journal of Transportation Systems Engineering and Information Technology,2007,7(1):93-100(陈琨,于雷.用于交通控制策略评估的微观交通尾气模拟与实例分析[J].交通运输系统工程与信息,2007,7(1):93-100)
[19] Wang Z,Chen F,Fujiyama T. Carbon Emission from Urban Passenger Transportation in Beijing[J].Transportation Research Part D:Transport&Environment,2015,41:217-227
[20] Ning Xiaoju,Qin Yaochen,Lu Fengxian. Study on Traffic CO2Emission of Urban Residents in Zhengzhou[D]. Kaifeng:Henan University,2013(宁晓菊,秦耀辰,鲁丰先.郑州城市居民交通CO2排放研究[D].开封:河南大学,2013)
[21] Jia T,Carling K,Hakansson J. Trips and Their CO2Emissions to and from a Shopping Center[J].Journal of Transport Geography,2013,33:135-145
[22] Luo X,Dong L,Dou Y,et al. Analysis on Spatial Temporal Features of Taxis’Emissions from Big Data Informed Travel Patterns:A Case of Shanghai,China[J]. Journal of Cleaner Production,2017,142(2):926-935
[23] Xu Chengwei,Wu Chaozhong,Chu Xiumin,et al.Study on the Average Emission Factor of Light Motor Vehicle in Wuhan Based on CMEM Model[J]. Traffic and Computer,2008,26(4):185-188(徐成伟,吴超仲,初秀民,等.基于CMEM模型的武汉市轻型机动车平均排放因子研究[J].交通与计算机,2008,26(4):185-188)
[24] Zhang Tao,Zhang Xin. The Application of Three Cubic Spline Interpolation in Wavelet Denoising[J].Computer Applications and Software, 2016, 33(8):88-90(张涛,张欣.三次样条插值在小波去噪中的应用[J].计算机应用与软件,2016,33(8):88-90)
[25] Li Q,Jia T. Correction of Measured Taxicab Exhaust Emission Data Based on CMEM Modle[C]. International Conference on Spatial Data Mining,Wuhan,2017
[26] Rong Qiusheng,Yan Junbiao,Guo Guoqiang. Research and Implementation Based on DBSCAN Clustering Algorithm[J]. Computer Application,2004,24(4):45-46(荣秋生,颜君彪,郭国强.基于DBSCAN聚类算法的研究与实现[J].计算机应用,2004,24(4):45-46)
[27] Zhou Qing,Qin Kun,Chen Yixiang,et al. Detection Method of Hot Spot in Taxi Track Based on Data Field[J]. Geography and Geographic Information Science,2016,32(6):51-56(周勍,秦昆,陈一祥,等.基于数据场的出租车轨迹热点区域探测方法[J].地理与地理信息科学,2016,32(6):51-56)
[28] Fu Zhongliang,Yang Yuanwei,Gao Xianjun,et al.Road Network Matching Using Multivariate Regression[J]. Geomatics and Information Science of Wuhan University,2016,41(2):171-177(付仲良,杨元维,高贤君,等.利用多元Logistic回归进行道路网匹配[J].武汉大学学报·信息科学版,2016,41(2):171-177)
[2] Zhang Lanyi,Hu Xisheng,Qiu Rongzu. A Review of Vehicle Exhaust Emission Model[J]. World SciTech R&D,2017(4):355-362(张兰怡,胡喜生,邱荣祖.机动车尾气污染物排放模型研究综述[J].世界科技研究与发展,2017(4):355-362)
[3] EU EEA. COPERT5 Manual[OL]. http://copert.emisia. com,2017
[4] US EPA. Technical Guidance on the Use of MOVES2010 for Emission Inventory Preparation in State Implementation Plans and Transportation Conformity[OL]. https://www. epa. gov/moves,2009
[5] Xie Rongfu,Chen Zhenbin,Deng Xiaokang,et al.Study on Emission Characteristics and Sharing Rate of Vehicle Pollutants in Haikou Based on MOVES2014a[J]. Natural Science Journal of Hainan University,2017,35(3):282-289(谢荣富,陈振斌,邓小康,等.基于MOVES2014a的海口市机动车污染物排放特征及分担率研究[J].海南大学学报·自然科学版,2017,35(3):282-289)
[6] Wang Haikun,Chen Changhong,Huang Cheng,et al. Using IVE Model to Calculate the Emission of Vehicle Pollutants in Shanghai[J]. Journal of Environmental Science,2006,26(1):1-9(王海鲲,陈长虹,黄成,等.应用IVE模型计算上海市机动车污染物排放[J].环境科学学报,2006,26(1):1-9)
[7] Cai H,Xie S. Estimation of Vehicular Emission Inventories in China from 1980 to 2005[J]. Atmospheric Environment,2007,41(39):8 963-8 979
[8] Liu Y,Liao W,Li L,et al. Vehicle Emission Trends in China’s Guangdong Province from 1994to 2014[J]. Science of the Total Environment,2017,586:512-521
[9] Yu Yang,Chen Cai. Analysis of Time and Space Characteristics of China’s Net Carbon Dioxide Emissions[J]. Geographical Sciences, 2013, 33(10):1 173-1 179(于洋,陈才.中国二氧化碳净排放的时空特性分析[J].地理科学,2013,33(10):1 173-1 179)
[10] Zheng B,Zhang Q,Borken-Kleefeld J,et al. How will Greenhouse Gas Emissions from Motor Vehicles be Constrained in China Around 2030?[J]. Applied Energy,2015,156:230-240
[11] Shan Jie,Qin Kun,Huang Changqing,et al. Methods of Crowd Sourcing Geographic Data Processing and Analysis[J]. Geomatics and Information Science of Wuhan University,2014,39(4):390-396(单杰,秦昆,黄长青,等.众源地理数据处理与分析方法探讨[J].武汉大学学报·信息科学版,2014,39(4):390-396)
[12] Sun F,Zhang X,Tang L,et al. Temporal and Spatial Distribution of High Efficiency Passengers Based on GPS Trajectory Big Data[J]. Journal of GeoInformation Science,2015,5(4):93-101
[13] Mazimpaka J D. Trajectory Data Mining:A Review of Methods and Applications[J]. Journal of Spatial Information Science,2016,13:61-99
[14] Taylor J,Zhou X,Rouphail N M,et al. Method for Investigating Intradriver Heterogeneity Using Vehicle Trajectory Data:A Dynamic Time Warping Approach[J]. Transportation Research Part B:Methodological,2015,73:59-80
[15] Feng Z,Zhu Y. A Survey on Trajectory Data Mining:Techniques and Applications[J]. IEEE Access,2017,4:2 056-2 067
[16] Lei Wei. Research on Simulation and Optimization of Urban Road Traffic Emission[D]. Wuhan:Wuhan University of Technology,2011(雷伟.城市道路交通排放的仿真优化研究[D].武汉:武汉理工大学,2011)
[17] Wu Chaozhong,Xu Chengwei,Yan Xinping. Micro Exhaust Emission Model of Urban Traffic Based on Optimal Velocity Model[J]. Journal of Wuhan University of Technology(Transportation Science&Engineering),2008,32(2):203-205(吴超仲,徐成伟,严新平.基于优化速度模型的城市交通微观尾气排放模型[J].武汉理工大学学报(交通科学与工程版),2008,32(2):203-205)
[18] Chen Kun,Yu Lei. Simulation and Example Analysis of Microscopic Traffic Tail Gas for Traffic Control Strategy Evaluation[J]. Journal of Transportation Systems Engineering and Information Technology,2007,7(1):93-100(陈琨,于雷.用于交通控制策略评估的微观交通尾气模拟与实例分析[J].交通运输系统工程与信息,2007,7(1):93-100)
[19] Wang Z,Chen F,Fujiyama T. Carbon Emission from Urban Passenger Transportation in Beijing[J].Transportation Research Part D:Transport&Environment,2015,41:217-227
[20] Ning Xiaoju,Qin Yaochen,Lu Fengxian. Study on Traffic CO2Emission of Urban Residents in Zhengzhou[D]. Kaifeng:Henan University,2013(宁晓菊,秦耀辰,鲁丰先.郑州城市居民交通CO2排放研究[D].开封:河南大学,2013)
[21] Jia T,Carling K,Hakansson J. Trips and Their CO2Emissions to and from a Shopping Center[J].Journal of Transport Geography,2013,33:135-145
[22] Luo X,Dong L,Dou Y,et al. Analysis on Spatial Temporal Features of Taxis’Emissions from Big Data Informed Travel Patterns:A Case of Shanghai,China[J]. Journal of Cleaner Production,2017,142(2):926-935
[23] Xu Chengwei,Wu Chaozhong,Chu Xiumin,et al.Study on the Average Emission Factor of Light Motor Vehicle in Wuhan Based on CMEM Model[J]. Traffic and Computer,2008,26(4):185-188(徐成伟,吴超仲,初秀民,等.基于CMEM模型的武汉市轻型机动车平均排放因子研究[J].交通与计算机,2008,26(4):185-188)
[24] Zhang Tao,Zhang Xin. The Application of Three Cubic Spline Interpolation in Wavelet Denoising[J].Computer Applications and Software, 2016, 33(8):88-90(张涛,张欣.三次样条插值在小波去噪中的应用[J].计算机应用与软件,2016,33(8):88-90)
[25] Li Q,Jia T. Correction of Measured Taxicab Exhaust Emission Data Based on CMEM Modle[C]. International Conference on Spatial Data Mining,Wuhan,2017
[26] Rong Qiusheng,Yan Junbiao,Guo Guoqiang. Research and Implementation Based on DBSCAN Clustering Algorithm[J]. Computer Application,2004,24(4):45-46(荣秋生,颜君彪,郭国强.基于DBSCAN聚类算法的研究与实现[J].计算机应用,2004,24(4):45-46)
[27] Zhou Qing,Qin Kun,Chen Yixiang,et al. Detection Method of Hot Spot in Taxi Track Based on Data Field[J]. Geography and Geographic Information Science,2016,32(6):51-56(周勍,秦昆,陈一祥,等.基于数据场的出租车轨迹热点区域探测方法[J].地理与地理信息科学,2016,32(6):51-56)
[28] Fu Zhongliang,Yang Yuanwei,Gao Xianjun,et al.Road Network Matching Using Multivariate Regression[J]. Geomatics and Information Science of Wuhan University,2016,41(2):171-177(付仲良,杨元维,高贤君,等.利用多元Logistic回归进行道路网匹配[J].武汉大学学报·信息科学版,2016,41(2):171-177)