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计算机工程 ›› 2020, Vol. 46 ›› Issue (2): 28-34. doi: 10.19678/j.issn.1000-3428.0053543

• 热点与综述 • 上一篇    下一篇

面向车辆目的地推测的时空搜索优化

韩磊1, 於志勇1,2,3, 朱伟平1, 於志文4   

  1. 1. 福州大学 数学与计算机科学学院, 福州 350116;
    2. 福建省网络计算与智能信息处理重点实验室, 福州 350116;
    3. 空间数据挖掘与信息共享教育部重点实验室, 福州 350002;
    4. 西北工业大学 计算机学院, 西安 710072
  • 收稿日期:2019-01-02 修回日期:2019-03-13 发布日期:2019-03-19
  • 作者简介:韩磊(1992-),男,硕士研究生,主研方向为群智感知;於志勇(通信作者),副教授、博士;朱伟平,博士研究生;於志文,教授、博士。
  • 基金资助:
    国家自然科学基金(61772136)。

Spatiotemporal Search Optimization for Vehicle Destination Inference

HAN Lei1, YU Zhiyong1,2,3, ZHU Weiping1, YU Zhiwen4   

  1. 1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China;
    2. Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou 350116, China;
    3. Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou 350002, China;
    4. School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
  • Received:2019-01-02 Revised:2019-03-13 Published:2019-03-19

摘要: 在仅有车辆起始位置信息的情况下,车辆目的地推测的准确率通常较低。针对该问题,通过在城市道路摄像头的视频录像数据中进行时空搜索,获取目标车辆更多的途经信息,以更准确地推测出其目的地。为在相同的时空搜索次数下最大化目标车辆目的地推测的准确率,设计基于概率的单一指标、基于概率和基尼指数的复合指标以及基于概率和信息增益的复合指标,以评估不同时空搜索对于车辆目的地推测的效用,并基于3种指标分别提出CFMM-MidQuery、CFMM-UtilityQuery-Gini和CFMM-UtilityQuery-Info算法。实验结果表明,时空搜索有助于提高车辆目的地推测的准确率,基于效益的复合指标较基于概率的单一指标评估效果更好,在时空搜索次数相同的条件下,两者目的地推测的准确率相差最高达11.4%。

关键词: 目的地推测, 特征评估, 时空搜索, Markov模型, 搜索优化

Abstract: The inference of vehicle destination can be inaccurate in the case that only the vehicle’s starting position is available.To address this problem,this paper proposes an approach that spatiotemporally searches the video data of city road cameras to obtain more information about the route of the passing vehicle,so as to predict its destination more accurately.In order to maximize the accuracy of target vehicle destination inference under the same spatiotemporal search times,three types of indexes are designed:the probability-based single index,the probability and Gini index-based composite index,and the probability and information gain-based composite index,which are used to evaluate the utility of different spatiotemporal searches for vehicle destination.Further,the CFMM-MidQuery algorithm,the CFMM-UtilityQuery-Gini algorithm and the CFMM-UtilityQuery-Info algorithm are proposed based on the three indexes respectively.Experimental results show that spatiotemporal search can improve the accuracy of vehicle destination inference.The effect of the benefit-based composite indexes is better than that of the probability-based single index.The difference in inference accuracy is as high as 11.4% under the same spatiotemporal search times.

Key words: destination inference, feature evaluation, spatiotemporal search, Markov model, search optimization

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