基于运行模式的车辆能耗排放估计方法
CSTR:
作者:
作者单位:

同济大学 道路与交通工程教育部重点实验室,加州大学河滨分校,同济大学 道路与交通工程教育部重点实验室,同济大学 道路与交通工程教育部重点实验室

中图分类号:

U121

基金项目:

中央高校基本科研业务费专项资金(1600219251)

  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [8]
  • |
  • 相似文献
  • |
  • 引证文献
  • | |
  • 文章评论
    摘要:

    车辆能耗排放特征与车辆运行模式密切相关。为提高车辆能耗排放估计的准确性,使用稀疏移动传感器数据,提出了一种基于运行模式的车辆能耗排放估计方法。以真实的车辆轨迹数据对模型进行了标定与验证。研究表明,所提出的车辆能耗排放估计方法能够有效地反映真实的车辆能耗排放特征。研究成果有助于拓展稀疏移动传感器数据的应用范围,为估计车辆能耗排放特征提供了一种新的研究思路。

    Abstract:

    Characteristics of vehicle fuel/emissions are related to vehicle driving mode. To improve the accuracy of vehicle fuel/emissions estimation, a modal-activity based vehicle fuel/emissions estimation method is proposed using spare mobile sensor data. The proposed method is calibrated and validated using the real-world vehicle trajectory data. Result reveals that our proposed method shows good performance on vehicle fuel/emissions estimation. The findings of our research can enhance the applicability of spare mobile sensor data and provide a new methodology to estimate vehicle fuel/emissions.

    参考文献
    [1] Comprehensive Modal Emission Model (CMEM) [EB/OL]. http://www.cert.ucr.edu/cmem/.
    [2] MOVES (Motor Vehicle Emission Simulator) [EB/OL]. https://www3.epa.gov/otaq/models/moves/.
    [3] Quiroga C and Bullock D. Travel time studies with global positioning and geographic information systems: an integrated methodology [J]. Transportation Research Part C, 1998, 6(1-2): 101-127.
    [4] Herring R, Hofleitner A, Abbeel P, et al. Estimating arterial traffic conditions using sparse probe data [C]//Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems. 2010: 929-936.
    [5] Wang X, Peng L, Chi T, et al. A hidden Markov model for urban-scale traffic estimation using floating car data [J]. PLos ONE, 2015, 10(12): e0145348.
    [6] Liu H, Chen X, Wang Y, et al. Vehicle Emission and Near-Road Air Quality Modeling for Shanghai, China Based on Global Positioning System Data from Taxis and Revised MOVES Emission Inventory [J]. Transportation Research Record: Journal of the Transportation Board, 2013, NO. 2340: 38-48.
    [7] Hao P, Boriboonsomsin K, Wu G, et al. Probabilistic model for estimating vehicle trajectories using sparse mobile sensor data [C]//Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems. 2014: 1363-1368.
    [8] Hao P,Wu G, Boriboonsomsin K, et al. Modal activity-based vehicle energy/emissions estimation using sparse mobile sensor data [C]//Proceedings of the Transportation Research Board 95th Annual Meeting. 2016, No. 16-6861.
    相似文献
    引证文献
引用本文

单肖年,郝鹏,陈小鸿,叶建红.基于运行模式的车辆能耗排放估计方法[J].同济大学学报(自然科学版),2017,45(09):1319~1327

复制
分享
文章指标
  • 点击次数:1162
  • 下载次数: 1008
  • HTML阅读次数: 579
  • 引用次数: 0
历史
  • 收稿日期:2016-12-07
  • 最后修改日期:2017-07-10
  • 录用日期:2017-05-22
  • 在线发布日期: 2017-09-25
文章二维码