| 基于地理格网的复杂路线车辆通行时间估算方法 |
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| 引用本文： | 武英豪,李成名,吴政,武鹏达.基于地理格网的复杂路线车辆通行时间估算方法[J].测绘通报,2019,0(6):61-65. |
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| 作者姓名： | 武英豪 李成名 吴政 武鹏达 |
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| 作者单位： | 聊城大学,山东 聊城252000;中国测绘科学研究院,北京100036;中国测绘科学研究院,北京,100036 |
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| 基金项目： | 中国测绘科学研究院基本科研业务费（7771804；AR1909） |
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| 摘 要： | 车辆通行时间隐含了特定时隙的交通状况,准确地汁算该时间在交通监测和路径规划中具有重要意义。现有研究通常利用车辆历史轨迹佔算一定距离内选定路径的通行时间,然而当路径距离较长时,限于很难找到完整穿越指定路径的历史轨迹而无法对其通行时间进行准确估计;此外,海量历史轨迹在估计路径通行时间时会产生巨大的数据管理和计算压力因此,本文引入地理格网,首先构建统一的时空索引,将路网及其历史轨迹分别划分为-系列落在地理格网单;(Cell)中的路段模式及轨迹段;然肩利用一系列频繁共享轨迹在Cell中的停留时间计算车辆在当前路段模式的通行时间;最肩通过一组防史时段相似路径模式的通行时间估算较长路线的车辆通行时间通过对北京市10000辆出租车一周的轨迹数据进行试验,验证了本文方法在处理海量历史轨迹数据上的有效性,以及在估算较氏路径上车辆通行时间的优越性。
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| 关 键 词： | 通行时间估计 轨迹数据 时空索引 Cassandra |
| 收稿时间： | 2018-11-05 |
Travel time estimation method of complex route based on geographic grid |
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| WU Yinghao,LI Chengming,WU Zheng,WU Pengda.Travel time estimation method of complex route based on geographic grid[J].Bulletin of Surveying and Mapping,2019,0(6):61-65. |
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| Authors: | WU Yinghao LI Chengming WU Zheng WU Pengda |
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| Institution: | 1. School of Environment and Planning, Liaocheng University, Liaocheng 252000, China;
2. Chinese Academy of Surveying and Mapping, Beijing 100036, China |
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| Abstract: | The travel time of a path implies the traffic condition in period of time. How to calculate this time accurately is of great significance in traffic monitoring and route planning. The existing studies usually use the taxi historical trajectories to estimate the travel time of a path. However, when the path is long, it's difficute to find a historical trajectory pass through the specified path completely, so that we can't estimate the travel time accurately. On the other hand, massive historical trajectories generate huge data management and computational pressure on path travel time estimation. For this reason, we introduce the geographic grid(Cell). First, we construct a unified spatio-temporal index, divide the trajectory data into a series of trajectory segments that fall in the geographic grid (Cell) and split the road network into a series of road patterns that fall in Cell. Then, the travel time of vehicle in current road pattern is represented as the residence time of a series of frequent shared trajectories in Cell. Finaly, we estimate the travel time of vehicles on longer routes using the passage time of a set of similar road patterns in historical periods. At the end of this paper, we verify the effectiveness of our method in dealing with massive historical trajectories and verify the superiority in estimating vehicle travel time on longer paths based on the GPS trajectories of 10 000 taxicabs over a period of one week. |
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| Keywords: | travel time estimation trajectory data spatio-temporal index Cassandra |
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