杨威, 龙华, 王美, 杜庆治. 基于时间距离的时空重排扫描优化方法[J]. 云南大学学报(自然科学版), 2020, 42(4): 638-647. doi: 10.7540/j.ynu.20190532
引用本文: 杨威, 龙华, 王美, 杜庆治. 基于时间距离的时空重排扫描优化方法[J]. 云南大学学报(自然科学版), 2020, 42(4): 638-647. doi: 10.7540/j.ynu.20190532
YANG Wei, LONG Hua, WANG Mei, DU Qing-zhi. Space–time permutation scanning optimization method based on multi-factor distance[J]. Journal of Yunnan University: Natural Sciences Edition, 2020, 42(4): 638-647. DOI: 10.7540/j.ynu.20190532
Citation: YANG Wei, LONG Hua, WANG Mei, DU Qing-zhi. Space–time permutation scanning optimization method based on multi-factor distance[J]. Journal of Yunnan University: Natural Sciences Edition, 2020, 42(4): 638-647. DOI: 10.7540/j.ynu.20190532

基于时间距离的时空重排扫描优化方法

Space–time permutation scanning optimization method based on multi-factor distance

  • 摘要: 针对时空重排扫描方法扫描使用中,通过空间直线距离来测量两观测点间距会忽略城市中的楼房、公园等障碍物等因素,导致两点间测得真实距离的偏差,从而对扫描结果造成影响, 提出了一种基于时间距离的时空重排扫描优化方法. 首先依据城市中障碍物对两点间实际路径的影响得到观测点间的真实路径,然后利用真实路径计算得到两观测点间的交通时间,最后利用交通时间来代替表示两点间的真实距离. 通过旧金山火灾数据集对本文优化方法进行验证,模型性能普遍提升了2%~5%.

     

    Abstract: In the use of the space–time permutation scan method, if we just measure the spatial straight line distance between two observation points, the distance will ignore factors such as buildings, parks, and other obstacles in the city, causing deviations from the actual distance between the two points, which will influence scan results. This paper proposes a space-time permutation scan optimization method based on temporal distance. Firstly, we can obtain the actual path between two observation points based on the influence of obstacles on the actual path in the city. Then we calculate the actual travel time between the two observation points. Finally, the travel time is used to represent the actual distance between the two points. The San Francisco fire dataset will be used to verify the optimization method in this paper. The model performance is generally improved by 2%~5%.

     

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