面向工作者的空间众包动态任务规划算法  

Dynamic Task Schedule Algorithm of Spatial Crowdsourcing for the Worker

作　　者：赵天鸿 涂伟 栾兆亮[1,2] 陈斯达 ZHAO Tianhong;TU Wei;LUAN Zhaoliang;CHEN Sida(College of Civil Engineering. Shenzhen University. Shenzhen 518060 China;Department of Urban Informatics. School of Architecture and Urban Planning. Shenzhen University, Shenzhen 518060. China;Shenzhen Key Laboratory of Spatial Smart Sensing and Services. Shenzhen University, Shenzhen 518060. China;Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, China)

机构地区：[1]深圳大学土木工程学院,广东深圳518060 [2]深圳大学空间信息智能感知与服务深圳市重点实验室,广东深圳518060 [3]深圳大学建筑与城市规划学院城市空间信息工程系,广东深圳518060 [4]深圳大学深圳大学智慧城市研究院,广东深圳518060

出　　处：《测绘地理信息》2019年第2期41-44,共4页Journal of Geomatics

基　　金：国家自然科学基金(41401444);国家留学基金(201708440434)

摘　　要：针对交通物流领域空间众包任务规划问题,提出了带有起始点和终止点的空间众包任务模型;设计了动态优化框架,用时空邻近算法生成初始解,再利用禁忌搜索进一步提高解的质量;通过改变任务的空间分布和相关参数进行大量实验。实验结果表明,时空邻近算法能在不同的数据条件下表现稳定,竞争比达到0.71～0.80,禁忌搜索算法能在时空邻近算法的基础上提升10%左右,竞争比达到0.82～0.93。Aiming at spatial crowdsourcing task scheduling, we designed a spatial crowdsourcing task model with starting and ending points and a dynamic optimization framework. Considering the temporal and spatial characteristics of tasks, the nearest spatial-temporal point based heuristic(NSH) is designed to generate the initial solution. A Tabu search(TS) method is proposed to improve the quality of the initial solution. Using the synthesized data, a large amount of experiments were carried out to evaluate the performance of the proposed method. The experimental results show that the NSH can be stable under different scenarios with a competition ratio of 0.71-0.80. TS can improve about 10% on the basis of NSH, the competition ratio reaches 0.82-0.93.

关 键 词：空间众包 任务规划 禁忌搜索 动态算法 时空邻近 

分 类 号：P208[天文地球—地图制图学与地理信息工程] TP312[天文地球—测绘科学与技术]