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首页> 外文期刊>Atmospheric environment >Estimation of carbon dioxide emissions per urban center link unit using data collected by the Advanced Traffic Information System in Daejeon, Korea
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Estimation of carbon dioxide emissions per urban center link unit using data collected by the Advanced Traffic Information System in Daejeon, Korea

机译：使用韩国大田市高级交通信息系统收集的数据估算每个城市中心链接单元的二氧化碳排放量

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CO_2 emissions on roads in urban centers substantially affect global warming. It is important to quantify CO_2 emissions in terms of the link unit in order to reduce these emissions on the roads. Therefore, in this study, we utilized real-time traffic data and attempted to develop a methodology for estimating CO_2 emissions per link unit. Because of the recent development of the vehicle-to-infrastructure (V21) communication technology, data from probe vehicles (PVs) can be collected and speed per link unit can be calculated. Among the existing emission calculation methodologies, mesoscale modeling, which is a representative modeling measurement technique, requires speed and traffic data per link unit. As it is not feasible to install fixed detectors at every link for traffic data collection, in this study, we developed a model for traffic volume estimation by utilizing the number of PVs that can be additionally collected when the PV data are collected. Multiple linear regression and an artificial neural network (ANN) were used for estimating the traffic volume. The independent variables and input data for each model are the number of PVs, travel time index (TTI), the number of lanes, and time slots. The result from the traffic volume estimate model shows that the mean absolute percentage error (MAPE) of the ANN is 18.67%, thus proving that it is more effective. The ANN-based traffic volume estimation served as the basis for the calculation of emissions per link unit. The daily average emissions for Daejeon, where this study was based, were 2210.19 ton/day. By vehicle type, passenger cars accounted for 71.28% of the total emissions. By road, Gyeryongro emitted 125.48 ton/day, accounting for 5.68% of the total emission, the highest percentage of all roads. In terms of emissions per kilometer, Hanbatdaero had the highest emission volume, with 7.26 ton/day/km on average. This study proves that real-time traffic data allow an emissions estimate in terms of the link unit. Furthermore, an analysis of CO_2 emissions can support traffic management to make decisions related to the reduction of carbon emissions.
机译：城市中心道路上的CO_2排放会严重影响全球变暖。为了减少道路上的这些排放量，重要的是根据连接单元量化CO_2排放量。因此，在这项研究中，我们利用了实时交通数据，并尝试开发一种估算每个链接单元的CO_2排放量的方法。由于车辆对基础设施（V21）通信技术的最新发展，可以收集来自探测车辆（PV）的数据并可以计算每个链接单元的速度。在现有的排放计算方法中，中尺度建模是一种代表性的建模测量技术，它需要每个链接单元的速度和交通数据。由于在每个链路上安装固定检测器以收集交通数据是不可行的，因此在本研究中，我们利用收集PV数据时可以额外收集的PV数量，开发了一种交通量估算模型。使用多元线性回归和人工神经网络（ANN）来估算交通量。每个模型的自变量和输入数据是PV数，行驶时间指数（TTI），车道数和时隙。交通量估计模型的结果表明，人工神经网络的平均绝对百分比误差（MAPE）为18.67％，从而证明了它的有效性。基于ANN的交通量估算是计算每个链接单元排放量的基础。这项研究的基础是，大田的日平均排放量为2210.19吨/天。按车型划分，乘用车占排放总量的71.28％。就公路而言，Gyeryongro每天排放125.48吨，占总排放量的5.68％，在所有道路中所占比例最高。就每公里排放量而言，汉巴达罗排放量最高，平均为7.26吨/日/公里。这项研究证明，实时交通数据允许以链接单位为单位的排放估算。此外，对CO_2排放量的分析可以支持交通管理，以做出与减少碳排放量有关的决策。

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