Journal of Geo-information Science >
Construction of a Micro Model for CO2 Emissions from CNG Taxi Based on Trajectory Data and Deep Learning Method and Evaluation of Carbon Reduction Benefits
Received date: 2023-05-31
Revised date: 2023-09-02
Online published: 2023-11-02
Supported by
National Key Research and Development Program of China(2021YFB3900900)
National Natural Science Foundation of China(42271473)
Natural Science Foundation of Hubei Province of China(2020CFA054)
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 (CO2) emission reduction benefits of CNG taxis, taking Wuhan as an example, a vehicle microscopic CO2 emission model based on deep learning method and trajectory data was proposed to investigate the spatial-temporal characteristics of CO2 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 CO2 emission data in the road test experiment, then we constructed a vehicle microscopic CO2 emission model by the BiLSTM algorithm and further verified its accuracy. Based on the proposed CO2 emission model and the trajectory data of 15 752 Wuhan taxis, the CO2 emissions throughout the entire lifecycle of urban taxis by 92# gasoline and CNG were estimated respectively to quantify the CO2 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 CO2 emission changes, meeting the accuracy for a large-scale estimation of urban taxi CO2 emissions. In addition, the accuracy of taxi CO2 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 CO2 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 CO2 emission as well as the CO2 emission reduction benefits of CNG taxis. The outperformance of deep learning methods over other methods for estimating vehicle CO2 emissions provides new ideas for large-scale and high-precision estimation of vehicle emissions. The CO2 emission reduction benefits of using CNG as fuel in taxis are significant, which provides a reference for the government to formulate relevant energy-saving and CO2 emission reduction policies.
LIU Qi , CHEN Biyu , LI Xinyi . Construction of a Micro Model for CO2 Emissions from CNG Taxi Based on Trajectory Data and Deep Learning Method and Evaluation of Carbon Reduction Benefits[J]. Journal of Geo-information Science, 2023 , 25(11) : 2191 -2203 . DOI: 10.12082/dqxxkx.2023.230300
算法1:基于BiLSTM的车辆CO2排放估算 |
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Input:车辆驾驶特征 ,CO2排放序列 |
Output:CO2排放估算模型M |
begin |
1.计算和提取车辆的驾驶特征序列 ; |
2. 生成训练集和测试集 Dtrain 和Dtest 从数据集 中; |
3. For mini batch d in Dtrain |
4. For T time window data dT in d |
5. for i=1 to T: |
6. Input Xt into forward LSTM to encoder |
7. Using the backword LSTM to encoder |
8. End For |
9. Compute |
10.End For |
11.训练模型M 用反向LSTM算法 |
12. End For |
13.存储车辆CO2排放估算模型为 M |
表1 3种算法的CO2排放估算性能评价结果Tab. 1 Evaluation results of CO2 emission estimation performance |
模型 | 92#汽油 | CNG | |||
---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | ||
SVR | 4.31 | 2.29 | 5.29 | 3.17 | |
LSTM | 2.22 | 1.21 | 2.53 | 1.38 | |
BiLSTM | 1.98 | 1.10 | 2.45 | 1.24 | |
IVE | 3.62 | 1.38 | 6.25 | 4.31 | |
CMEM | 3.24 | 1.29 | 5.95 | 3.74 |
注:加粗数值表示5种模型评价结果中的最优值。 |
表2 武汉市路网道路类型信息Tab. 2 The detailed information of road types in the Wuhan road network. |
道路等级 | 道路类型 | 限速/(km/h) |
---|---|---|
1 | 支路 | 30 |
2 | 次干道 | 40 |
3 | 主干道 | 60 |
4 | 快速路 | 80 |
5 | 高速路 | 120 |
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