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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Vehicle Energy/Emissions Estimation Based on Vehicle Trajectory Reconstruction Using Sparse Mobile Sensor Data
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Vehicle Energy/Emissions Estimation Based on Vehicle Trajectory Reconstruction Using Sparse Mobile Sensor Data

机译：基于稀疏移动传感器数据的车辆轨迹重构的车辆能量/排放量估算

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摘要

Microscopic vehicle emissions models have been well developed in the past decades. Those models require second-by-second vehicle trajectory data as a key input to perform vehicle energy/emissions estimation. Due to the omnipresence of mobile sensors such as floating cars, real-world vehicle trajectory data can be collected in a large scale. However, most large-scaled mobile sensor data in practice are sparse in terms of sampling rate due to the consideration in implementation cost. In this paper, a new modal activity framework for vehicle energy/emissions estimation using sparse mobile sensor data is presented. The valid vehicle dynamic states are identified including four driving modes, named acceleration, deceleration, cruising, and idling. The best valid vehicle dynamic state with the largest probabilities is selected to reconstruct the second-by-second vehicle trajectory between consecutive sampling times. Then vehicle energy/emissions factors are estimated based on operating mode distributions. The proposed model is calibrated and validated using the Next Generation Simulation's dataset, and shows better performance in vehicle energy/emissions estimation compared with the linear interpolation model. Sensitivity analysis is performed to show the model accuracy with different time intervals. This paper provides a new methodology for vehicle energy/emissions estimation and extends the application area of sparse mobile sensor data.
机译：过去几十年来，微观车辆排放模型已经得到了很好的发展。这些模型需要每秒的车辆轨迹数据作为执行车辆能量/排放估算的关键输入。由于浮动传感器等移动传感器无处不在，因此可以大规模收集现实世界的车辆轨迹数据。但是，由于考虑到实现成本，实际上大多数大规模移动传感器数据的采样率都很稀疏。本文提出了一种基于稀疏移动传感器数据的车辆能量/排放估算的新型模态活动框架。确定有效的车辆动态状态，包括四个驾驶模式，分别称为加速，减速，巡航和空转。选择具有最大概率的最佳有效车辆动态状态，以重建连续采样时间之间的第二至第二车辆轨迹。然后，基于操作模式分布来估计车辆能量/排放因子。所提出的模型使用“下一代仿真”的数据集进行了校准和验证，与线性插值模型相比，在车辆能量/排放估算中表现出更好的性能。进行敏感性分析以显示不同时间间隔的模型准确性。本文提供了一种新的车辆能量/排放估算方法，并扩展了稀疏移动传感器数据的应用领域。

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