面向车辆目的地推测的时空搜索优化

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%。

Spatiotemporal Search Optimization for Vehicle Destination Inference

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.