Computing the CO2 Emissions of Taxi Trajectories and Exploring Their Spatiotemporal Patterns in Wuhan City
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摘要: 车辆尾气是道路交通二氧化碳（CO2）排放的重要来源，目前的研究主要集中在区域CO2排放清单的计算和分析上，鲜有从微观层面上进行CO2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CO2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CO2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CO2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CO2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CO2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CO2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。Abstract: 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 CO2 emission inventories, but very few studies have focused on computing vehicle CO2 emissions using a micro-based model. This paper presents an in-depth study on computing the CO2 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 CO2 emissions from the taxi trajectory, and statistical results suggest that CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 emissions reduction strategies, and they can provide guidance for taxis management, low-carbon traveling, and so on.
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图 3 出租车行程轨迹的CO2单日排放总量
Figure 3. Total Amount of Carbon Dioxide Emissions from Taxis Trajectory per Day
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图 4 周末、工作日、国外的圣瓦伦丁节出租车行程轨迹的CO2小时排放量
Figure 4. Carbon Dioxide Emissions from Weekend, Working Day and the Valentine's Day
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图 5 典型时段出租车行程轨迹点CO2排放量的空间聚类图
Figure 5. Clusters of Carbon Dioxide Emission of Taxi Trajectory Points in Typical Periods
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图 6 典型时段道路线段的出租车CO2排放强度空间分布图
Figure 6. Spatial distribution of Carbon Dioxide Emission from Taxi Trajectory Data of Road Segments in Typical Periods
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图 7 不同日期格网单元出租车CO2排放的局部Moran’s I值
Figure 7. Local Moran's I for the Carbon Dioxide Emission of Spatial Grid at Different Dates
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图 8 不同时间段格网单元出租车CO2排放的局部Moran’s I值
Figure 8. Local Moran's I for the Carbon Dioxide Emission of Spatial Grid at Different Periods
表 1 不同日期CO2排放全局Moran’s I值及检验
Table 1 Overall Moran's I and Its Statistical Test atDifferent Dates
日期 Moran’s I值 Z检验量 P值 2015-02-01 0.642 3 44.142 0.01 2015-02-02 0.602 1 28.565 0.01 2015-02-03 0.610 2 49.124 0.01 2015-02-04 0.614 5 32.120 0.01 2015-02-05 0.512 4 18.858 0.01 2015-02-06 0.541 2 26.356 0.01 2015-02-07 0.589 2 10.586 0.01 2015-02-08 0.489 6 16.258 0.01 2015-02-09 0.358 9 24.223 0.01 2015-02-10 0.512 4 36.255 0.01 2015-02-11 0.510 3 42.536 0.01 2015-02-12 0.448 7 19.255 0.01 2015-02-13 0.589 6 22.363 0.01 2015-02-14 0.711 5 34.144 0.01 2015-02-15 0.682 0 45.356 0.01 
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